Multiple antennas in wireless communications: Array signal processing and channel capacity

We investigate two aspects of multiple-antenna wireless communication systems in this thesis: (1) deployment of an adaptive beamformer array at the receiver; and (2) space-time coding for arrays at the transmitter and the receiver. In the first part of the thesis, we establish sufficient conditions for the convergence of a popular least mean squares (LMS) algorithm known as the sequential Partial Update LMS Algorithm for adaptive beamforming. Partial update LMS (PU-LMS) algorithms are reduced complexity versions of the full update LMS that update a subset of filter coefficients at each iteration. We introduce a new improved algorithm, called Stochastic PU-LMS, which selects the subsets at random at each iteration. We show that the new algorithm converges for a wider class of signals than the existing PU-LMS algorithms.; The second part of this thesis deals with the multiple-input multiple-output (MIMO) Shannon capacity of multiple antenna wireless communication systems under the average energy constraint on the input signal. Previous work on this problem has concentrated on capacity for Rayleigh fading channels. We investigate the more general case of Rician fading. We derive capacity expressions, optimum transmit signals as well as upper and lower bounds on capacity for three Rician fading models. In the first model the specular component is a dynamic isotropically distributed random process. In this case, the optimum transmit signal structure is the same as that for Rayleigh fading. In the second model the specular component is a static isotropically distributed random process unknown to the transmitter, but known to the receiver. In this case the transmitter has to design the transmit signal to guarantee a certain rate independent of the specular component. Here also, the optimum transmit signal structure, under the constant magnitude constraint, is the same as that for Rayleigh fading. In the third model the specular component is deterministic and known to both the transmitter and the receiver. In this case the optimum transmit signal and capacity both depend on the specular component. We show that for low signal to noise ratio (SNR) the specular component completely determines the signal structure whereas for high SNR the specular component has no effect. We also show that training is not effective at low SNR and give expressions for rate-optimal allocation of training versus communication.