Robust spatio-temporal partial-response signaling over a frequency-selective fading MIMO channel with imperfect CSI

Partial-response signaling is known to facilitate the equalizer design because a controlled amount of residual interference is permitted. The design of the target impulse response of the partial-response precoder often assumes perfect channel state information, which is unfortunately not available at the transmitter in most practical applications. Consequently, this contribution focuses instead on the robust and joint design of a spatio-temporal target impulse response and the equalization coefficients for a frequency-selective fading multiple-input multiple-output communication channel based on current and/or previous noisy channel estimates. More precisely, the error in the channel estimates is statistically modeled, and robustness is achieved by minimizing the mean-squared estimation error averaged over the joint distribution of the actual channel and the available channel estimates. Numerical results of the bit error rate confirm that the proposed robust partial-response signaling not only provides a significant performance gain compared to traditional full-response signaling, but also outperforms the naive approach, which ignores channel estimation errors

Robust Beamforming for Cognitive and Cooperative Wireless Networks

Ph.DDOCTOR OF PHILOSOPH

Multi-user spatial diversity techniques for wireless communication systems

Multiple antennas at the transmitter and receiver, formally known as multiple-input multiple-output (MIMO) systems have the potential to either increase the data rates through spatial multiplexing or enhance the quality of services through exploitation of diversity. In this thesis, the problem of downlink spatial multiplexing, where a base station (BS) serves multiple users simultaneously in the same frequency band is addressed. Spatial multiplexing techniques have the potential to make huge saving in the bandwidth utilization. We propose spatial diversity techniques with and without the assumption of perfect channel state information (CSI) at the transmitter. We start with proposing improvement to signal-to-leakage ratio (SLR) maximization based spatial multiplexing techniques for both fiat fading and frequency selective channels. [Continues.

Future cellular systems: fundamentals and the role of large antenna arrays

In this thesis, we analyze the performance of three promising technologies being considered for future fifth generation (5G) and beyond wireless communication systems, with primary goals to: i) render 10-100 times higher user data rate, ii) serve 10-100 times more users simultaneously, iii) 1000 times more data volume per unit area, iv) improve energy efficiency on the order of 100 times, and iv) provide higher bandwidths. Accordingly, we focus on massive multiple-input multiple-output (MIMO) systems and other future wireless technologies, namely millimeter wave (mmWave) and full-duplex (FD) systems that are being considered to fulfill the above requirements. We begin by focusing on fundamental performance limits of massive MIMO systems under practical constraints such as low complexity processing, array size and limited physical space. First, we analyze the performance of a massive MIMO base station (BS) serving spatially distributed multi-antenna users within a fixed coverage area. Stochastic geometry is used to characterize the spatially distributed users while large dimensional random matrix theory is used to achieve deterministic approximations of the sum rate of the system. We then examine the deployment of a massive MIMO BS and the resulting energy efficiency (EE) by considering a more realistic set-up of a rectangular array with increasing antenna elements within a fixed physical space. The effects of mutual coupling and correlation among the BS antennas are incorporated by deriving a practical mutual coupling matrix which considers coupling among all antenna elements within the BS. Accordingly, the optimum number of antennas that can be deployed for a particular antenna spacing when EE is considered as a design criteria is derived. Also, it is found that mutual coupling effect reduces the EE of the massive system by around 40-45% depending on the precoder/receiver used and the physical space available for antenna deployment. After establishing the constraints of antenna spacing on massive MIMO systems for the current microwave spectrum, we shift our focus to mmWave frequencies (more than 100GHz available bandwidth), where the wavelength is very small and as a result more antennas can be rigged within a constrained space. Accordingly, we integrate the massive MIMO technology with mmWave networks. In particular, we analyze the performance of a mmWave network consisting of spatially distributed BS equipped with very large uniform circular arrays (UCA) serving spatially distributed users within a fixed coverage area. The use of UCA is due to its capability of scanning through both the azimuth as well as elevation dimensions. We show that using such 3D massive MIMO techniques in mmWave systems yield significant performance gains. Further, we show the effect of blockages and path loss on mmWave networks. Since blockages are found to be quite detrimental to mmWave networks, we create alternative propagation paths with the aid of relays. In particular, we consider the deployment of relays in outdoor mmWave networks and then derive expressions for the coverage probability and transmission capacity from sources to a destination for such relay aided mmWave networks using stochastic geometric tools. Overall, relay aided mmWave transmission is seen to improve the signal to noise ratio at the destination by around 5-10dB with respect to specific coverage probabilities. Finally, due to the fact that the current half duplex (HD) mode transmission only utilizes half the spectrum at the same time in the same frequency, we consider a multiuser MIMO cellular system, where a FD BS serves multiple HD users simultaneously. However, since FD systems are plagued by severe self-interference (SI), we focus on the design of robust transceivers, which can cancel the residual SI left after antenna and analog cancellations. In particular, we address the sum mean-squared-errors (MSE) minimization problem by transforming it into an equivalent semidefinite programming (SDP) problem. We propose iterative alternating algorithms to design the transceiver matrices jointly and accordingly show the gains of FD over HD systems. We show that with proper SI cancellation, it is possible to achieve gains on sum rate of up to 70-80% over HD systems

