Improved decoder metrics for DS-CDMA in practical 3G systems

While 4G mobile networks have been deployed since 2008. In several of the more developed markets, 3G mobile networks are still growing with 3G having the largest market -in terms of number of users- by 2019. 3G networks are based on Direct- Sequence Code-Division Multiple-Access (DS-CDMA). DS-CDMA suffers mainly from the Multiple Access Interference (MAI) and fading. Multi-User Detectors (MUDs) and Error Correcting Codes (ECCs) are the primary means to combat MAI and fading. MUDs, however, suffer from high complexity, including most of sub-optimal algorithms. Hence, most commercial implementations still use conventional single-user matched filter detectors. This thesis proposes improved channel decoder metrics for enhancing uplink performance in 3G systems. The basic idea is to model the MAI as conditionally Gaussian, instead of Gaussian, conditioned on the users’ cross-correlations and/or the channel fading coefficients. The conditioning implies a time-dependent variance that provides enhanced reliability estimates at the decoder inputs. We derive improved log-likelihood ratios (ILLRs) for bit- and chip- asynchronous multipath fading channels. We show that while utilizing knowledge of all users’ code sequences for the ILLR metric is very complicated in chip-asynchronous reception, a simplified expression relying on truncated group delay results in negligible performance loss. We also derive an expression for the error probability using the standard Gaussian approximation for asynchronous channels for the widely used raised cosine pule shaping. Our study framework considers practical 3G systems, with finite interleaving, correlated multipath fading channel models, practical pulse shaping, and system parameters obtained from CDMA2000 standard. Our results show that for the fully practical cellular uplink channel, the performance advantage due to ILLRs is significant and approaches 3 dB