Minimization of Internally Reflected Power via Waveform Design in Cognitive MIMO Radar

Ahmed Abouelfadl, Ioannis Psaromiligkos, and Benoit Champagne

*IEEE Transactions on Aerospace and Electronic Systems*,
Aug 2023
.

State-of-the-art cognitive MIMO radars maximize the signal-to-interference-plus-noise ratio (SINR) for an extended target of interest by matching the transmitted waveforms to the target impulse response (TIR). Existing methods to match the transmitted waveforms do not consider the problem of internally-reflected power due to the mutual coupling between the transmitting antenna array elements, which results in transmitter inefficiency and possible hardware damage. While the mutual coupling problem in MIMO radars has been handled using microwave techniques heretofore, we herein advocate a signal-processing approach to this problem in cognitive MIMO radars. Specifically, we propose an effective waveform design formalism allowing to jointly maximize the SINR and minimize the reflected power from the transmitting antennas under a TIR matching constraint, while achieving waveform orthogonality in the Doppler domain. Minimizing the reflected power is achieved through the incorporation of a regularization term, taking the form of an l-infinity-norm, in the objective function of a minimum variance distortionless response criterion. An efficient proximal gradient method is developed to solve the resulting non-smooth optimization problem. Simulations with different TIR distributions and transmitting antenna array sizes show that the proposed waveform design algorithm results in lower active reflection coefficients for the antenna elements than selected benchmarks. Furthermore, our algorithm offers a competitive SINR performance compared to these benchmarks and can cope with the fast-varying TIR.

Channel Estimation for Reconfigurable Intelligent Surface-Assisted Full-Duplex MIMO with Hardware Impairments

Alexander J. Fernandes and Ioannis Psaromiligkos

*IEEE Wireless Communications Letters*,
vol. 12,
no. 10,
pp. 1697-1701,
Oct 2023
.

We consider the problem of channel estimation in a multiple-input-multiple-output (MIMO) full-duplex (FD) wireless communication system assisted by a reconfigurable intelligent surface (RIS) with hardware impairments (HI) occurring at the transceivers and RIS elements. We propose an unbiased channel estimator that requires knowledge of only the first and second order statistics of the HI, for which we derive closed form expressions. The proposed estimator reduces to the maximum likelihood estimator (MLE) in the case of ideal hardware. We also describe FD and HD orthogonal pilot schemes that minimize the mean square error of the MLE in the case of ideal hardware. We verify the performance of the estimator under varying conditions of transceiver and RIS HI via numerical simulations.

Energy-Efficient Resource Allocation for D2D-Assisted Fog Computing

Onur Karatalay, Ioannis Psaromiligkos, and Benoit Champagne

*IEEE Transactions on Green Communications and Networking*,
vol. 6,
no. 4,
pp. 1990-2002,
Dec 2022
.

In this paper, we address the problem of energy-efficient resource allocation in a multi-device D2D-assisted fog computing scenario, where the goal is to minimize the total energy consumption subject to constraints on the transmit powers, computation resources and task processing times. The considered problem is non-convex and finding its global optimum is generally intractable; hence we propose two sub-optimal approaches to solve it. First, by investigating the relationship between the task processing time and the total energy consumption, we show how the original problem can be relaxed into a sequence of convex subproblems whose solutions can be efficiently obtained via standard algorithms. Second, to further reduce computational complexity, we propose a low-complexity heuristic resource allocation strategy which does not require calculating gradients and the Hessian matrices in the solution process. We also develop a lower bound on the total energy consumption for the considered task offloading scenario as a benchmark for comparison purpose. Computer simulations under a wide range of conditions and parameter settings show that both methods achieve a near-optimal solution in comparison to the lower bound.

Segmentation of Atherosclerotic Plaque Features from Histopathology Images using Novel Deep Learning Techniques

M. Mohebpour, K. Gasbarrino, K. Khan, H. Zheng, N. Babazadeh Khameneh, I. Psaromiligkos, and S. Daskalopoulou

*Arteriosclerosis, Thrombosis, and Vascular Biology*,
vol. 42, Suppl. Issue: Abstracts From the American Heart Association's Vascular Discovery: From Genes to Medicine 2022 Scientific Sessions, Seattle, USA, May 2022,
Nov 2022
.

Energy-Efficient D2D-Aided Fog Computing under Probabilistic Time Constraints

Onur Karatalay, Ioannis Psaromiligkos, and Benoit Champagne

* in Proceedings of *
*2021 IEEE Global Communications Conference (GLOBECOM)*,
Aug 2021
.

Device-to-device (D2D) communication is an enabling technology for fog computing by allowing the sharing of computation resources between mobile devices. However, temperature variations in the device CPUs affect the computation resources available for task offloading, which unpredictably alters the processing time and energy consumption. In this paper, we address the problem of resource allocation with respect to task partitioning, computation resources and transmit power in a D2D-aided fog computing scenario, aiming to minimize the expected total energy consumption under probabilistic constraints on the processing time. Since the formulated problem is non-convex, we propose two sub-optimal solution methods. The first method is based on difference of convex (DC) programming, which we combine with chance-constraint programming to handle the probabilistic time limitations. Considering that DC programming is dependent on a good initial point, we propose a second method that relies on only convex programming, which eliminates the dependence on user-defined initialization. Simulation results demonstrate that the latter method outperforms the former in terms of energy efficiency and run-time.

A Distributed Pulse-Based Synchronization Protocol for Half-Duplex D2D Communications

Onur Karatalay, Ioannis Psaromiligkos, Benoit Champagne, and Benoit Pelletier

*IEEE Open Journal of the Communications Society*,
vol. 2,
pp. 245-261,
Jan 2021
.

In distributed device-to-device (D2D) communications, no common reference time is available and the devices must employ distributed synchronization techniques. In this context, pulse-based synchronization, which can be implemented by distributed phase-locked loops is preferred due to its scalability. Several factors degrade the performance of pulse-based synchronization, such as duplexing scheme, clock skew and propagation delays. Furthermore, in distributed networks, devices should be aware of the synchronization status of others in order to initiate data communications. To address these prevailing issues, we first introduce a half-duplex timing-advance synchronization algorithm wherein each device alternates between being a transmitter and receiver in their exchange of synchronization pulses at each clock period. Based on this algorithm, we propose a novel fully-distributed pulse-based synchronization protocol for half-duplex D2D communications in 5G wireless networks. The protocol allows participating devices to become aware of the global synchronization status, so that they can complete the synchronization process ideally at the same time and proceed to data communication. In simulation experiments over multi-path frequency selective channels, the proposed synchronization protocol is shown to outperform a benchmark approach from the recent literature over a wide range of conditions, e.g., clock skew, number of devices, and network topology.

Energy-Efficient Group-Sparse Transceiver Design for Multiuser MIMO Relaying in C-RAN

Jiaxin Yang, Ayoub Saab, Alireza Morsali, Benoit Champagne, and Ioannis Psaromiligkos

*IEEE Transactions on Green Communications and Networking*,
vol. 4,
no. 3,
pp. 703-716,
Sep 2020
.

This paper addresses the problem of centralized transceiver design for multiuser MIMO amplify-and-forward (AF) relaying within a cloud radio access network (C-RAN). The aim is to optimize AF matrices of remote radio heads (RRHs) acting as relays, in order to improve the reception quality at the destinations while reducing network power consumption and feedback overhead on the fronthaul links. A two-stage method is proposed to solve this problem efficiently. The first stage relies on interference leakage minimization subject to per-relay transmit power constraints along with signal preserving constraints. To reduce the total network power, RRH selection is achieved by incorporating in the objective function a regularization term that promotes group-sparsity among the RRHs. In the second stage, to reduce feedback overhead, a different penalty term is added that induces weight-level sparsity in the AF matrix of each active RRH. For both stages, low-complexity iterative algorithms based on the alternating direction method of multipliers (ADMM) are developed to solve the corresponding regularized problems with low complexity. Extensive simulations are performed to demonstrate the explicit benefits of the proposed design method, which results in notably lower power consumption, computational complexity and weight feedback overhead than conventional approaches.

Lumen & Media Segmentation of IVUS Images via ellipse fitting using a Wavelet-Decomposed Subband CNN

P. Sinha, Y. Wu, I. Psaromiligkos, and Z. Zilic

* in Proceedings of *
*2020 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)*,
Jun 2020
.

We propose an automatic segmentation method for both lumen and media in IntraVascular UltraSound (IVUS) images using a deep convolutional neural network (CNN). In contrast to previous approaches that broadly fall under the category of labeling each pixel to be either lumen, media or background, we propose to use a structurally regularized CNN via wavelet-based subband decomposition that directly predicts two ellipses that best represent each of lumen and media segments. The proposed architecture significantly reduces computational complexity and offers better performance compared to recent techniques in the literature. We evaluated our network on the publicly available IVUS-Challenge-2011 dataset using two performance metrics, namely Jaccard Measure (JM) and Hausdorff Distance (HD). The evaluation results show that our proposed network outperforms the state-of-the-art lumen and media segmentation methods by a maximum of 8% in JM (Lumen) and nearly 33% in HD (Media).

Covariance-Free Nonhomogeneity STAP Detector in Compound Gaussian Clutter Based on Robust Statistic

A. Abouelfadl, I. Psaromiligkos, and B. Champagne

*IET Radar, Sonar and Navigation*,
vol. 13,
no. 12,
pp. 2107 – 2119,
Dec 2019
.

Space time adaptive processing (STAP) detects targets by computing adaptive weight vectors for each cell under test using its covariance matrix, as estimated from surrounding secondary cells. In this context, the non-homogeneity detector (NHD) excludes the anomalous secondary cells that adversely affect the detection performance. The existing robust NHDs require estimating the covariance matrix of each secondary cell, which hinders their implementation in modern radars with large-dimensional range cells. In this paper, we propose a new low-complexity NHD that is suitable for highly correlated clutter environments with both Gaussian and non-Gaussian heavy-tailed distributions. The proposed detector, which is based on the projection depth function from the field of robust statistics, features a nonparametric and covariance-free test statistic. As a result, its computational complexity is much lower than that of current NHDs, such as the widely used normalized adaptive matched filter (NAMF) detector, especially for large-dimensional range cells. In Monte Carlo simulations with different clutter distributions and radar system configurations, the proposed detector shows a comparable performance to that of NAMF. The low complexity and robust performance of the new detector make it particularly attractive for real time applications.

