Iterative processing is widely adopted nowadays in modern wireless receivers for the decoding of advanced channel codes. The extension of this principle with an additional iterative feedback loop to the equalizer and to the demapper blocks has proven to provide excellent error rate performances in severe channel conditions (erasure, multipath, real fading channel models). However, the adoption of iterative processing with turbo decoding is constrained by the additional implied implementation complexity, heavily impacting throughput, latency and power consumption. In order to handle these issues and to enable the wide adoption of iterative processing, new system-level optimization techniques have to be investigated. In this work, we have studied the convergence speed and the system-level complexity of advanced receivers combining multiple iterative processes. The first part of this thesis was focusing on the study of combining iterative demodulation with turbo decoding, while the second part has extended this study to iterative MIMO receivers combining turbo equalization, turbo demodulation and turbo decoding. Various communication techniques and system parameters, as speci¿ed in emerging wireless communication applications, have been considered. Novel iteration schedulings of inner and outer feedback loops have been proposed to improve the convergence and reduce the overall complexity in terms of arithmetic operations and memory accesses. Furthermore, the conducted analysis and the proposed schedulings demonstrate the effectiveness of the outer feedback loops, even in terms of complexity, compared to the feed forward classical receivers. These results allowed the proposal of original complexity adaptive iterative receivers performing turbo decoding, turbo demodulation and turbo equalization.
Iterative receivers: scheduling, convergence speed and complexity