Multiple coding and space-time multi-user detection in multiple antenna systems

We study transmit power adaption and capacity-approaching coding/decoding in multiple-input - multiple-output (MIMO) Rayleigh fading channels under the assumption that perfect channel state information is known at both the transmitter and the receiver. The capacity of MIMO systems with transmitter channel state information can be achieved via two schemes: a multiple coding scheme with temporal and spatial water filling and a single coding scheme with temporal and spatial water filling. The former requires an infinite number of different codes and the latter requires inter-block coding and therefore a very long code. We propose three different simple, but powerful, methods for transforming the MIMO fading channel into a set of additive white noise Gaussian channels, to which standard codes for the Gaussian channel can then be applied. We show through a number of examples that these methods can closely approach channel capacity. The code length of the proposed multiple-coding scheme can be much shorter since it can adapt its rate to fading conditions. Also the code length is determined solely by the length of codes chosen for the AWGN channel(s), not the fading dynamics.; We study the applications of space-time block codes in DS-CDMA system. We propose subspace-based blind decoders for the downlink which can blindly suppress the multiple access interferences from other users and exploit the advantages provided by multiple antennas simultaneously. Our schemes can be used for quite a few existing space-time block codes because we borrow the structure of linear dispersion codes. We also propose non-coherent blind decoders for the downlink of DS-CDMA system equipped with multiple antennas. Our non-coherent blind decoders can suppress the multiple access interferences and exploit diversity gain without knowing the channel state information either at the base station or at the mobile station. Our simulations results show that the proposed blind decoders significantly outperform the traditional method currently used.