Multiuser Downlink Beamforming Techniques for Cognitive Radio Networks

Spectrum expansion and a significant network densification are key elements in meeting the ever increasing demands in data rates and traffic loads of future communication systems. In this context, cognitive radio (CR) techniques, which sense and opportunistically use spectrum resources, as well as beamforming methods, which increase spectral efficiency by exploiting spatial dimensions, are particularly promising. Thus, the scope of this thesis is to propose efficient downlink (DL) beamforming and power allocation schemes, in a CR framework. The methods developed here, can be further applied to various practical scenarios such as hierarchical multi-tier, heterogenous or dense networks. In this work, the particular CR underlay paradigm is considered, according to which, secondary users (SUs) opportunistically use the spectrum held by primary users (PUs), without disturbing the operation of the latter. Developing beamforming algorithms, in this scenario, requires that channel state information (CSI) from both SUs and PUs is required at the BS. Since in CR networks PUs have typically limited or no cooperation with the SUs, we particularly focus on designing beamforming schemes based on statistical CSI, which can be obtained with limited or no feedback. To further meet the energy efficiency requirements, the proposed beamforming designs aim to minimize the transmitted power at the BS, which serves SUs at their desired Quality-of-Service (QoS), in form of Signal-to-interference-plus-noise (SINR), while respecting the interference requirements of the primary network. In the first stage, this problem is considered under the assumption of perfect CSI of both SUs and PUs. The difficulty of this problem consists on one hand, in its non-convexity and, on the other hand, in the fact that the beamformers are coupled in all constraints. State-of-the-art approaches are based on convex approximations, given by semidefinite relaxation (SDR) methods, and suffer from large computational complexity per iteration, as well as the drawback that optimal beamformers cannot always be retrieved from the obtained solutions. The approach, proposed in this thesis, aims to overcome these limitations by exploiting the structure of the problem. We show that the original downlink problem can be equivalently represented in a so called ’virtual’ uplink domain (VUL), where the beamformers and powers are allocated, such that uplink SINR constraints of the SUs are satisfied, while both SUs and PUs transmit to the BS. The resulting VUL problem has a simpler structure than the original formulation, as the beamformers are decoupled in the SINR constraints. This allows us to develop algorithms, which solve the original problem, with significantly less computational complexity than the state-of-the-art methods. The rigurous analysis of the Lagrange duality, performed next, exposes scenarios, in which the equivalence between VUL and DL problems can be theroretically proven and shows the relation between the obtained powers in the VUL domain and the optimal Lagrange multipliers, corresponding to the original problem. We further use the duality results and the intuition of the VUL reformulation, in the extended problem of joint admission control and beamforming. The aim of this is to find a maximal set of SUs, which can be jointly served, as well as the corresponding beamforming and power allocation. Our approach uses Lagrange duality, to detect infeasible cases and the intuition of the VUL reformulation to decide upon the users, which have the largest contribution to the infeasibiity of the problem. With these elements, we construct a deflation based algorithm for the joint beamforming and admission control problem, which benefits from low complexity, yet close to optimal perfomance. To make the method also suitable for dense networks, with a large number of SUs and PUs, a cluster aided approach is further proposed and consists in grouping users, based on their long term spatial signatures. The information in the clusters serves as an initial indication of the SUs which cannot be simultaneously served and the PUs which pose similar interference constraints to the BS. Thus, the cluster information can be used to significantly reduce the dimension of the problem in scenarios with large number of SUs and PUs, and this fact is further validated by extensive simulations. In the second part of this thesis, the practical case of imperfect covariance based CSI, available at the transmitter, is considered. To account for the uncertainty in the channel knowledge, a worst case approach is taken, in which the SINR and the interference constraints are considered for all CSI mismatches in a predefined set One important factor, which influences the performance of the worst case beamforming approach is a proper choice of the the defined uncertainty set, to accurately model the possible uncertainties in the CSI. In this thesis, we show that recently derived Riemannian distances are better suited to measure the mismatches in the statistical CSI than the commonly used Frobenius norms, as they better capture the properties of the covariance matrices, than the latter. Therefore, we formulate a novel worst case robust beamforming problem, in which the uncertainty set is bounded based on these measures and for this, we derive a convex approximation, to which a solution can be efficiently found in polynomial time. Theoretical and numerical results confirm the significantly better performance of our proposed methods, as compared to the state-of-the-art methods, in which Frobenius norms are used to bound the mismatches. The consistently better results of the designs utilizing Riemannian distances also manifest in scenarios with large number of users, where admission control techniques must supplement the beamforming design with imperfect CSI. Both benchmark methods as well as low complexity techniques, developed in this thesis to solve this problem, show that designs based on Riemannian distance outperform their competitors, in both required transmit power as well as number of users, which can be simultaneously served

