This work deals with the design of MT transmit, MR receive antenna MIMO (Multiple Input Multiple Output) communication system where the transmitter performs a linear operation on data. This linear precoding model includes systems which involve signal shaping for achieving higher data rates, uncoded MIMO Multicarrier and Single-Carrier systems and, the more recent, MIMO-OFDM (Orthogonal Frequency Division Multiplexing) systems employing full diversity Space-Frequency codes. The objective of this work is to design diversity centric and rate centric linear precoded MIMO systems whose performance is better than the existing designs. In particular, we consider MIMO-OFDM systems, Zero Padded MIMO systems and MIMO systems with limited rate feedback. Design of full diversity MIMO-OFDM systems of rate symbol per channel use (1 s/ pcu) : In literature, MIMO-OFDM systems exploiting full diversity at a rate of 1 s/ pcu are based on a few specific Space-Frequency (SF)/ Space-Time-Frequency (STF) codes. In this work, we devise a general parameterized framework for the design of MIMO-OFDM systems employing full diversity STF codes of rate 1 s/ pcu. This framework unifies all existing designs and provides tools for the design of new systems with interesting properties and superior performance. Apart from rate and diversity, the parameters of the framework are designed for a low complexity receiver. The parameters of the framework usually depend on the channel characteristics (number of multipath, Delay Profile (DP)). When channel characteristics are available at the transmitter, a procedure to optimize the performance of STF codes is provided. The resulting codes are termed as DP optimized codes. Designs obtained using the optimization are illustrated and their performance is shown to be better than the existing ones. To cater to the scenarios where channel characteristics are not available at the transmitter, a complete characterization of a class of full diversity DP Independent (DPI) STF codes is provided. These codes exploit full diversity on channels with a given number of multipath irrespective of their characteristics. Design of DP optimized STF codes and DPI codes from the same framework highlights the flexibility of the framework. Design of Zero Padded (ZP) MIMO systems : While the MIMO-OFDM transmitter needs to precode data for exploiting channel induced multipath diversity, ZP MIMO systems with ML receivers are shown to exploit multipath diversity without any precoding. However, the receiver complexity of such systems is enormous and hence a study ZP MIMO system with linear receivers is undertaken. Central to this study involves devising low complexity receivers and deriving the diversity gain of linear receivers. Reduced complexity receiver implementations are presented for two classes of precoding schemes. An upper bound on the diversity gain of linear receivers is evaluated for certain precoding schemes. For uncoded systems operating on a channel of length L, this bound is shown to be MRL_MT +1 for uncoded transmissions, i.e, such systems tend to exploit receiver and multipath diversities. On the other hand, MIMO-OFDM systems designed earlier have to trade diversity with receiver complexity. These observations motivate us to use ZP MIMO systems with linear receivers for channels with large delay spread when receiver complexity is at a premium. Design examples highlighting the attractiveness of ZP systems when employed on channels with large delay spread are also presented. Efficient design of MIMO systems with limited feedback : Literature presents a number of works that consider the design of MIMO systems with partial feedback. The works that consider feedback of complete CSI, however, do not provide for an efficient system design. In this work, we consider two schemes, Correlation matrix feedback and Channel information feedback that convey complete CSI to the transmitter. This CSI is perturbed due to various impairments. A perturbation analysis is carried out to study the variations in mutual information for each of the proposed schemes. For ergodic channels, this analysis is used to design a MIMO system with a limited rate feedback. Using a codebook based approach, vector quantizers are designed to minimize the loss in ergodic capacity for each of the proposed schemes. The efficiency of the design stems from the ability to obtain closed-form expression for centroids during the iterative vector quantizer design. The performance of designed vector quantizers compare favorably with the existing designs. The vector quantizer design for channel information feedback is robust in the sense that the same codebook can be used across all operating SNR. Use of vector quantizers for improving the outage performance is also presented.

