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Reduced Search Iterative Detection and Decoding: Issues and Solutions

Saleem Ahmed1, Sooyoung Kim1, Hyung-Jick Ryu2 , and Won-Yong Kim2
1.Division of Electronic Eng., Chonbuk National University, Jeonju, Korea
2.Comesta, Inc., Daejeon, Korea 305-509
Abstract—In this paper, we evaluate a number of reduced search detection methods for iterative detection and decoding (IDD) system. We evaluate the basic issues which can restrict the performance of the reduced search detectors in IDD systems. One of the main issues in reduced search detectors is related to missing hypothesis sometimes known as log likelihood ratio (LLR) clipping problem. If a suitable method is not used for missing hypothesis, it can degrade the performance of the reduced search based IDD systems. The solution is provided which can overcome the problem of missing hypothesis. The simulation results show that the proposed method provides substantial performance gain in each MIMO detector iterations.

Index Terms—iterative detection and decoding, sphere decoding, log likelihood ratio

Cite: Saleem Ahmed, Sooyoung Kim, Hyung-Jick Ryu, and Won-Yong Kim, "Reduced Search Iterative Detection and Decoding: Issues and Solutions," Lecture Notes on Information Theory, Vol. 2, No. 2, pp. 129-133, June 2014. doi: 10.12720/lnit.2.2.129-133
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