A structurally regularized convolutional neural network for image classification using wavelet-based subband decomposition

P. Sinha, I. Psaromiligkos, and Z. Zilic

* in Proceedings of *
*2019 IEEE International Conference on Image Processing (ICIP)*,
pp. 649-653,
Sep 2019
.

We propose a convolutional neural network (CNN) architecture for image classification based on subband decomposition of the image using wavelets. The proposed architecture decomposes the input image spectra into multiple critically sampled subbands, extracts features using a single CNN per subband, and finally, performs classification by combining the extracted features using a fully connected layer. Processing each of the subbands by an individual CNN, thereby limiting the learning scope of each CNN to a single subband, imposes a form of structural regularization. This provides better generalization capability as seen by the presented results. The proposed architecture achieves best-in-class performance in terms of total multiply-add-accumulator operations and nearly best-in-class performance in terms of total parameters required, yet it maintains competitive classification performance. We also show that the proposed architecture is more robust than the regular full-band CNN to noise caused by weight-and-bias quantization and input quantization.

Fully Distributed Energy-Efficient Synchronization for Half-duplex D2D Communications

O. Karatalay, I. Psaromiligkos, B. Champagne, and B. Pelletier

* in Proceedings of *
*IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2019)*,
Sep 2019
.

Synchronization is a challenging problem especially for distributed systems, such as out-of-coverage D2D networks, as no common reference time is available. In such cases, devices use distributed synchronization algorithms, however, accurately determining when to stop the synchronization process is as challenging as achieving synchronization since they do not have the synchronization status of other devices in the network. From energy efficiency and performance perspective, the synchronization process should be stopped at all devices at the same time. In addition, to counteract the effect of propagation delays during synchronization, timing-advance (TA) clocks should be employed. This could be achieved in the synchronization process, however, after the devices are synchronized, there is no central mechanism to instruct them on TA clocks for transmitting or receiving data packets. In this paper, we propose a synchronization algorithm which, in an energy-efficient manner, allows devices to (i) acquire the synchronization status of others and terminate the synchronization process as soon as all devices in the network are synchronized, (ii) allow the synchronized devices to properly advance/regress their clocks prior to data communication by tracking their relative timing. We numerically demonstrate that the maximum synchronization error over multipath channels is around 0.6us and it can be maintained during data communication.

Extended Target Frequency Response Estimation Using Infinite HMM in Cognitive Radars

A. Abouelfadl, I. Psaromiligkos, and B. Champagne

* in Proceedings of *
*2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)*,
Aug 2019
.

A cognitive radar adapts its waveform to match the extended target's frequency response (TFR) for optimized detection performance. In practice, the TFR is unknown and is usually estimated using the Kalman filter assuming a linear Gaussian model. However, this assumption is not always fulfilled and other filters as the particle filter should be used. In all cases, existing approaches require the complete knowledge of the statistical distributions of both the TFR and interference. In this paper, we present a novel formulation of the TFR estimation problem that allows us to use the infinite hidden Markov model (iHMM) to estimate and track the TFR without such prior knowledge. Monte Carlo simulations considering Gaussian and non-Gaussian distributions for TFR and interference as well as jamming effects show that the proposed iHMM-based method ameliorates the estimation accuracy compared to the conventional Bayesian filtering techniques.

Fast Converging Distributed Pulse-coupled Clock Synchronization for Half-duplex D2D Communications over Multipath Channels

O. Karatalay, I. Psaromiligkos, B. Champagne, and B. Pelletier

* in Proceedings of *
*2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)*,
Dec 2018
.

Clock synchronization is a fundamental problem in distributed device-to-device (D2D) communications as envisioned in fifth generation (5G) wireless networks. To achieve synchronization, especially in out-of-coverage scenarios where no common reference point is available, devices can use distributed phase-locked loops (DPLLs). Although DPLLs provide accurate synchronization in ideal conditions, i.e., additive noise channel without propagation delays, dispersive multipath channels may significantly degrade the synchronization performance. The choice of duplexing mode (full-duplex or half-duplex) also affects greatly the performance of a distributed synchronization scheme. Since full-duplexing is not yet a practical technology at the device side, in this work we consider distributed synchronization under the realistic assumption of half-duplex communication over multipath channels. We first present a new synchronization algorithm that allows devices to self-determine their transceiver mode, and then, we introduce a modified DPLL algorithm based on iterative propagation delay estimation to improve synchronization performance. Numerical results show that when using the proposed algorithms, devices can achieve a steady-state timing error on the order of 1 microsec while allowing them to arbitrarily join or leave the synchronization process.

Efficient Group-Sparse Transceiver Design for Multiuser MIMO Relaying in C-RAN

A. Saab, J. Yang, B. Champagne, and I. Psaromiligkos

* in Proceedings of *
*2018-Fall IEEE Vehicular Technology Conference*,
Aug 2018
.

This paper addresses the design of multiuser MIMO amplify-and-forward relaying within a cloud radio access network (C-RAN) from an energy-efficient perspective. The aim is to jointly select remote radio heads and optimize their transceiver in order to assist the communication between multiple source-destination pairs. We formulate the design problem as an interference leakage minimization subject to per-relay power constraints along with linear signal preserving constraints at the destinations. To obtain an energy efficient relaying solution, the objective function is penalized with a regularization term which promotes the group-sparsity among the resultant relaying weights. A low-complexity iterative algorithm based on the alternating direction method of multipliers (ADMM) is then proposed to solve the regularized problem. Simulation results demonstrate the explicit benefits of the proposed algorithm, which results in significantly lower power consumption and computational complexity than conventional relaying design methods.

A Low-Complexity Nonparametric STAP Detector

A. Abdouelfadl, I. Psaromiligkos, and B. Champagne

* in Proceedings of *
*2018 IEEE National Aerospace & Electronics Conference (NAECON)*,
Jul 2018
.

Phased array radars use space time adaptive processing (STAP) to detect targets in angle, range, and speed using an adaptive weight vector that depends mainly on the covariance matrix of the cell under test (CUT). This covariance matrix is estimated from the secondary cells surrounding the CUT under the assumption of homogeneous clutter and noise background. However, these secondary cells are often contaminated by multiple discrete interferers, targets or combination thereof, which degrade the estimation of the CUT's covariance matrix and, in turn, the detection performance. In this paper, we address the problem of detecting the nonhomogeneous secondary cells that need to be excluded from the adaptive weight calculation. We introduce a nonparametric and covariance-free alternative to the normalized adaptive matched filter (NAMF) test that does not need the tedious estimation process of the covariance matrix matrix of secondary cells nor prior knowledge about the interference distribution. Consequently, the computational complexity of the weight vector is reduced, which is of a great importance for real-time operation of radar systems. The equivalent robust performance of the proposed test compared to the NAMF test is demonstrated through simulations under different clutter scenarios and operation conditions.

A Rational Distributed Process-level Account of Independence Judgment

A. S. Nobandegani, and I. N. Psaromiligkos

* in Proceedings of *
*40th Annual Meeting of the Cognitive Science Society (CogSci18)*,
Jul 2018
.

It is inconceivable how chaotic the world would look to humans, faced with innumerable decisions a day to be made under uncertainty, had they been lacking the capacity to distinguish the relevant from the irrelevant — a capacity which computationally amounts to handling probabilistic independence relations. The highly parallel and distributed computational machinery of the brain suggests that a satisfying process-level account of human independence judgment should also mimic these features. In this work, we present the first rational, distributed, message-passing process-level account of independence judgment, called D*. Interestingly, D* shows a curious, but normatively justified tendency for quick detection of dependencies, whenever they hold. Furthermore, D* outperforms all the previously proposed algorithms in the AI literature in terms of worst-case running time, and a salient aspect of it is supported by recent work in neuroscience investigating possible implementations of Bayes nets at the neural level. D* nicely exemplifies how the pursuit of cognitive plausibility can lead to the discovery of state-of-the-art algorithms with appealing properties, and its simplicity makes D* potentially a good candidate as a teaching tool.

A Theory of Generalized Proximity for ADMM

F. Cote, I. N. Psaromiligkos, and W. Gross

* in Proceedings of *
*2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP) - Symposium on Distributed Optimization and Resource Management over Networks*,
Nov 2017
.

The alternating direction method of multipliers has become in recent years the most widely used proximal method for signal processing. In this paper, we lay the groundwork for a new notion of proximity and use it to illustrate that the method (ADMM) is actually somewhat of a maladroit rearrangement of a new, more practical procedure that generalizes the Douglas-Rachford algorithm. Compared to ADMM, the algorithm that we propose enjoys not only a more sensible form, but also a more general convergence result.

The Causal Frame Problem: An Algorithmic Perspective

A. S. Nobandegani, and I. N. Psaromiligkos

* in Proceedings of *
*39th Annual Meeting of the Cognitive Science Society (CogSci17)*,
Jul 2017
.

On the Use of Distributed Synchronization in 5G Device to Device Networks

David Tétreault-La Roche, B. Champagne, I. N. Psaromiligkos, and B. Pelletier

* in Proceedings of *
*IEEE ICC 2017 Ad-Hoc and Sensor Networking Symposium*,
May 2017
.

Time synchronization is a key aspect of D2D schemes, particularly in decentralized networks where no reference time is available. Distributed phase-locked loops (DPLL) is a synchronization algorithm well suited for decentralized situations. In this work, we study DPLL in the context of 5G networks, where we include in our analysis several practical aspects of D2D communication, such as propagation delays, multipath propagation, and the use of SC-FDMA. We propose practical methods to compensate for their effects, and introduce new performance metrics to evaluate the merits of the synchronization algorithm. Through simulations at the physical layer, which capture the effects of analog-digital conversions, we demonstrate that time synchronization in a decentralized setting is possible under the constraints specified by the 3GPP for D2D applications.

Relevance Effect: Exploiting Bayesian Networks to Improve Supervised Learning

A. Salehi Nobdandegani, J. Kabbara, and I. N. Psaromiligkos

* in Proceedings of *
*2017 International Joint Conference on Neural Networks*,
May 2017
.

Deductive logic and its variants enjoy the common property of monotonicity. For tasks such as inductive reasoning and belief revision, this was eventually deemed a serious flaw, prompting attempts to construct non-monotonic versions of logic. With the introduction of the idea of probabilistic reasoning to AI, particularly with the advent of Bayesian networks (BNs), the aforementioned monotonicity was no longer an issue: probability is inherently non-monotonic. In this work, we introduce the notion of relevance effect which bears on exploiting BNs to generate realizations of relevant variables to be used for potentially improving the performance of a learning model on a supervised classification task. We explore the potential of using the relevance effect in the context of Deep Belief Networks (DBNs) with a focus on relational domains. We show that although the idea is at odds with the non-monotonicity of probabilistic reasoning, we attain an improvement in learning performance in different simulations on both synthetic and real-world scenarios. The observation that adopting this notion has improved the performance of a powerful model like DBNs hints to its potential to be practiced so as to enhance the performance of supervised learning methods in general. We furthermore highlight the connections as well as the implications of our work to the psychology literature.

