Abstract
We design a coded massive multiple-input multiple-output (MIMO) system using low-density parity-check (LDPC) codes and iterative joint detection and decoding (JDD) algorithm employing a low complexity detection. We introduce the factor graph representation of the LDPC coded massive MIMO system, based on which the message updating rule in the JDD is defined. We devise a tool for analyzing extrinsic information transfer (EXIT) characteristics of messages flowing in the JDD and the three-dimensional (3-D) EXIT chart provides a visualization of the JDD behavior. Based on the proposed 3-D EXIT analysis, we design jointly the degree distribution of irregular LDPC codes and the JDD strategy for the coded massive MIMO system. The JDD strategy was determined to achieve a higher error correction capability with a given amount of computational complexity. It was observed that the coded massive MIMO system equipped with the proposed LDPC codes and the proposed JDD strategy has lower bit error rate than conventional LDPC coded massive MIMO systems.
Highlights
The massive multiple-input multiple-output (MIMO) system, whose transmitter and receiver are equipped with tens to hundreds of antennas, has recently attracted many researchers and engineers because it can vastly improve the transmission data rate and spectral efficiency [1,2,3,4,5,6,7]
We considered low-density parity-check (LDPC) coded massive MIMO systems over 16 × 16, 64 × 64 and 256 × 256 channels with code rates of R = 0.5 and 0.75
We defined a factor graph representation of the LDPC coded massive MIMO system and defined updating rules for messages flowing in the joint detection and decoding (JDD) process
Summary
The massive multiple-input multiple-output (MIMO) system, whose transmitter and receiver are equipped with tens to hundreds of antennas, has recently attracted many researchers and engineers because it can vastly improve the transmission data rate and spectral efficiency [1,2,3,4,5,6,7]. As an approach to reduce the detection complexity, suboptimal linear detection algorithms have been intensively studied [7,16,17,18,19,20,21,22,23,24,25,26], where matched filter (MF) detection, zero forcing (ZF) detection and minimum mean squared error (MMSE) detection are well known examples These linear detection schemes cannot lower the computational complexity of the massive MIMO receiver to an acceptable level because the inversion of high dimensional matrices is still required. Tree-searching soft-input soft-output (SISO) MIMO detection algorithms have been proposed in various forms [23,24,25,26]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
