Computationally efficient blind-adaptive algorithms for multi-antenna systems

Multi-input multi-output (MIMO) systems are expected to play a crucial role in future wireless communications and a significant increase of interest in all aspects of MIMO system design has been seen in the past decade. The primary interest of this thesis is in the receiver part of the MIMO system. In this area, continuous interest has been shown in developing blind-adaptive decoding algorithms. While blind decoding algorithms improve data throughput by enabling the system designer to replace training symbols with data, they also tend to perform robustly against any environment disturbances, compared to their training-based counterparts. On the other hand, considering the fact that the wireless end user environment is becoming increasingly mobile, adaptive algorithms have the ability to improve the performance of a system regardless of whether it is a blind system or a training-based one. The primary difficulty faced by blind and adaptive algorithms is that they generally are computationally intense. In this thesis, we develop semi-blind and blind decoding algorithms that are adaptive in nature as well as computationally efficient for multi-antenna systems. First, we consider the problem of channel tracking for MIMO communication systems where the MIMO channel is time-varying. We consider a class of MIMO systems where orthogonal space-time block codes (OSTBCs) are used as the underlying space-time coding schemes. For a general MIMO system with any number of transmitting and receiving antenna combinations, a two-step MIMO channel tracking algorithm is proposed. As the first step, Kalman filtering is used to obtain an initial channel estimate for the current block based on the channel estimates obtained for previous blocks. Then, in the second step, the so-obtained initial channel estimate is refined using a decision-directed iterative method. We show that, due to specific properties of orthogonal space-time block codes, both the Kalman filter and the decision-directed algorithm can be significantly simplified. Then, we extend the above receiver for MIMO-OFDM systems and propose a computationally efficient semi-blind receiver for MIMO systems in frequency-selective channels. Further, for the proposed receivers, we have derived theoretical performance analysis in terms of probability of error. Assuming the knowledge of the transmitted symbols for the first block, we have derived the instantaneous signal to interference and noise ratio (SINR) for consecutive transmission blocks in the absence of training, by exploiting Kalman filtering to track the channel in a decision-directed mode. Later, we extend the the theoretical performance limit comparisons for time-domain vs. frequency-domain channel tracking for MIMO-OFDM systems. Further, we study the advantage of adaptive channel tracking algorithms in comp-type pilot aided channel estimation schemes in practical MIMO-OFDM systems. After that, an efficient sequential Monte-Carlo (SMC) algorithm is developed for blind detection in MIMO systems where OSTBCs are used as the underlying space-time coding scheme. The proposed algorithm employs Rao-Blackwellization strategy to marginalize out the (unwanted) channel coefficients and uses optimal importance function to generate samples to propagate the posterior distribution. The algorithm is blind in the sense that, unlike the earlier ones, the transmission of training symbols is not required by this scheme. The marginalization involves the computation of (hundreds of) Kalman filters running in parallel resulting in intense computer requirement. We show that, the marginalization step can be significantly simplified for the specified problem under no additional assumptions – resulting in huge computational savings. Further, we extend this result to frequency selective channels and propose a novel and efficient SMC receiver for MIMO-OFDM systems. Finally, a novel adaptive algorithm is presented for directional MIMO systems.Specifically, the problem of direction of arrival (DOA) tracking of an unknown time-varying number of mobile sources is considered. The challenging part of the problem is the unknown, time-varying number of sources that demand a combination of source enumeration techniques and sequential state estimation methods to track the time-varying number of DOAs. In this thesis, we transform the problem into a novel state-space model, and, by employing probability hypothesis density (PHD) filtering technique, propose a simple algorithm that is able to track the number of sources as well as the corresponding directions of arrivals. In addition to the fact that the proposed algorithm is simple and easier to implement, simulation results show that, the PHD implementation yields superior performance over competing schemes in tracking rapidly varying number of targets.