A distributed constrained-form support vector machine

F. Cote, I. N. Psaromiligkos, and W. Gross

* in Proceedings of *
*2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*,
Mar 2017
.

Despite the importance of distributed learning, few fully distributed support vector machines exist. In this paper, not only do we provide a fully distributed nonlinear SVM; we propose the first distributed constrained-form SVM. In the fully distributed context, a dataset is distributed among networked agents that cannot divulge their data, let alone centralize the data, and can only communicate with their neighbors in the network. Our strategy is based on two algorithms: the Douglas-Rachford algorithm and the projection-gradient method. We validate our approach by demonstrating through simulations that it can train a classifier that agrees closely with the centralized solution.

System and Method for Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) Offset Quadrature Amplitude Modulation (OQAM)

I. N. Psaromiligkos, I. Tinjaca, and M. Advoli

*US Patent*,
Jan 2017
.

In-network Linear Regression with Arbitrarily Spilt Data Matrices

F. Cote, I. N. Psaromiligkos, and W. Gross

* in Proceedings of *
*2016 IEEE GlobalSIP Symposium on Distributed Information Processing, Optimization, and Resource Management over Networks*,
pp. 580-584,
Dec 2016
.

We address for the first time the question of how networked agents can collaboratively fit a Morozov-regularized linear model when each agent knows a summand of the regression data. This question generalizes previously studied data-splitting scenarios, which require that the data be partitioned among the agents. To answer the question, we introduce a class of network-structured problems, which contains the regularization problem, and by using the Douglas-Rachford splitting algorithm, we develop a distributed algorithm to solve these problems. We illustrate through simulations that our approach is an effective strategy for fully distributed linear regression.

Robust and secure beamformer design for MIMO relaying with imperfect eavesdropper CSI

N. Badra and J. Yang and I. Psaromiligkos and B. Champagne

* in Proceedings of *
*2016 IEEE Conference on Communications and Network Security (CNS)*,
pp. 442-449,
Oct 2016
.

This paper presents a computationally efficient beamforming approach to combat wiretapping in a relay-based multiple-input multiple output (MIMO) communication system which is part of a cognitive radio (CR) network. The system operates in two stages, that is, multiple-access (MA) followed by broadcasting (BC) using physical layer network coding (PNC). The beamforming design is based on minimizing the mean square error (MSE) at the receiving node(s) while enforcing signal-to-interference-plus-noise ratio (SINR) constraints at the eavesdroppers. The constraints take into account uncertainty bounds on eavesdropper channel estimation errors. In each stage of communication, an optimization problem is devised and solved using an iterative procedure, considering two different types of eavesdropper functionality, i.e., selection combining and blind beamforming. Numerical results show the convergence of the MSE at the nodes and the SINR distributions at the eavesdroppers for both cases. Comparisons to previously suggested solutions for blind beamforming are also included showing improvements in MSE values in the MA stage and computational efficiency in both stages.

Carotid atherosclerotic plaque alters the direction of longitudinal motion in the artery wall

J. Tat, I. N. Psaromiligkos, and S. Daskalopoulou

*Ultrasound in Medicine and Biology*,
vol. 42,
no. 9,
pp. 2114–2122,
Sep 2016
.

Longitudinal motion of the artery, a cyclical, bidirectional movement of the wall in the long axis of the artery, has recently gained interest in the characterization of artery function. The aim of this study was to evaluate longitudinal motion in patients with internal carotid atherosclerotic plaques. Speckle tracking ultrasound was used to assess common carotid artery wall motion in 12 patients with carotid plaque causing either moderate (50%–79%) or severe (80%–99%) stenosis based on the North American Carotid Endarterectomy Trial, and 23 healthy participants. Although healthy individuals were found to have a retrograde wall motion pattern, a distinct anterograde pattern was noted with plaque presence. Importantly, patients with severe plaque stenosis had greater anterograde motion (0.53 ± 0.36 mm) than those with moderate stenosis (0.17 ± 0.15 mm) (p < 0.05), likely owing to high wall shear stresses associated with greater peak systolic velocities at the site of stenosis (severe: 342.0 ± 99.4 cm/s, moderate: 177.5 ± 31.2 cm/s, p < 0.01). There were no differences in peak systolic velocities at plaque-free segments between plaque groups (severe: 80.2 ± 24.8 cm/s, moderate: 92.7 ± 23.0 cm/s). Blood flow at stenotic areas better predicted motion than plaque-free segments. We conclude that the presence of carotid plaque can have significant influence on longitudinal motion, with significantly greater anterograde displacements with increased stenosis. Future studies are needed to further investigate carotid artery wall mechanics.

Kernel subspace pursuit for sparse regression

J. Kabbara and I. N. Psaromiligkos

*Pattern Recognition Letters*,
vol. 69,
pp. 56-61,
Jan 2016
.

Recently, results from sparse approximation theory have been considered as a means to improve the generalization performance of kernel-based machine learning algorithms. In this paper, we present Kernel Subspace Pursuit (KSP), a new method for sparse non-linear regression. KSP is a low-complexity method that iteratively approximates target functions in the least-squares sense as a linear combination of a limited number of elements selected from a kernel-based dictionary. Unlike other kernel methods, by virtue of KSP’s algorithmic design, the number of KSP iterations needed to reach the final solution does not depend on the number of basis functions used nor that of elements in the dictionary. We experimentally show that, in many scenarios involving learning synthetic and real data, KSP is less complex computationally and outperforms other kernel methods that solve the same problem, namely, Kernel Matching Pursuit and Kernel Basis Pursuit.

Multi-Context Models for Reasoning under Partial Knowledge: Generative Process and Inference Grammar

A. Salehi Nobandegani and I. N. Psaromiligkos

* in Proceedings of *
*31st Conference on Uncertainty in Artificial Intelligence (UAI 2015)*,
Jul 2015
.

Arriving at the complete probabilistic knowledge of a domain, i.e., learning how all variables interact, is indeed a demanding task. In reality, settings often arise for which an individual merely possesses partial knowledge of the domain, and yet, is expected to give adequate answers to a variety of posed queries. That is, although precise answers to some queries, in principle, cannot be achieved, a range of plausible answers is attainable for each query given the available partial knowledge. In this paper, we propose the Multi-Context Model (MCM), a new graphical model to represent the state of partial knowledge as to a domain. MCM is a middle ground between Probabilistic Logic, Bayesian Logic, and Probabilistic Graphical Models. For this model we discuss: (i) the dynamics of constructing a contradiction-free MCM, i.e., to form partial beliefs regarding a domain in a gradual and probabilistically consistent way, and (ii) how to perform inference, i.e., to evaluate a probability of interest involving some variables of the domain.

Exact Cramer Rao Bounds for Semiblind Channel Estimation in Amplify-and-Forward Two-Way Relay Networks Employing Square QAM

S. Abdallah and I. N. Psaromiligkos

*IEEE Transactions on Wireless Communications*,
pp. 6955-6967,
Dec 2014
.

In this paper, we derive the Cramer-Rao bound (CRB) for semiblind channel estimation in amplify-and-forward two-way relay networks employing square QAM, assuming flat-fading channel conditions. The derived bound is exact as it is based on the true likelihood function that takes into account the statistics of the transmitted data symbols. Using the new bound, we show that exploiting even a limited number of transmitted data symbols in addition to the pilot symbols leads to substantial estimation accuracy improvements over conventional pilot-based estimation. We also propose a semiblind expectation-maximization-based estimation algorithm that performs very close to the exact CRB at an affordable computational cost. The superior accuracy of the semiblind approach makes it possible to significantly reduce the training overhead for channel estimation, thus offering a higher throughput and a better tradeoff between accuracy and spectral efficiency. We also derive the modified CRB, which approximates the exact CRB at high SNR for low modulation orders.

A ML-Based Framework for Joint TOA/AOA Estimation of UWB Pulses in Dense Multipath Environments

F. Shang and B. Champagne and I. N. Psaromiligkos

*IEEE Transactions on Wireless Communications*,
vol. 13,
no. 10,
pp. 5305-5318,
Oct 2014
.

We present a joint estimator of the time of arrival (TOA) and angle of arrival (AOA) for impulse radio ultrawideband (UWB) systems in which an antenna array is employed at the receiver. The proposed method consists of two steps: 1) preliminary estimation of the TOA and the average power delay profile (APDP) using energy-based threshold crossing and log-domain least-squares fitting, respectively; and 2) joint TOA refinement and AOA estimation by local 2-D maximization of a log-likelihood function (LLF) that employs the preliminary estimates from the first step. The derivation of the LLF relies on an original formulation in which the superposition of images from secondary paths is modeled as a Gaussian random process, whose second-order statistical properties are characterized by a wideband space-time correlation function. In addition to the APDP, this function incorporates a special gating mechanism to represent the onset of the secondary paths, thereby leading to a novel form of the LLF. Closed-form expressions for the Cramer-Rao bound on the variance of the TOA and AOA estimators are also derived, which formally take into account pulse overlap through this gating mechanism. In simulation experiments based on multipath UWB channel models featuring both diffuse and directional image fields, our approach exhibits superior performance to that of a competing scheme from the recent literature.

On the Chandra–Poram–Bose symbol error probability expression for coherent orthogonal M-ary frequency shift keying

F. Cote, I. N. Psaromiligkos, and W. J. Gross

*International Journal of Communication Systems*,
vol. 27,
no. 10,
pp. 2092-2096,
Oct 2014
.

An expression for the average symbol error probability of coherent orthogonal M-ary frequency shift keying in generalized fading was recently reported by Chandra, Poram, and Bose. We show that the expression is only exact for M = 2; it does, however, provide an accurate approximation for M > 2. By modifying the derivation of the reported expression, we derive a lower bound for M ⩽5 that has the same complexity as the reported expression, and we illustrate that, for M > 2, the expression of the derived bound provides an approximation that is also more accurate.

Joint TOA/AOA estimation of IR-UWB signals in the presence of multiuser interference

F. Shang and B. Champagne and I. Psaromiligkos

* in Proceedings of *
*2014 IEEE 15th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)*,
pp. 504-508,
Jun 2014
.

