Reduced-Complexity Soft MIMO Detection Based on Causal and Noncausal Decision Feedback

Abstract

We present a reduced-complexity soft detection (RCSD) scheme geared to multiple-input multiple-output (MIMO) systems with spatial domain multiplexing leading to layered space-time or space-frequency architecture. The proposed algorithm relies on a trellis representation of the MIMO signals and a subsequent formulation of constrained-depth maximum a posterior (MAP) detection in conjunction with soft decision feedback (SDF). Decision feedback is broken into causal and noncausal parts in an effort to maximize the observation window while maintaining a reasonable computational load. Two variations of RCSD are proposed to utilize the noncausal information in different ways. Analysis based on mean squared error (MSE) and log-likelihood ratio (LLR) associated with symbols in different stages of the trellis is done to develop insight into error propagation in the RCSD algorithm, as well as to compare the quality of the soft information obtained by the two RCSD variants. Error-rate simulations are conducted in the context of turbo-like iterative demapping and decoding (IDD). The resulting performance and required complexity are compared with those of maximum a posterior (MAP) detection, soft sphere detection (SD), as well as the Turbo-BLAST processing scheme. We observe favorable performance/complexity tradeoffs with the proposed soft detection scheme for a number of modulation/channel scenarios.