Mathini Sellathurai

Turbo-MIMO for wireless communications

This article reviews an important class of MIMO wireless communications, known collectively as turbo-MIMO systems. A distinctive property of turbo-MIMO wireless communication systems is that they can attain a channel capacity close to the Shannon limit and do so in a computationally manageable manner. The article focuses attention on a subclass of turbo-MIMO systems that use space-time coding based on bit-inter-leaved coded modulation. Different computationally manageable decoding (detection) strategies are briefly discussed. The article also includes computer experiments that are intended to improve the understanding of specific issues involved in the design of turbo-MIMO systems.

Space-time coding in mobile Satellite communications using dual-polarized channels

The use of dual-orthogonal polarization (horizontal/vertical or circular right-hand/left-hand polarizations) can increase the rate of transmission of satellite communication systems by a factor of two. However, the cross polar discriminations (XPDs) of the satellite and earth station antennas may be large enough to severely interfere between the two polarizations. In this paper, we investigate the use of space-time coding techniques in satellite-land mobile systems using dual-polarized transmit and receive antennas. In particular, we show that we can achieve significant gains by using layered space-time coding concepts and iterative detection and decoding receivers in communications systems employing polarization diversity channels in the presence of line-of-sight components.

Computationally Efficient Vector Perturbation Precoding Using Thresholded Optimization

We propose a low-complexity vector perturbation (VP) precoding scheme for the downlink of multi-user multiple input multiple output (MU-MIMO) systems. While conventional VP performs a computationally intensive sphere search through multiple candidate perturbation vectors to minimize the norm of the precoded signal, the proposed precoder applies a threshold to the desired norm to reduce the number of search nodes visited by the sphere encoder. This threshold is determined by the performance requirements of the mobile users. Once the threshold is met, the search for the perturbation vectors finishes thus saving significant computational burden at the transmitter. To evaluate the advantages of the proposed technique compared to VP, we further derive the computational complexity in terms of the volume of the associated search space and the resulting numerical operations. In addition, we use a new performance-complexity metric to study the relevant tradeoff and look at the power efficiency of the system, both of which metrics can be used to optimize the user-determined threshold accordingly\color{black}. The presented analysis and results show that the proposed thresholded VP (TVP) offers a favorable tradeoff between performance and complexity where significant complexity reduction is attained while the user threshold performance is guaranteed.

Reweighted Nuclear Norm Approach for Interference Alignment

Managing uncoordinated interference becomes a substantial problem for heterogeneous networks, since the unplanned interferences from the femtos cannot be coordinately aligned with that from the macro/pico base stations (BSs). Due to the uncoordinated interference, perfect interference alignment (IA) may be not attained. In order to achieve linear capacity scaling by IA, we follow the rank-constrained rank minimization (RCRM) framework which minimizes the rank of the interference subspace with full rank constraint on the direct signal space. Considering that the sum of log function can obtain low-rank solutions to linear matrix inequality (LMI) problems for positive semidefinite matrices, we introduce sum of log function as an approximation surrogate of the rank function. To minimize the concave function, we implement a Majorization-Minimization (MM) algorithm and develop a reweighted nuclear norm minimization algorithm with a weight matrix introduced. Moreover, considering the practical available signal-to-noise ratio (SNR), a mixed approach is developed to further improve the achievable sum rate in low-to-moderate SNR region. Simulation results show that the proposed algorithm considerably improves the sum rate performance and achieves the highest multiplexing gain than the recently developed IA approaches for various interference channels.

Vector Perturbation Based on Symbol Scaling for Limited Feedback MISO Downlinks

We propose a low-complexity vector precoding (VP) scheme for the downlink of multi-user multiple input single output (MU-MISO) systems with limited feedback. Conventional VP requires the use of modulo operation and knowledge of the scaling factor used at the transmitter in order to remove the perturbation quantity at the receiver. The latter may be problematic in certain limited feedback scenarios where only quantized versions of the scaling factor can be made available at the receiver. To circumvent this shortcoming, we propose a modified VP technique where the search of perturbing vectors is limited to the area in the symbol constellation which is constrictive to the information symbols, i.e., the area where the distances from the decision thresholds are increased with respect to a distance threshold. By doing this, the perturbation quantities can only enhance detection and need not be removed at the receiver. Successful detection can therefore be done without the use of the modulo operation and the scaling factor, which makes the proposed VP scheme applicable to limited feedback scenarios. Analytical and simulation results show that the performance of the proposed scheme is free of the error floor typically encountered with conventional VP in the presence of limited feedback.

LPI Optimization Framework for Target Tracking in Radar Network Architectures Using Information-Theoretic Criteria

Widely distributed radar network architectures can provide significant performance improvement for target detection and localization. For a fixed radar network, the achievable target detection performance may go beyond a predetermined threshold with full transmitted power allocation, which is extremely vulnerable in modern electronic warfare. In this paper, we study the problem of low probability of intercept (LPI) design for radar network and propose two novel LPI optimization schemes based on information-theoretic criteria. For a predefined threshold of target detection, Schleher intercept factor is minimized by optimizing transmission power allocation among netted radars in the network. Due to the lack of analytical closed-form expression for receiver operation characteristics (ROC), we employ two information-theoretic criteria, namely, Bhattacharyya distance and J-divergence as the metrics for target detection performance. The resulting nonconvex and nonlinear LPI optimization problems associated with different information-theoretic criteria are cast under a unified framework, and the nonlinear programming based genetic algorithm (NPGA) is used to tackle the optimization problems in the framework. Numerical simulations demonstrate that our proposed LPI strategies are effective in enhancing the LPI performance for radar network.