We present a joint estimator of the time-of-arrival (TOA) and angle-of-arrival (AOA) for impulse radio ultra-wideband (IR-UWB) localization systems in which an antenna array is employed at the receiver and multiuser (MUI) interference exists. The proposed method includes 3 steps: (1) time-alignment and averaging to reduce the power level of the MUI and background noise; (2) preliminary TOA estimation based on energy detection followed by quadratic averaging; (3) joint TOA and AOA estimation using a recently proposed log likelihood function, but further extended to consider the effect of MUI. The validity of the proposed method is demonstrated by numerical simulations over a realistic space-time channel model.

Denoising using multi-stage randomized orthogonal matching pursuit

S. Koskinas and I. Psaromiligkos

* in Proceedings of *
*2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*,
pp. 4983-4987,
May 2014
.

Orthogonal Matching Pursuit (OMP) can denoise a signal by greedily approximating a least-squares (LS) estimate as a linear combination of elements (atoms) of a dictionary. OMP iteratively decomposes a signal through deterministic atom selections at each iteration step. Recently proposed randomized OMP algorithms employ random atom selections instead and have the potential to further improve denoising. Typically, the best approximation from these algorithms can be obtained only within a narrow range of iterations. In this paper, we propose a novel multi-stage randomized OMP (MS-ROMP) denoising approach that performs successive ROMP runs, each denoising the obtained estimate from the previous one. We show through simulations that, under certain conditions, this can significantly improve denoising performance by producing a good approximation after any number of iterations beyond the sparsity level.

Improving the tracking ability of KRLS using Kernel Subspace Pursuit

J. Kabbara and I. N. Psaromiligkos

* in Proceedings of *
*2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*,
pp. 4543-4547,
May 2014
.

We present a new Kernel Recursive Least Squares (KRLS) algorithm that is able to efficiently track time-varying systems. In order to alleviate the detrimental effect of a large dictionary size on the algorithm's tracking ability, we decouple the equality between dictionary size and weight vector size, an equality that has been encountered in all previous KRLS algorithms. In the proposed method, the maximum size of the weight vector is fixed and is independent from the dictionary size. We introduce the Kernel Subspace Pursuit algorithm which we use to choose a subset of the dictionary that tracks best the most recent received data samples. The selected dictionary elements are then used in the KRLS iterations. We show through simulations that our algorithm outperforms existing KRLS algorithms in tracking time-varying systems.

Semi-Blind Channel Estimation with Superimposed Training for OFDM-Based AF Two-Way Relaying

S. Abdallah and I. N. Psaromiligkos

*IEEE Transactions on Wireless Communications*,
vol. 13,
no. 5,
pp. 2468-2467,
May 2014
.

We consider the problem of channel estimation for OFDM-based amplify-and-forward (AF) two-way relay networks (TWRNs). While previous works have adopted a pilot-based approach, we propose a semi-blind approach that exploits both the transmitted pilots as well as the received data samples to improve the estimation performance. Our proposed semi-blind estimator is based on the Gaussian maximum likelihood (GML) criterion which treats that data symbols as Gaussian-distributed nuisance parameters. The GML estimates are obtained using an iterative quasi-Newton method. To assist in the estimation of the individual channels, we adopt a superimposed training strategy at the relay. We design the pilot vectors of the terminals and the relay to optimize the estimation performance. Furthermore, we derive the semi-blind and pilot-based Cramer-Rao bounds (CRBs) to use as performance benchmarks. Finally, we use simulation studies to show that the proposed method provides substantial improvements in estimation accuracy over the conventional pilot-based estimation and that it approaches the semi-blind CRB as SNR increases. These improvements are possible using only a limited number of OFDM data blocks, which demonstrates the practicality of the semi-blind approach.

EM-Based Semi-Blind Channel Estimation in Amplify-and-Forward Two-Way Relay Networks

S. Abdallah and I. N. Psaromiligkos

*IEEE Wireless Communications Letters*,
vol. 2,
no. 5,
pp. 527-530,
Oct 2013
.

In this letter, we propose an expectation maximization (EM)-based algorithm for semi-blind channel estimation of reciprocal channels in amplify-and-forward (AF) two-way relay networks (TWRNs). By utilizing data samples as well as pilots, the proposed algorithm provides substantially higher estimation accuracy than the conventional training-based least squares (LS) estimator without incurring a significant computational cost. Simulation results also show that it performs very close to the corresponding semi-blind Cramer-Rao bound.

A novel ML based joint TOA and AOA estimator for IR-UWB systems

F. Shang and B. Champagne and I. Psaromiligkos

* in Proceedings of *
*2013 IEEE International Conference on Acoustics, Speech and Signal Processing*,
pp. 5190-5194,
May 2013
.

A novel joint TOA and AOA estimator is proposed for impulse radio Ultra-Wideband (IR-UWB) systems, in which a uniform linear array of antennas is employed at the receiver. The proposed method consists of two steps: (1) coarse estimation of the TOA and the average power delay profile; (2) joint TOA refinement and AOA estimation by maximization of a novel log likelihood function (LLF) using the coarse estimates from the first step. The derivation of the LLF is based on an original approach in which the pulse image from the primary path is modeled as a deterministic component while the superposition of the images from the secondary paths is modeled as a Gaussian random process. In addition, a special gating mechanism is used to characterize the secondary paths, thereby leading to a previously unknown form of the LLF in step (2). According to simulation experiments based on standard UWB channel models, our approach exhibits superior performance to that of a competing scheme from the recent literature.

Semi-blind channel estimation for OFDM-based amplify-and-forward two-way relay networks

S. Abdallah and I. N. Psaromiligkos

* in Proceedings of *
*2013 IEEE International Conference on Acoustics, Speech and Signal Processing*,
pp. 4987-4991,
May 2013
.

We consider the problem of channel estimation for OFDM-based amplify-and-forward (AF) two-way relay networks (TWRNs). Unlike previous works which were based on a fully pilot-based approach, we propose a semi-blind approach that exploits both the transmitted pilots as well as the received data samples to provide an enhanced estimation performance. Superimposed training is adopted at the relay to assist in the estimation of the individual channels. We base our semi-blind estimator on the maximum-likelihood (ML) criterion and employ an iterative low-complexity Quasi-Newton method to obtain the ML semi-blind channel estimates. As a performance benchmark we derive the semi-blind Cramer-Rao bound (CRB). Using simulation studies, we show that the proposed approach provides a substantial improvement in estimation accuracy over the conventional pilot-based approach.

Blind Channel Estimation for Amplify-and-Forward Two-Way Relay Networks Employing M-PSK Modulation

S. Abdallah and I. N. Psaromiligkos

*IEEE Transactions on Signal Processing*,
vol. 60,
no. 7,
pp. 3604-3615,
Jun 2012
.

We consider the problem of channel estimation for amplify-and-forward (AF) two-way relay networks (TWRNs). Most works on this problem focus on pilot-based approaches which impose a significant training overhead that reduces the spectral efficiency of the system. To avoid such losses, we propose blind channel estimation algorithms for AF TWRNs that employ constant-modulus (CM) signaling. Our main algorithm is based on the deterministic maximum likelihood (DML) approach. Assuming M -PSK modulation, we show that the resulting estimator is consistent and approaches the true channel with high probability at high SNR for modulation orders higher than 2. For BPSK, however, the DML algorithm performs poorly and we propose an alternative algorithm that yields much better performance by taking into account the BPSK structure of the data symbols. For comparative purposes, we also investigate the Gaussian maximum-likelihood (GML) approach which treats the data symbols as Gaussian-distributed nuisance parameters. We derive the Cramer-Rao bound and use Monte Carlo simulations to investigate the mean squared error (MSE) performance of the proposed algorithms. We also compare the symbol-error rate (SER) performance of the DML algorithm with that of the training-based least-squares (LS) algorithm and demonstrate that the DML offers a superior tradeoff between accuracy and spectral efficiency.

Joint estimation of time of arrival and channel power delay profile for pulse-based UWB systems

F. Shang and B. Champagne and I. Psaromiligkos

* in Proceedings of *
*2012 IEEE International Conference on Communications (ICC)*,
pp. 4515-4519,
Jun 2012
.

Sub-Nyquist maximum likelihood (ML)-based time of arrival (TOA) estimation methods for ultra-wideband (UWB) signals normally assume a priori knowledge of the UWB channel in the form of the average power delay profile (APDP). In practice however, and despite its importance, the APDP is not always available. To address this issue, we develop in this paper a joint estimator of TOA and APDP. Knowing that the APDP of a UWB channel usually consists of several clusters, each with specific exponential decay rate, a parametric APDP model of this type is employed. The parameters of this model are estimated via a least-squares fitting approach; then the estimated APDP is used to form a likelihood function and obtain a ML estimator of the TOA. Simulations show that the TOA estimated jointly in this way achieves a good accuracy in practical scenarios. The proposed APDP estimate can also help to boost the performance of previously reported TOA estimators that assume a priori APDP knowledge, although the proposed ML scheme generally offers superior performance.

Partially-Blind Estimation of Reciprocal Channels for AF Two-Way Relay Networks Employing M-PSK Modulation

S. Abdallah and I. N. Psaromiligkos

*IEEE Transactions on Wireless Communications*,
vol. 11,
no. 5,
pp. 1649-1654,
May 2012
.

We consider the problem of channel estimation for amplify-and-forward two-way relays assuming channel reciprocity and M-PSK modulation. In an earlier work, a partially-blind maximum-likelihood estimator was derived by treating the data as deterministic unknowns. We prove that this estimator approaches the true channel with high probability at high signal-to-noise ratio (SNR) but is not consistent. We then propose an alternative estimator which is consistent and has similarly favorable high SNR performance. We also derive the Cramer-Rao bound on the variance of unbiased estimators.

A Chernoff-type Lower Bound for the Gaussian Q-function

F. Cote, I. N. Psaromiligkos, and W. Gross

*arXiv [math.PR]*,
Mar 2012
.

A lower bound for the Gaussian Q-function is presented in the form of a single exponential function with parametric order and weight. We prove the lower bound by introducing two functions, one related to the Q-function and the other similarly related to the exponential function, and by obtaining inequalities that indicate the sign of the difference of the two functions.

Widely Linear versus Conventional Subspace-Based Estimation of SIMO Flat-Fading Channels: Mean Squared Error Analysis

S. Abdallah and I. N. Psaromiligkos

*IEEE Transactions on Signal Processing*,
vol. 60,
no. 3,
pp. 1307-1318,
Mar 2012
.

