Iterative QR-Based SFSIC Detection and Decoder Algorithm for a Reed–Muller Space-Time Turbo System

• Published in: Entropy (Basel, Switzerland) Vol. 19; no. 8; p. 426
• Main Authors: Ni, Liang-Fang, Wang, Yi, Li, Wei-Xia, Wang, Pei-Zhen, Zhang, Jia-Yan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2017
• Subjects: Algorithms; Codes; Coding; Computer simulation; Decoding; Detectors; Feedback; Interference; iterative detection and decoder; Likelihood ratio; Multiplexing; QR decomposition; Reed–Muller space time turbo system; reliability metric; soft feedback segment interference cancellation; Spacetime; Symbols; trellis-based maximum a posteriori probability decoder
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e19080426

An iterative QR-based soft feedback segment interference cancellation (QRSFSIC) detection and decoder algorithm for a Reed-Muller (RM) space-time turbo system is proposed in this paper. It forms the sufficient statistic for the minimum-mean-square error (MMSE) estimate according to QR decomposition-based soft feedback successive interference cancellation, stemmed from the a priori log-likelihood ratio (LLR) of encoded bits. Then, the signal originating from the symbols of the reliable segment, the symbol reliability metric, in terms of an a posteriori LLR of encoded bits which is larger than a certain threshold, is iteratively cancelled with the QRSFSIC in order to further obtain the residual signal for evaluating the symbols in the unreliable segment. This is done until the unreliable segment is empty, resulting in the extrinsic information for a RM turbo-coded bit with the greatest likelihood. Bridged by de-multiplexing and multiplexing, an iterative QRSFSIC detection is concatenated with an iterative trellis-based maximum a posteriori probability RM turbo decoder as if a principal Turbo detection and decoder is embedded with an iterative subordinate QRSFSIC detection and a RM turbo decoder, exchanging each other’s detection and decoding soft-decision information iteratively. These three stages let the proposed algorithm approach the upper bound of the diversity. The simulation results also show that the proposed scheme outperforms the other suboptimum detectors considered in this paper.