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Layered graph-merged detection and decoding of non-binary LDPC coded massive MIMO systems
Conference paper

Layered graph-merged detection and decoding of non-binary LDPC coded massive MIMO systems

Shusen Jing, Junmei Yang, Huayi Zhou, Yeong-Luh Ueng, Xiaohu You and Chuan Zhang
Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017, Vol.2017-October, pp.1084-1088
04/2018

Abstract

belief propagation factor graph joint detection and decoding Massive MIMO detection NB-LDPC codes Control and Optimization Computer Networks and Communications Hardware and Architecture Signal Processing Biomedical Engineering Instrumentation
In order to improve the spectral efficiency and reliability of communication systems, both the multiple-input multiple-output (MIMO) technique and non-binary low-density parity-check (NB-LDPC) codes are considered as powerful tools to meet the requirements. In order to further optimize the MIMO NB-LDPC systems and adapt to massive MIMO scenarios, a new joint detection and decoding (JDD) method of massive MIMO with NB-LDPC codes, called layered graph-merged detection and decoding (GMDD), is proposed. In this paper, factor graphs associated to symbol-based belief propagation (BP) MIMO detection and NB-LDPC decoding are merged into a single graph. For massive MIMO condition, a MIMO detection graph is attached to several NB-LDPC decoding graphs, which makes the methods unique to existed ones. Compared with separated detection and decoding (SDD), for BER less than 5 × 10 -2 the proposed method can achieve nearly 7 dB gain with same or even fewer iterations. Corresponding hardware architecture and complexity analysis are also given in this paper.

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