We analyze the mean squared error (MSE) performance of widely linear (WL) and conventional subspace-based channel estimation for single-input multiple-output (SIMO) flat-fading channels employing binary phase-shift-keying (BPSK) modulation when the covariance matrix is estimated using a finite number of samples. The conventional estimator suffers from a phase ambiguity that reduces to a sign ambiguity for the WL estimator. We derive accurate closed-form expressions for the MSE of the two estimators under four ambiguity resolution scenarios. In the first three scenarios, the receiver resolves the ambiguity using some clairvoyant knowledge about the channel. The first scenario, used as a reference, is the ideal case of optimal resolution. The second scenario assumes that one of the channel coefficients is known and the third assumes knowledge of the coefficient with the largest magnitude. The fourth scenario considers the more realistic case where pilot symbols are employed for ambiguity resolution. Our work demonstrates that there is a strong relationship between the accuracy of ambiguity resolution and the relative performance of WL and conventional subspace-based estimators, showing that the WL estimator performs better when partial or inaccurate channel information is employed for ambiguity resolution.

Single-port MMSE beamforming

B. Lawrence and I. N. Psaromiligkos

* in Proceedings of *
*2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications*,
pp. 1557-1561,
Sep 2011
.

We develop a novel, microwave beamforming algorithm based on the minimum mean-square error (MMSE) criterion. Relative to perturbation algorithms, the proposed method requires only a fraction of weight changes to estimate the beamformer weights, thereby making it more suitable for high-data-rate applications. Numerical simulations reveal that the proposed algorithm offers promising performance and is less susceptible to performance degradation than the perturbation algorithm when used with slow-switching and/or finite-resolution phase shifters and amplitude-control devices.

Widely linear vs. conventional subspace-based estimation of SIMO flat-fading channels

S. Abdallah and I. N. Psaromiligkos

* in Proceedings of *
*2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications*,
pp. 1672-1676,
Sep 2011
.

We analyze the mean-squared error (MSE) performance of widely linear (WL) and conventional subspace-based channel estimation for single-input multiple-output (SIMO) flat-fading channels employing binary phase-shift-keying (BPSK) modulation when the covariance matrix is estimated using a finite number of samples. The conventional estimator suffers from a phase ambiguity that reduces to a sign ambiguity for the WL estimator. We derive closed-form expressions for the MSE of the two estimators under four different scenarios which vary in the amount and accuracy of the information available for ambiguity resolution. Our work demonstrates that the relative performance of WL and conventional subspace-based estimators is strongly related to the accuracy of ambiguity resolution and shows that the less information available about the actual channel for ambiguity resolution, or the lower the accuracy of this information, the more favorable the WL estimator becomes.

GNSS Modulation: A Unified Statistical Description

F. D. Cote and I. N. Psaromiligkos and W. J. Gross

*IEEE Transactions on Aerospace and Electronic Systems*,
vol. 47,
no. 3,
pp. 1814-1836,
Jul 2011
.

A unifying framework for all signals employed by the Global Positioning System (GPS) and Galileo system is presented. The framework reconciles split-spectrum modulations under a single analytical formulation. The formulation allows for the derivation of closed-form equations for the autocorrelation function (ACF) and power spectral density (PSD) containing, as special cases, the corresponding functions for GPS and Galileo signals. Simulation studies and comparisons with existing expressions demonstrate the generality and accuracy of the proposed statistical description.

Blind channel estimation for MPSK-based amplify-and-forward two-way relaying

S. Abdallah and I. N. Psaromiligkos

* in Proceedings of *
*2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*,
pp. 2828-2831,
May 2011
.

We consider the problem of channel estimation for amplify-and-forward (AF) two-way relay networks (TWRNs). The majority of works on this problem develop pilot-based algorithms that allocate significant resources for training. We will show in this work that such overhead is not necessary when the terminals employ M-ary phase-shift keying (M-PSK). Using the constant-modulus nature of the transmitted symbols, we develop a relaxed blind maximum-likelihood (ML) channel estimator. We study the performance of the ML estimator in the high SNR and large sample-size scenarios, demonstrating that it performs well in both cases. As a benchmark, we also present and analyze an intuitive low-complexity estimator based on sample-averaging. Simulation studies are used to compare the mean-squared error performance of the two algorithms.

Joint estimation of time of arrival and power profile for UWB localization

F. Shang and B. Champagne and I. Psaromiligkos

* in Proceedings of *
*IEEE 10th International Conference on Signal Processing*,
pp. 1484-1487,
Oct 2010
.

In time of arrival (TOA) estimation of received ultra-wideband (UWB) pulses, traditional maximum likelihood (ML) and generalized likelihood estimators become impractical due to their high sampling rate. Sub-nyquist ML-based TOA estimation currently assumes a priori knowledge of the UWB channels in the form of the average power delay profile (APDP). In this paper, instead of assuming a known APDP, we propose and investigate a joint estimator of the TOA and the APDP. A parametric model is assumed for the APDP and its parameters are estimated via a least-squares approach; the estimated APDP is then used to find the TOA estimate. The proposed method requires low sampling rate and is well-suited for real-time implementation. Simulation results show that it can achieve a fine accuracy in practical UWB TOA estimation scenarios.

On the code tracking performance of GNSS modulation

F. D. Côté and I. N. Psaromiligkos and W. J. Gross

* in Proceedings of *
*2010 44th Annual Conference on Information Sciences and Systems (CISS)*,
pp. 1-5,
Mar 2010
.

A Bayesian lower bound is proposed as a new baseline for the positioning accuracy provided by satellite radionavigation signals in the presence of Gaussian noise and interference. Positioning accuracy is typically investigated using the Crame¿¿r-Rao Bound (CRB), but the CRB does not always constitute a tight benchmark. The proposed bound is used to obtain a threshold on signal energy that indicates the suitability of the CRB. The threshold reveals that a large signal bandwidth cannot reliably compensate for low signal energy in order to sustain positioning accuracy.

Semi-blind channel estimation for amplify-and-forward two-way relay networks employing constant-modulus constellations

S. Abdallah and I. N. Psaromiligkos

* in Proceedings of *
*2010 44th Annual Conference on Information Sciences and Systems (CISS)*,
Mar 2010
.

We present two channel estimation algorithms for amplify-and-forward two-way relay networks that employ constant-modulus constellations. The proposed algorithms do not assume complete knowledge of the transmitted symbols: they only require the transmission of a very short training sequence in order to resolve the inevitable phase ambiguity in the channel estimate. We first derive the maximum-likelihood (ML) estimator which is shown to perform very well even for a relatively small number of samples when the signal-to-noise ratio is sufficiently high. To address the high computational complexity of the ML estimator we propose a second, simpler, algorithm that can be updated at run-time and performs well for a sufficiently large sample size. Theoretical and experimental studies demonstrate the performance of the proposed algorithms.

Design and Analysis of Supervised and Decision-Directed Estimators of the MMSE/LCMV Filter in Data Limited Environments

J. M. Farrell and I. N. Psaromiligkos and S. N. Batalama

*IEEE Transactions on Signal Processing*,
vol. 56,
no. 2,
pp. 437-446,
Feb 2008
.

We consider sample-matrix-inversion (SMI)-type estimates of the minimum-mean-square-error (MMSE) and the linearly constrained-minimum-variance (LCMV) linear filters obtained from data records of limited size. We quantify theoretically the (detrimental) effect of the desired-signal energy level on the mean square (MS) filter estimation error and the normalized output signal-to-interference-plus-noise ratio (SINR) by deriving a new exact analytical expression and a lower bound, respectively. For cases where accumulation of pure disturbance observations is not possible, we show theoretically how certain intuitive, pilot-assisted, and decision-directed adaptive filter implementations that utilize desired-signal-present data/observations perform close to their desired-signal-absent counterparts. Simulation studies illustrate our theoretical developments in the context of spread-spectrum communications over multipath fading channels under perfect and nonperfect synchronization.

Performance of blind channel estimation algorithms for space-frequency block coded multi-carrier code division multiple access systems

S. N. Nazar and I. N. Psaromiligkos

*IET Communications*,
vol. 2,
no. 2,
pp. 320-328,
Feb 2008
.

The problem of blind channel estimation for downlink space-frequency block coded multi-carrier code division multiple access (SFBC MC-CDMA) schemes is considered. For these schemes, the authors first develop a system model for complex modulated signals, which reduces the multichannel estimation problem to a single-input single-output problem. Then, they present an intuitive subspace-based channel estimation method along with the corresponding necessary and sufficient conditions under which the channel estimate is unique (within a complex scalar). Their studies highlight two interesting properties of SFBC MC-CDMA systems: (i) there is no antenna order ambiguity (also known as permutation ambiguity) even though only one spreading code is assigned to each user; (ii) channel identifiability is guaranteed, regardless of the channel zeros location. They also establish the unbiasedness of the channel estimates and derive closed-form expressions for the mean-square-error of the estimates as well as the corresponding Cramer-Rao bound (CRB). In the derivation of the CRB, they suggest a novel approach which assumes the knowledge of only the spreading code of desired user. This approach results in a tighter bound than the CRB derived based on the knowledge of all users' signatures.

Minimum Variance Channel Estimation in MC-CDMA Systems: Bias Analysis and Cramer-Rao Bound

S. Nayeb Nazar and I. N. Psaromiligkos

*IEEE Transactions on Signal Processing*,
vol. 55,
no. 6,
pp. 3143-3148,
Jun 2007
.

We present a comprehensive performance analysis of the minimum variance channel estimator for multicarrier code-division multiple access systems. We provide novel highly accurate closed form expressions for the bias due to the additive noise as well as the finite data record mean-square error of the channel estimates. In addition, we derive the corresponding Crameacuter-Rao bound that assumes the knowledge of only the spreading code of the desired user

MUSIC-Based Joint DoA Estimation and Signal Enumeration

R. Wu and I. N. Psaromiligkos

* in Proceedings of *
*2007 Canadian Conference on Electrical and Computer Engineering*,
pp. 1030-1033,
Apr 2007
.