Parameter estimation and signal detection are the two key components of a wireless communication system. They directly impact the bit-error-ratio (BER) performance of the system. Several criteria have been successfully applied for parameter estimation and signal detection. They include maximum likelihood (ML), maximum a-posteriori probability (MAP), least square (LS) and minimum mean square error (MMSE) etc. In the linear detection framework, linear MMSE (LMMSE) and LS are the most popular ones. Nevertheless, these criteria do not necessarily minimize the BER, which is one of the key aspect of any communication receiver design. Thus, minimization of BER is tantamount to an important design criterion for a wireless receiver, the minimum bit/symbol error ratio (MBER/MSER). We term this design criterion as the minimum-error-probability (MEP). In this thesis, parameter estimation and signal detection have been extensively studied based on the MEP framework for various unexplored scenar-ios of a wireless communication system. Thus, this thesis has two broad categories of explorations, first parameter estimation and then signal detection. Traditionally, the MEP criterion has been well studied in the context of the discrete signal detection in the last one decade, albeit we explore this framework for the continuous parameter es-timation. We first use this framework for channel estimation in a frequency flat fading single-input single-output (SISO) system and then extend this framework to the carrier frequency offset (CFO) estimation of multi-user MIMO OFDM system. We observe a reasonably good SNR improvement to the tune of 1 to 2.5 dB at a fixed BER (tentatively at 10−3). In this context, it is extended to the scenario of multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) or MIMO-OFDM with pa-rameter estimation error statistics obtained from LMMSE only and checked its effect at the equalizer design using MEP and LMMSE criteria. In the second exploration of the MEP criterion, it is explored for signal detection in the context of MIMO-relay and MIMO systems. Various low complexity solutions are proposed to alleviate the effect of high computational complexity for the MIMO-relay. We also consider various configurations of relay like cognitive, parallel and multi-hop relaying. We also propose a data trans-mission scheme with a rate of 1/Ns (Ns is the number of antennas at the transmitter) with the help of the MEP criterion to design various components. In all these cases, we obtain considerable BER improvement compared to the existing solutions.

Bayesian approaches for sparse signal recovery have enjoyed a long-standing history in signal processing and machine learning literature. Among the Bayesian techniques, the expectation maximization based Sparse Bayesian Learning(SBL) approach is an iterative procedure with global convergence guarantee to a local optimum, which uses a parameterized prior that encourages sparsity under an evidence maximization frame¬work. SBL has been successfully employed in a wide range of applications ranging from image processing to communications. In this thesis, we propose novel, efficient and low-complexity SBL-based algorithms that exploit structured sparsity in the presence of fully/partially known measurement matrices. We apply the proposed algorithms to the problem of channel estimation and data detection in Orthogonal Frequency Division Multiplexing(OFDM) systems. Further, we derive Cram´er Rao type lower Bounds(CRB) for the single and multiple measurement vector SBL problem of estimating compressible vectors and their prior distribution parameters. The main contributions of the thesis are as follows: We derive Hybrid, Bayesian and Marginalized Cram´er Rao lower bounds for the problem of estimating compressible vectors drawn from a Student-t prior distribution. We derive CRBs that encompass the deterministic or random nature of the unknown parameters of the prior distribution and the regression noise variance. We use the derived bounds to uncover the relationship between the compressibility and Mean Square Error(MSE) in the estimates. Through simulations, we demonstrate the dependence of the MSE performance of SBL based estimators on the compressibility of the vector. OFDM is a well-known multi-carrier modulation technique that provides high spectral efficiency and resilience to multi-path distortion of the wireless channel It is well-known that the impulse response of a wideband wireless channel is approximately sparse, in the sense that it has a small number of significant components relative to the channel delay spread. In this thesis, we consider the estimation of the unknown channel coefficients and its support in SISO-OFDM systems using a SBL framework. We propose novel pilot-only and joint channel estimation and data detection algorithms in block-fading and time-varying scenarios. In the latter case, we use a first order auto-regressive model for the time-variations, and propose recursive, low-complexity Kalman filtering based algorithms for channel estimation. Monte Carlo simulations illustrate the efficacy of the proposed techniques in terms of the MSE and coded bit error rate performance. • Multiple Input Multiple Output(MIMO) combined with OFDM harnesses the inherent advantages of OFDM along with the diversity and multiplexing advantages of a MIMO system. The impulse response of wireless channels between the Nt transmit and Nr receive antennas of a MIMO-OFDM system are group approximately sparse(ga-sparse),i.e. ,the Nt Nr channels have a small number of significant paths relative to the channel delay spread, and the time-lags of the significant paths between transmit and receive antenna pairs coincide. Often, wire¬less channels are also group approximately-cluster sparse(ga-csparse),i.e.,every ga-sparse channel consists of clusters, where a few clusters have all strong components while most clusters have all weak components. In this thesis, we cast the problem of estimating the ga-sparse and ga-csparse block-fading and time-varying channels using a multiple measurement SBL framework. We propose a bouquet of novel algorithms for MIMO-OFDM systems that generalize the algorithms proposed in the context of SISO-OFDM systems. The efficacy of the proposed techniques are demonstrated in terms of MSE and coded bit error rate performance.