Information theory and coding for spread spectrum communication systems

The present work is based on the thesis that an information theoretic perspective can prove useful in understanding spread spectrum communication systems. In addressing physical layer and signal processing issues for wireless links, we use information theory as a conceptual tool to provide design guidelines and to dictate fundamental performance limits.;Wireless communication systems often use spread spectrum modulation to provide multiple access, e.g., code division multiple access (CDMA), in which users transmit using distinct spreading sequences that can be exploited by a multiuser detector at the receiver. While the unconstrained Gaussian multiple access channel is well understood, a complete information theory of multiuser spread spectrum systems is missing. Several fundamental questions remain: (i) Although direct sequence spreading can never increase capacity, does it necessarily entail a substantial loss in capacity? (ii) Since both direct sequence spreading and channel coding result in bandwidth expansion, how should one allocate a fixed bandwidth expansion between coding and spreading to allow for a high spectral efficiency, low complexity receiver?;This dissertation addresses the above questions and also presents results on multiuser detection for CDMA and performance analysis of convolutional codes. All results concerning CDMA assume symbol-synchronous users and binary spreading sequences. First, we study the performance of the linear parallel interference cancellation (LPIC) multiuser detector and find conditions under which it performs poorly. Second, we study the capacity loss due to spreading in a multiuser CDMA system with optimum joint multiuser decoding and conclude that channel coding can help to mitigate this loss. Third, we study the effects of spreading in a CDMA system with linear multiuser detection and single-user decoding by evaluating the coding-spreading tradeoff for the linear minimum mean-squared error (LMMSE) and the matched filter (MF) detectors. We find that, in a multicell system using convolutional codes and soft decision decoding, the gap between the peak spectral efficiencies of the LMMSE and the MF is small. Finally, we present a novel technique to compute the weight distribution function of a convolutional code, which uses a generalized Viterbi algorithm in conjunction with the Berlekamp-Massey algorithm.