In this paper we describe a new criterion for the detection of the number of signals impinging on a M-element uniform linear array (ULA). Our criterion makes explicit use of the peak information of the MUSIC spectrum. Specifically, for each hypothesis of k sources, in addition to computing the noise variance estimate using the M-k smallest eigenvalues of the sample covariance matrix, the new criterion applies an additional correction term calculated from the k largest peaks of the MUSIC spectrum which is generated from the testing noise subspace of dimension M-k. We prove that the proposed criterion provides a consistent estimate of the number of signals and demonstrate that it has a better performance at low SNR for equal-power sources when compared with the original MDL-based signal number detection criterion [1]. Enumeration Ren Wu and Ioannis N. Psaromiligkos

Short Data Record DoA and Timing Estimation in Multipath DS/CDMA Systems

R. Wu and I. N. Psaromiligkos

* in Proceedings of *
*2007 Canadian Conference on Electrical and Computer Engineering*,
pp. 753-756,
Apr 2007
.

We consider the problem of joint direction-of-arrival (DoA) and time delay estimation for antenna-array DS/CDMA systems operating in multipath propagation environments where signal coherency can severely deteriorate the estimation accuracy. We propose two MUSIC-type algorithms based on the spatial smoothing technique that essentially break the coherency of the multipath signals. The proposed algorithms utilize space-time received vectors that span only a single information symbol period and exhibit superior performance when the data record size available for parameter estimation is limited.

Efficient Second-Order Statistics-based Channel Estimation Algorithms for MC-CDMA Systems Using Transmit Diversity

S. N. Nazar and I. N. Psaromiligkos

* in Proceedings of *
*IEEE Globecom 2006*,
pp. 1-6,
Nov 2006
.

Blind channel estimation algorithms for the downlink of space-frequency block coded multi-carrier code division multiple access (SFBC MC-CDMA) schemes are presented. We first formulate two channel estimation methods based on the second-order statistics (SOS) of the frequency-domain received signal, namely: (i) a minimum variance (MV) criterion, and (ii) a subspace-based approach. Then, we highlight the unique structure of the input covariance matrix. The matrix structure allows us to propose modifications of the presented algorithms that are computationally efficient and offer enhanced performance in practical cases where only an estimate of the received signal covariance matrix is available. Moreover, we address the issue of channel identifiability by investigating the necessary and sufficient conditions under which the channel estimates are unique (within a complex scalar).

Efficient Minimum Variance Receivers for MC-CDMA Systems Using Transmit Diversity

S. Nayeb Nazar and I. N. Psaromiligkos

* in Proceedings of *
*2006 Fortieth Asilomar Conference on Signals, Systems and Computers*,
pp. 2209-2213,
Oct 2006
.

The issues of blind channel estimation and information symbol detection for the downlink of Space-Frequency Block Coded Multi-Carrier Code Division Multiple Access (SFBC MC- CDMA) schemes are revisited. Specifically, we first formulate blind channel estimation and detection algorithms based on the Second-Order Statistics (SOS) of the frequency-domain received signal using a Minimum Variance (MV) criterion. Then, we identify the unique structure of the input covariance matrix based on which we propose modifications of the presented algorithms that are computationally efficient and offer enhanced performance in practical cases where only an estimate of the received signal covariance matrix is available. The effectiveness of the new algorithms utilizing the special structure of the received signal covariance matrix is demonstrated through the comparison with the corresponding conventional approaches.

Robust Minimum Variance Beamforming with Dual Response Constraints

M. Robinson and I. Psaromiligkos

* in Proceedings of *
*2006 Fortieth Asilomar Conference on Signals, Systems and Computers*,
pp. 2276-2280,
Oct 2006
.

The minimum variance distortionless response (MVDR) beamformer is a popular method of combining multiple antenna outputs in order to recover a signal of interest (SOI) with known steering vector in the presence of noise and interference. However, in practice the precise value of the SOI steering vector is unknown and only an estimate is used. In hopes of protecting the actual SOI in case of mismatches it has been recently proposed to use a non-attenuation constraint inside a hypersphere centered at the presumed SOI steering vector. In an effort to strike a balance between robustness to steering vector error and interference-plus-noise suppression, we propose in this paper to use two concentric hyperspheres instead of one with different degrees of protection in each. We determine conditions on the user defined parameters to ensure existence of a solution to the resulting constrained optimization problem. The multiply constrained filter solution is demonstrated to be of the diagonally loaded type with adaptive loading factor. We further give necessary conditions for the two-constraint filter to be distinct, in terms of SINR, to the one-constraint case. Numerical simulations show that using two constraints yields improved SINR performance compared to one constraint for small steering vector mismatches or large input SNR.

A Simple Method for the Evaluation of the BER Performance of Space-Time SMI-MVDR DS/CDMA Receivers

O. F. Escudero and I. N. Psaromiligkos

* in Proceedings of *
*IEEE Vehicular Technology Conference*,
Sep 2006
.

We propose a method to approximate the bit-error-rate (BER) performance of antenna-array-based CDMA systems employing sample-matrix-inversion (SMI) minimum-variance-distortionless-response (MVDR) space-time (ST) linear receivers. In the proposed method, a form of the Gaussian Q(ldr) function is approximated by a finite series of exponentially decaying cosines that are optimized using a recently proposed relative error measure. The proposed method provides accurate closed form approximations to the BER performance for a wide range of practical scenarios with very low complexity.

BER performance of BPSK transmissions over multipath channels

O. Fonseca and I. N. Psaromiligkos

*Electronics Letters*,
vol. 42,
no. 20,
pp. 1164-1165,
Sep 2006
.

A new closed form expression for the bit error rate (BER) performance of binary phase shift keying (BPSK) transmissions over frequency selective channels is presented. The expression is obtained through a novel approximation of the Gaussian Q(middot) function by a fixed series of sinusoids with exponentially decreasing amplitudes. Numerical results demonstrate the accuracy of the derived expression

Further Results on the Performance of Minimum Variance Channel Estimation Algorithms for MC-CDMA Systems

S. N. Nazar and I. N. Psaromiligkos

* in Proceedings of *
*IEEE Vehicular Technology Conference*,
pp. 1-5,
Sep 2006
.

A comprehensive performance analysis of the minimum variance (MV) channel estimator for multicarrier code-division multiple access (MC-CDMA) systems is presented. In particular, we provide closed form expressions for the asymptotic bias and the mean-square-error (MSE) of the estimates as well as the corresponding Cramer-Rao bound (CRB). The derived formula for the bias due to the additive noise characterizes accurately the behavior of the actual MV estimator in the case of heavy system loading or small processing gain. In addition, in the derivation of the CRB, we suggest a novel approach which assumes the knowledge of only the spreading code of the desired user. This approach results in a tighter bound than the CRB derived based on the knowledge of all users' signatures.

Spatial-Smoothing-Based Direction-of-Arrival, Propagation Delay and Channel Estimation for Antenna-Array DS/CDMA Systems

R. Wu and I. N. Psaromiligkos

* in Proceedings of *
*IEEE Vehicular Technology Conference*,
pp. 1-5,
Sep 2006
.

We consider the problem of joint estimation of direction-of-arrival (DoA), propagation delay, and complex channel gain for antenna-array-based DS/CDMA communication systems over frequency selective multipath channels. We propose a MUSIC-type estimation algorithm which utilizes the spatial smoothing preprocessing technique. The proposed algorithm essentially breaks the multipath-induced coherency within the received signals and recovers the full signal subspace spanned by all dominant signal paths of all users. This allows the use of MUSIC-type DoA and delay estimators for the individual paths of the user of interest. Based on the angle and timing information, we then estimate the multipath fading coefficients. Simulation results illustrate the effectiveness of this approach.

Bit-error-rate performance evaluation of SMI-MSINR and SMI-MVDR DS/CDMA receivers

O. F. Escudero and I. N. Psaromiligkos

* in Proceedings of *
*IEEE Wireless Communications and Networking Conference, 2006. WCNC 2006.*,
vol. 3,
pp. 1597-1602,
Apr 2006
.

We propose a method to approximate the bit-error-rate (BER) performance of CDMA systems employing sample-matrix-inversion (SMI) maximum-signal-to-interference-plus-noise-ratio (MSINR) and SMI minimum-variance-distortionless-response (MVDR) linear receivers. In the proposed method, two forms of the Gaussian Q-function are approximated by finite series of exponentials and exponentially decaying cosines that are optimized using a relative error measure. The proposed method provides highly accurate closed form approximations to the BER performance for a wide range of practical scenarios with very low complexity

On subspace channel estimation for chip-level space-time block coded multi-rate CDMA

E. B. Nicolov and S. N. Nazar and I. N. Psaromiligkos

* in Proceedings of *
*IEEE Wireless Communications and Networking Conference, 2006. WCNC 2006.*,
vol. 3,
pp. 1525-1530,
Apr 2006
.

We investigate the problem of blind channel estimation for the downlink of a variable spreading gain (VSG) multi-rate DS/CDMA (MR CDMA) system which uses the orthogonal space-time block code (STBC) proposed by Alamouti. In contrast to traditional symbol-level STBC CDMA systems, we consider a chip-level ST block coding system for which we derive a simple subspace based blind channel estimation method. For this algorithm we investigate the necessary and sufficient conditions for the channel to be uniquely identifiable up to a complex multiplicative constant. Furthermore, we analyze the behavior of the channel estimation algorithm as a function of the data samples utilized, establishing its unbiasedness and providing a closed form expression for the mean-square-error (MSE) as well as the Cramer-Rao bound for the channel estimation performance

Performance analysis of the MVDR channel estimator for space-frequency block coded MC-CDMA systems

S. Nayeb Nazar and I. N. Psaromiligkos

* in Proceedings of *
*IEEE Wireless Communications and Networking Conference, 2006. WCNC 2006.*,
vol. 4,
pp. 2029-2034,
Apr 2006
.

Minimum variance distortionless response (MVDR)-type blind channel estimation algorithms for the downlink of space-frequency block coded multi-carrier code division multiple access (SFBC-MC-CDMA) schemes are developed and analyzed. The performance of the channel estimator under sufficiently large data record size and small noise assumptions is studied by deriving analytical expressions for the bias and the variance of the estimator. Analysis of the mean-squared error (MSE) of the proposed estimator shows that at sufficiently high SNRs the squared norm of the bias can be considered negligible compared to the variance of the estimator. Furthermore, we establish a bound on the MSE of the biased estimates which is specifically derived for downlink transmissions where the knowledge of only the spreading code of the desired user is available. Finally, simulation examples illustrate the performance of the MVDR channel estimator and the accuracy of our analytical results

On Subspace-based Blind Channel Estimation Algorithms for SFBC MC-CDMA systems

S. N. Nazar and I. N. Psaromiligkos

* in Proceedings of *
*Thirty-Ninth Asilomar Conference on Signals, Systems and Computers, 2005.*,
pp. 1089-1093,
Oct 2005
.

