Low-complexity iterative algorithms for near-optimal detection in like-signal interference

Reliable communication of digital information is complicated in the presence of like-signal interference, that is, interference caused by the unintentional overlap of multiple information-bearing signals at the receiver. Over the past years, two forms of like-signal interference, namely intersymbol interference (ISI) and multiple-access interference (MAI), have lead to an array of ISI equalization and multiuser detection techniques aimed at combating interference. While these areas traditionally have dealt with detection of uncoded data, most modern-day communication systems use some form of error-correction coding. In recent years, introduction of Turbo codes and iterative decoding algorithms, have inspired a joint detection and decoding approach in which soft information is iteratively exchanged between soft-input soft-output (SISO) detection and decoding modules of the receiver.; In this work, complexity reduction techniques are examined for iterative detection of coded data in the presence of ISI and MAI. State reduction and soft interference cancellation (SIC), two methods of complexity reduction in conventional detectors, are investigated in the context of SISO detection. For whitened ISI channels, both techniques are combined to improve the performance of previously proposed reduced-state SISO (RS-SISO) algorithms under various channel conditions. For unwhitened ISI channels, a bias compensated RS-SISO algorithm is proposed for cancellation of anti-causal interference.; A novel reduced-complexity algorithm is proposed for SISO multiuser detection, where multiple detectors are operated in parallel, each executing the Verdu algorithm on a subset of active users. SIC is employed by each detector to remove the interference due to unmodeled users. Soft output information generated by the parallel detectors are combined for each user and then exchanged with a bank of SISO decoders. Simulation results are presented for comparison with other algorithms in the literature. The performance of iterative receivers with APP and LMMSE based multiuser detectors are analyzed using the density evolution analysis technique.