Transmit precoding and Bayesian detection for cognitive radio networks with limited channel state information

Field of study: Electrical & computer engineering.Dr. Dominic K. C. Ho, Dissertation Supervisor.Includes vita."May 2017."Cognitive radio (CR) represents a recent direction for enabling coexistence among heterogeneous networks. It can be a potential solution for the problem of scarce spectrum available for wireless communication systems. The study here investigates the underlay and interweave paradigms for the coexistence of CR network of secondary users (SUs) with a primary network of primary users (PUs). Under underlay mode, both networks communicates concurrently using the same resources. With interweave, SU is able to communicate as long as (some) PUs are not active. Usually, underlay or interweave employs multiple antennas at SU to use the spectral resources better and manage the interference towards the primary network. Performance of the CR network under either paradigm depends largely on the amount and quality of channel state information (CSI) available about the different communication links. In practical systems, often CSI at SU has uncertainty since it is deviated from the true one or is not known at all. This uncertainty should be accounted when designing the precoding schemes for SU or otherwise the interference impact on primary networks would violate the quality of service (QoS) requirements for PUs. This dissertation considers two cases regarding to the availability of CSI, the first one is when CSI is imperfect and the second is when CSI is completely not known. For the underlay mode, we investigate two manifolds. The first one addresses the problem of maximizing the throughput of a multiple-input multiple-output (MIMO) SU when CSI of the interference link to PU is completely unknown or partially known. We study the achievable rates for SU under two different QoS requirements for the PU: the conventional interference temperature and leakage rate metrics. When CSI is unavailable, we develop an iterative adaptation algorithm that satisfies the QoS constraint through exploiting the side-information in the primary communication network. When CSI is inaccurate, we model the uncertainty deterministically such that the uncertainty error belongs to a convex compact set defined by the Schatten norm. We design the precoder by following the worst case formulation. We further investigate the relation between the unknown and the inaccurate CSI cases when using the interference temperature metric, and reveal that the performance of the latter is not necessarily better than the former. The second manifold assumes there is uncertainty in the SU intended link for communication as well as in the interference link from SU to PU. Similar to the first manifold, we follow the deterministic modelling using Schatten norm for the uncertainty and apply the worst case philosophy. For a given precoder matrix, we find the worst uncertainty error in the set that describes the uncertainty in each link. We further develop an iterative numerical algorithm for the precoder. Simpler solutions for the precoder and the uncertainty errors are derived under some special instances of the Schatten norm and certain requirement of transmission power. For the interweave mode, we assume there is no CSI available at SU and derive a Bayesian detector for the proposed binary hypothesis problem. For the null or noise model, we propose a conjugate prior for the unknown spatial covariance matrix. For the alternative or data model, we propose a new class of improper priors for the covariance matrix. We introduce the fractional Bayes factor (FBF) approach to enhance the detection capability of the Bayes factor. The developed FBF is compared with those using the conjugate priors for both hypotheses and generalized likelihood ratio test (GLRT), and it yields significant improvement.Includes bibliographical references (pages 126-142)