Detection and channel estimation algorithms for chip-level space-frequency block coded MC-CDMA systems

S. N. Nazar and I. N. Psaromiligkos

* in Proceedings of *
*IEEE International Conference on Wireless And Mobile Computing, Networking And Communications (WiMob), 2005.*,
vol. 1,
pp. 145-152 Vol. 1,
Aug 2005
.

A combined space-frequency (SF) block coding and multi-carrier code division multiple access (MC-CDMA) scheme for downlink transmissions is presented, in which SF block coding is performed at the chip level, i.e. after code spreading. For this scheme, we develop linear single-user joint SF decoding and detection algorithms that exhibit low complexity, low decoding delay and enhanced performance for frequency-selective fading channels compared to their symbol-level counterparts. In addition, it is demonstrated that by exploiting the signal structure imposed by the chip-level SF block coding, blind channel estimation (with only a scalar ambiguity) is feasible, without assigning multiple signatures to each user. Finally, simulation examples demonstrate the performance of the proposed algorithms.

Received signal strength based location estimation of a wireless LAN client

M. Robinson and I. Psaromiligkos

* in Proceedings of *
*IEEE Wireless Communications and Networking Conference, 2005*,
vol. 4,
pp. 2350-2354 Vol. 4,
Mar 2005
.

We consider the problem of identifying the location of a mobile client in an IEEE 802.11b wireless LAN. We propose an estimation algorithm that utilizes received signal strength (RSS) measurements provided by most 802.11b physical layer implementations. In contrast to traditional radio map-based methods that require a burdensome map building phase, the proposed algorithm requires no prior training by employing a probabilistic RSS model that is derived from indoor signal propagation models and the blueprint of the building in which the location estimation system is being deployed. Experimental studies demonstrate the accuracy of the proposed method.

Data-record size requirements for adaptive space-time DS-CDMA signal detection

I. N. Psaromiligkos and S. N. Batalama

*IEEE Transactions on Communications*,
vol. 52,
no. 9,
pp. 1538-1546,
Sep 2004
.

We investigate the data-record size requirements of sample-matrix-inversion-based minimum-variance-distortionless response and maximum-signal-to-interference-plus-noise-ratio adaptive algorithms to meet a given performance objective in joint space-time signal-detection problems for direct-sequence code-division multiple-access systems. We derive closed-form expressions that provide the data-record size that is necessary to achieve a given performance confidence level in a neighborhood of the optimal performance point, as well as expressions that identify the performance level that can be reached for a given data-record size. This is done by using close approximations of the involved probability density functions. The practical significance of the derived expressions lies in the fact that the expressions are functions of the number of antenna elements, the number of multipaths, and the system spreading gain only, while they depend neither on the ideal input covariance matrix, which is not known in most realistic applications, nor on the exact ideal performance value.

Evaluating average causal effect using wireless sensor networks

M. Coates and I. Psaromiligkos

* in Proceedings of *
*2004 IEEE International Conference on Acoustics, Speech, and Signal Processing*,
vol. 3,
pp. iii-905-8 vol.3,
May 2004
.

Sensor networks have exciting potential applications in agriculture and medicine, where after the application of treatment, it is beneficial not merely to track the response but to assess the causal impact of the treatment reception. We describe a distributed algorithm for the evaluation of the average causal effect of treatment reception upon response. Our procedure applies the expectation-maximization algorithm across a graphical model of the system, using local message-passing techniques. The key collaborative step in the algorithm is simple message aggregation and averaging, which we perform over a tree network topology. Finally, for completeness purposes, we describe a simple framework for the construction and maintenance of the tree topology that provides a robust mechanism for executing the algorithm using spread-spectrum or ultra-wideband communication.

Fast converging minimum probability of error neural network receivers for DS-CDMA communications

J. D. Matyjas and I. N. Psaromiligkos and S. N. Batalama and M. J. Medley

*IEEE Transactions on Neural Networks*,
vol. 15,
no. 2,
pp. 445-454,
Mar 2004
.

We consider a multilayer perceptron neural network (NN) receiver architecture for the recovery of the information bits of a direct-sequence code-division-multiple-access (DS-CDMA) user. We develop a fast converging adaptive training algorithm that minimizes the bit-error rate (BER) at the output of the receiver. The adaptive algorithm has three key features: i) it incorporates the BER, i.e., the ultimate performance evaluation measure, directly into the learning process, ii) it utilizes constraints that are derived from the properties of the optimum single-user decision boundary for additive white Gaussian noise (AWGN) multiple-access channels, and iii) it embeds importance sampling (IS) principles directly into the receiver optimization process. Simulation studies illustrate the BER performance of the proposed scheme.

Recursive short-data-record estimation of AV and MMSE/MVDR linear filters for DS-CDMA antenna array systems

I. N. Psaromiligkos and S. N. Batalama

*IEEE Transactions on Communications*,
vol. 52,
no. 1,
pp. 136-148,
Jan 2004
.

The presence of the desired signal during estimation of the minimum mean-square error (MMSE)/minimum-variance distortionless-response (MVDR) and auxiliary-vector (AV) filters under limited data support leads to significant signal-to-interference-plus-noise ratio (SINR) performance degradation. We quantify this observation in the context of direct-sequence code-division multiple-access (DS-CDMA) communications by deriving close approximations for the mean-square filter estimation error, the probability density function of the output SINR, and the probability density function of the symbol-error rate (SER) of the sample matrix inversion (SMI) receiver evaluated using both a desired-signal-present and desired-signal-absent input covariance matrix. To avoid such performance degradation, we propose a DS-CDMA receiver that utilizes a simple pilot-assisted algorithm that estimates and then subtracts the desired signal component from the received signal prior to filter estimation. Then, to accommodate decision-directed operation, we develop two recursive algorithms for the on-line estimation of the AV and MMSE/MVDR filter and we study their convergence properties. Finally, simulation studies illustrate the SER performance of the overall receiver structures.

On the relative output SINR of full and partial decorrelators

Ping Xiong and I. N. Psaromiligkos and S. N. Batalama

*IEEE Transactions on Communications*,
vol. 51,
no. 10,
pp. 1633-1637,
Oct 2003
.

We investigate the relative output signal-to-interference-plus-noise ratio (SINR) performance of two linear direct-sequence code-division multiple-access multiuser detectors: the full decorrelator and the partial decorrelator. We derive necessary and sufficient conditions on the system parameters under which the partial decorrelator outperforms the full decorrelator in the output SINR sense. As a side study, we consider a blind implementation of the full decorrelator that is based on eigendecomposition of the interference-plus-noise autocovariance matrix and can be easily modified to provide a partial decorrelator. Simulation studies illustrate the relative SINR and bit-error rate performance of the full and partial decorrelator under perfectly known and sample-average-estimated input statistics.

Single-user detection algorithms for space-time block coded DS/CDMA transmissions over multipath fading channels

S. N. Nazar and I. N. Psaromiligkos

* in Proceedings of *
*2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484)*,
vol. 2,
pp. 1084-1088 Vol.2,
Oct 2003
.

We consider a combined space-time (ST) block coding and direct-sequence code division multiple access (DS/CDMA) scheme for downlink transmissions over multipath fading channels, in which ST block coding is performed at the chip level, i.e. after code spreading. For this scheme, we develop low complexity and low decoding delay linear ST single-user detection algorithms. In addition, we demonstrate that by exploiting the signal structure imposed by the chip-level ST block coding, blind channel multipath estimation (with only a scalar ambiguity) is feasible, even with the assignment of a single code vector to each user.

Rapid combined synchronization/demodulation structures for DS-CDMA systems - part II: finite data-record performance analysis

I. N. Psaromiligkos and S. N. Batalama

*IEEE Transactions on Communications*,
vol. 51,
no. 7,
pp. 1162-1172,
Jul 2003
.

We investigate the coarse synchronization performance of blind adaptive linear self-synchronized receivers for asynchronous direct-sequence code-division multiple-access communications under finite data record adaptation. Based on transformation noise modeling techniques, three alternative methods are developed, leading to analytical expressions that approximate the probability of coarse synchronization error of matched-filter-type and minimum-variance distortionless-response-type receivers. The expressions are explicit functions of the data record size and the filter order and reveal the effect of short data-record sample matrix-inversion implementations on the coarse synchronization performance. Besides their theoretical value, the derived expressions provide simple, highly-accurate alternatives to computationally demanding performance evaluation through simulations. The effect of the data record size on the probability of coarse synchronization error is further quantified through the use of a receiver synchronization resolution metric. Numerical and simulation studies examine the accuracy of the theoretical developments and show that the derived expressions approximate closely the actual coarse synchronization performance.

Rapid combined synchronization/demodulation structures for DS-CDMA systems - part I: Algorithmic developments

I. N. Psaromiligkos and S. N. Batalama and M. J. Medley

*IEEE Transactions on Communications*,
vol. 51,
no. 6,
pp. 983-994,
Jun 2003
.

Blind adaptive linear receivers are considered for the demodulation of direct-sequence code-division multiple-access signals in asynchronous transmissions. The proposed structures are self-synchronized in the sense that adaptive synchronization and demodulation are viewed and treated as an integrated receiver operation. Two computationally efficient combined synchronization/demodulation schemes are proposed, developed, and analyzed. The first scheme is based on the principles of minimum-variance distortionless-response processing, while the second scheme follows the principles of auxiliary-vector filtering and exhibits enhanced performance in short data-record scenarios. In both cases, the resulting receiver is a linear structure of order exactly equal to the system processing gain. Simulation studies included in this paper demonstrate the coarse synchronization as well as the bit-error rate performance of the proposed strategies.

On the relative output SINR of the full and partial decorrelators

Ping Xiong and I. N. Psaromiligkos and S. N. Batalama

* in Proceedings of *
*2001 IEEE Military Communications Conference (MILMCOM)*,
vol. 2,
pp. 1423-1428 vol.2,
Oct 2001
.

We investigate the relative output SINR performance of two linear multiuser detectors - the full decorrelator and the partial decorrelator. For each detector, we consider two implementations that are equivalent under perfectly known input statistics. The first implementation utilizes the signature matrix while the second implementation is based on the eigendecomposition of the ideal input covariance matrix. While the full decorrelator aims at decorrelating the complete multiple-access-interference, the partial decorrelator aims at decorrelating only a part of it by excluding one or more user-signatures or eigenvectors from the corresponding implementation method. We derive necessary and sufficient conditions on the signal energy and signature cross-correlation levels under which the partial decorrelator outperforms the full decorrelator in the output SINR sense. Numerical results demonstrate the validity of the above conditions and simulation studies illustrate the relative SINR and BER performance of the full and partial decorrelators under sample-average-estimated input statistics.

