Paper Info
Title
An Improved Mixed Gibbs Sampling Algorithm Based On Multiple Random Parallel Markov Chains For Massive Mimo Systems
Abstract
Massive MIMO can significantly improve diversity/multiplexing gain, channel capacity and spectrum efficiency. An efficient signal detection algorithm plays a very important role for system performance. However, the BER, complexity and delay performance of present signal detection algorithms are far from optimal with the increasing number of antennas. This paper presents an improved mixed Gibbs sampling algorithm based on multiple random parallel markov chains (MGS-MRPMC). It has lower complexity than traditional signal detection algorithms in massive MIMO systems. The simulation shows that the proposed algorithm can alleviate the stalling problem encountered in the mixed Gibbs sampling (MGS) algorithm with higher order QAM. Moreover, it can reduce the serious delay of the mixed Gibbs sampling with multiple restarts (MGS-MR) algorithm. The use of parallel strategy makes it suitable for hardware implementation in practice.
Year
Venue
Keywords
2016
2016 IEEE 27TH ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR, AND MOBILE RADIO COMMUNICATIONS (PIMRC)
Massive MIMO, signal detection, mixed Gibbs sampling, parallel Markov Chains, stalling problem
Field
DocType
Citations 
Mathematical optimization,Detection theory,Computer science,QAM,Markov chain,MIMO,Algorithm,Spectral efficiency,Multiplexing,Channel capacity,Gibbs sampling
Conference
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Cheng Gao1128.29
Jin Xu23710.47
Xiaofeng Tao31033140.26
Zhiheng Qin401.01