A Study on MVDR Beamforming Applied to an ESPAR Antenna

The adaptive beamforming algorithm-minimum variance distortionless response (MVDR) has been studied based on the electronically steerable parasitic array radiator (ESPAR) antenna. The ESPAR antenna uses a single radio-frequency (RF) front end, and its beamforming is achieved by adjusting reactance loads of parasitic elements coupled to the central active element. In the proposed beamforming method, the MVDR beamformer optimizes weights applied to outputs of beams. The optimization problem is formulated as a second-order-cone programming (SOCP) problem including a Euclidean distance metric to approximate the optimal equivalent weight vector to a feasible solution. Then the ESPAR beampattern design strategy iterates between the SOCP problem and a simple projection of reactance loads. The simulations show that the proposed MVDR beamforming method based on an ESPAR antenna gives a beam steering at the desired direction and placing nulls at the interfering directions, and it converges fast. However, when the desired source is close to the interferer, the output signal-to-interference-plus-noise ratio (SINR) degrades and where we use the interference-plus-noise sample covariance matrix to improve the beamforming performance.

Performance of Rayleigh-Product MIMO Channels with Linear Receivers

This paper presents an analytical investigation on the performance of Rayleigh-product MIMO channels with linear minimum mean-square-error (MMSE) or zero-forcing (ZF) receivers. For MMSE receivers, exact closed-form expressions for the ergodic sum-rate of the system are derived. In addition, simplified expressions are obtained for the key parameters dictating the sum-rate performance of the system in the high signal-to-noise ratio (SNR) regime (i.e., high SNR slope and power offset) and low SNR regime (i.e., minimum energy per information bit required to convey any positive rate and the wideband slope). While for ZF receivers, tight closed-form upper and lower bounds for the ergodic sum-rate of the system are derived. It is analytically proven that the ZF and MMSE receivers achieve the same sum rate performance in the high SNR regime. Moreover, for both MMSE and ZF receivers, the achievable diversity-multiplexing tradeoff (DMT) of Rayleigh-product MIMO channels is characterized. The findings suggest that a larger number of scatterers will improve the the performance of Rayleigh-product MIMO channels with linear receivers, and the ZF receivers achieve the same performance as the MMSE receivers in Rayleigh-product MIMO channels in the high SNR regime. Moreover, it is demonstrated that as long as the number of the scatterers is greater than the number of receive antennas, linear receivers achieve the optimal DMT.

Transmitter Subset Selection in FM-Based Passive Radar Networks for Joint Target Parameter Estimation

Passive radar network systems utilize multiple transmitters of opportunity and multichannel receivers to offer remarkable performance improvement due to the advantage of signal and spatial diversities. The frequency modulation (FM) commercial radio signals have become attractive for passive radar applications owing to their wide-spread availability and the favourable Doppler resolution. In this paper, two transmitter subset selection schemes, balancing the trade-off between target parameter estimation accuracy and infrastructure utilization, are proposed for FM-based passive radar networks. In the first, the subset size of selected transmitters employed in the estimation process is minimized by effectively selecting a subset of transmitters, such that the required target parameter estimation mean-square error (MSE) threshold is attained. In the second, an optimal subset of transmitters of a predetermined size κ is selected, such that the estimation MSE is minimized. These problems are formulated as a knapsack problem, where the coherent Cramér-Rao lower bound (CRLB) is used as a performance metric. Both transmitter subset selection schemes are tackled with greedy selection algorithms by successively selecting transmitters so as to minimize the performance gap between the CRLB and a predetermined MSE threshold or a predetermined subset size. Numerical simulations demonstrate that the problem of transmitter subset selection is not only a function of the transmitted waveforms but also of the relative geometry between the target and the passive radar network systems, which leads to reductions in both computational load and signal processing costs.

Analytical Derivation of Multiuser Diversity Gains with Opportunistic Spectrum Sharing in CR Systems

This paper investigates the multiuser diversity introduced by opportunistic user selection in cognitive radio (CR) networks, where multiple cognitive users request to access the spectral resources of the licensed (primary) user. We investigate a simple cognitive user selection strategy aiming at maximizing the received signal-to-interference-plus-noise ratio (SINR) for a given power budget, under interference constraints to the primary. We study the statistics of the SINR at the cognitive receiver, and derive exact analytical expressions of its probability density function (PDF). We then analytically calculate the diversity gains introduced in the system due to the selection of one cognitive user amongst multiple candidates compared to the case when only one cognitive user exists and no selection occurs. Furthermore, we utilize the PDF of the SINR to predict the bit error rate (BER) of the selected cognitive user. Finally, the asymptotic behavior of the diversity gains for the low transmit power region of the primary and cognitive links, and as the number of candidate links becomes large is also investigated. All three multiaccess scenarios are investigated, namely multiple access channel (MAC), broadcast channel (BC) and parallel access channel (PAC), and the results show that the analytically derived expressions closely match simulated performance.