Interference-plus-noise covariance matrix estimation for adaptive space-time processing of DS/CDMA signals

I. N. Psaromiligkos and S. N. Batalama

* in Proceedings of *
*52nd Vehicular Technology Conference Fall 2000*,
vol. 5,
pp. 2197-2204,
Sep 2000
.

The presence of the desired signal during the estimation of the minimum-variance-distortionless-response (MVDR) or a auxiliary-vector (AV) filter under limited data records leads to significant signal-to-interference-plus-noise ratio (SINR) performance degradation. We quantify this observation in the context of DS/CDMA communications by deriving two new close approximations for the probability density functions (under both desired-signal-“present” and desired-signal-“absent” conditions) of the output SINR and bit-error-rate (BER) of the sample-matrix-inversion (SMI) MVDR receiver. To avoid such performance degradation we propose a DS/CDMA receiver that utilizes a simple pilot-assisted algorithm that estimates and then subtracts the desired signal component from the received signal prior to filter estimation. Then, to accommodate decision directed operation we develop two recursive algorithms for the on-line estimation of the MVDR and AV filter and we study their convergence properties. Finally, simulation studies illustrate the BER performance of the overall receiver structures

Blind self-synchronized receivers for DS/CDMA communications

I. N. Psaromiligkos and S. N. Batalama

* in Proceedings of *
*2000 IEEE International Conference on Communications (ICC)*,
vol. 2,
pp. 949-953 vol.2,
Jun 2000
.

We consider blind adaptive linear receivers for the demodulation of DS/CDMA signals in asynchronous transmissions. The proposed structures are self-synchronized in the sense that adaptive synchronization and demodulation are viewed and treated as an integrated receiver operation. Two computationally efficient combined synchronization/demodulation schemes are proposed, developed and analyzed. The first scheme is based on the principles of minimum-variance-distortionless-response (MVDR) processing, while the second scheme follows the principles of auxiliary-vector filtering and exhibits enhanced performance in short data record scenarios. The coarse synchronization performance of combined synchronization/demodulation receivers under finite data record adaptation is also investigated. Analytic expressions are derived that approximate closely the probability of coarse synchronization error of the conventional correlator and the MVDR type combined synchronization/demodulation scheme and provide low cost highly accurate alternatives to the computationally demanding performance evaluation through simulations

Data record size requirements of MVDR-optimized adaptive antenna arrays

S. N. Batalama and I. N. Psaromiligkos

* in Proceedings of *
*2000 IEEE International Conference on Acoustics, Speech, and Signal Processing*,
vol. 5,
pp. 3069-3072,
Jun 2000
.

We investigate the data-record-size requirements of the minimum-variance-distortionless-response beamformer to meet a given performance objective in signal detection and direction-of-arrival estimation problems. For signal detection problems we consider the output-energy performance measure while for direction-of-arrival estimation problems we adopt a spectrum-based measure defined as the ratio between the estimated and the ideal spectrum. In both cases, closed form expressions are derived that provide the data record size that is necessary to achieve a given performance confidence level in a neighborhood of the optimal performance point. This is done by utilizing close approximations of the involved probability density functions and Markoff-type inequalities. The practical significance of the derived expressions lies in the fact that the expressions are functions of the number of antenna elements only, while they are independent of the ideal input covariance matrix which is not known in most realistic applications

Finite data record performance analysis of rapid synchronization and combined demodulation algorithms

I. N. Psaromiligkos and S. N. Batalama

* in Proceedings of *
*2000 IEEE International Conference on Acoustics, Speech, and Signal Processing*,
vol. 5,
pp. 2557-2560,
Jun 2000
.

We investigate the coarse synchronization performance of matched-filter-type (MF) and minimum-variance-distortion less-response-type (MF) near self-synchronized receivers for asynchronous direct-sequence code-division-multiple-access communications under finite data record adaptation. Analytic expressions are derived that approximate closely the probability of coarse synchronization error and provide low-cost highly-accurate alternatives to the computationally demanding performance evaluation through simulations. The expressions are explicit functions of the data record size N and the filter order p and reveal the effect of short-data-record sample-matrix-inversion (SMI) implementations on the coarse synchronization performance

Finite data record maximum SINR adaptive space-time processing

I. N. Psaromiligkos and S. N. Batalama

* in Proceedings of *
*Tenth IEEE Workshop on Statistical Signal and Array Processing*,
pp. 677-681,
Jan 2000
.

The presence of the desired signal during the estimation of the minimum variance distortionless response (MVDR) or auxiliary vector (AV) filter under limited data records leads to significant signal-to-interference-plus-noise ratio (SINR) performance degradation. We quantify this observation in the context of DS/CDMA communications by deriving two new close approximations for the probability density functions (under both desired signal “present” and “absent” conditions) of the output SINR and bit error rate (BER) of the sample matrix inversion (SMI) MVDR receiver. To avoid such performance degradation we propose a DS/CDMA receiver that utilizes a simple pilot-assisted algorithm that estimates and then subtracts the desired signal component from the received signal prior to filter estimation. Then, to accomodate decision directed operation we develop two recursive algorithms for the on-line estimation of the MVDR and AV filter and we study their convergence properties. Finally, simulation studies illustrate the BER performance of the overall receiver structure

On adaptive minimum probability of error linear filter receivers for DS-CDMA channels

I. N. Psaromiligkos and S. N. Batalama and D. A. Pados

*IEEE Transactions on Communications*,
vol. 47,
no. 7,
pp. 1092-1102,
Jul 1999
.

Receiver architectures in the form of a linear filter front-end followed by a hard-limiting decision maker are considered for DS-CDMA communication systems. Based on stochastic approximation concepts a recursive algorithm is developed for the adaptive optimization of the linear filter front-end in the minimum BER sense. The recursive form is decision driven and distribution free. For additive white Gaussian noise (AWGN) channels, theoretical analysis of the BER surface of linear filter receivers identifies the subset of the linear filter space where the optimal receiver lies and offers a formal proof of guaranteed global optimization with probability one for the two-user case. To the extent that the output of a linear DS-CDMA filter can be approximated by a Gaussian random variable, a minimum-mean-square-error optimized linear filter approximates the minimum BER solution. Numerical and simulation results indicate that for realistic AWGN DS-CDMA systems with reasonably low signature cross-correlations the linear minimum BER filter and the MMSE filter exhibit approximately the same performance. The linear minimum BER receiver is superior, however, when either the signature cross-correlation is high or the background noise is non-Gaussian

Adaptive robust spread-spectrum receivers

S. N. Batalama and M. J. Medley and I. N. Psaromiligkos

*IEEE Transactions on Communications*,
vol. 47,
no. 6,
pp. 905-917,
Jun 1999
.

We consider the problem of robust detection of a spread-spectrum (SS) signal in the presence of unknown correlated SS interference and additive non-Gaussian noise. The proposed general SS receiver structure is comprised by a vector of adaptive chip-based nonlinearities followed by an adaptive linear tap-weight filter and combines the relative merits of both nonlinear and linear signal processing. The novel characteristics of our approach are as follows. First, the nonlinear receiver front-end adapts itself to the unknown prevailing noise environment providing robust performance for a wide range of underlying noise distributions. Second, the adaptive linear tap-weight filter that follows the nonlinearly processed chip samples results in a receiver that is proven to be effective in combating SS interference as well. To determine the receiver parameters, we propose, develop, and study three adaptive schemes under a joint mean-square error (MSE), or a joint bit-error-rate (BER), or a joint MSE-BER optimization criterion. As a side result, we derive the optimum decision fusion filter for receivers that utilize hard-limiting (sign) chip nonlinearities. Numerical and simulation results demonstrate the performance of the proposed schemes and offer comparisons with the conventional matched-filter (MF), the decorrelator, the conventional minimum-variance-distortionless-response (MVDR) filter, and the sign-majority vote receiver

Bit error rate optimization of DS-CDMA receivers

I. N. Psaromiligkos and S. N. Batalama

* in Proceedings of *
*9th European Signal Processing Conference (EUSIPCO 1998)*,
pp. 1-4,
Sep 1998
.

In search of acceptable cost versus performance trade-off points for DS-CDMA receivers linear tap-weight filters are considered. Based on stochastic approximation concepts a recursive algorithm is developed for the adaptive optimization of linear filters in the minimum Bit Error Rate (BER) sense. The recursive form is decision driven and distribution free. For AWGN channels, theoretical analysis of the BER surface of linear filter receivers identifies the subset of the linear filter space where the optimal receiver lies and offers a formal proof of guaranteed global optimization with probability one for the 2-user case. To the extent that the output of a linear DS-CDMA filter can be approximated by a Gaussian random variable, a minimum-mean-square-error optimized linear filter approximates the minimum BER solution. Numerical and simulation results indicate that for realistic AWGN DS-CDMA systems with reasonably low signature cross-correlations the linear minimum BER filter and the MMSE filter exhibit approximately the same performance. The linear minimum BER receiver is superior, however, when either the signature cross-correlation is high or the channel noise is non-Gaussian.

Minimum bit-error-rate decision fusion receivers for robust DS spread-spectrum communications

S. N. Batalama and D. A. Pados and I. N. Psaromiligkos

* in Proceedings of *
*IEEE Military Communications Conference (MILCOM)*,
vol. 3,
pp. 959-963 vol.3,
Oct 1996
.

The conventional matched filter (MF) correlation receiver for direct sequence spread spectrum transmissions is reconsidered from the theoretical point of view of robust distributed detection (decision fusion). Separate chip-by-chip decisions on the transmitted information bit of interest are generated. The individual decisions are then optimally fused to produce the final decision on the transmitted bit. It is proven that the majority-vote strategy is the statistically optimal fusion rule for additive white Gaussian noise channels. An exact closed form expression is given for the probability of error induced by the majority-vote receiver. It is shown that the performance is independent of the identity of the active interfering user population, that is signatures and signature cross-correlations, in DS-CDMA environments. This perfect robustness with respect to the identity of the multiuser interference component is coupled by significant resistance toward occasional high-power jamming (equivalently occasional natural severe channel imperfections) or outlier-prone background noise, or both. Some numerical studies illustrate these theoretical findings