Paper Info
Title
Stochastic MIMO Detector Based on the Markov Chain Monte Carlo Algorithm
Abstract
stochastic computing framework for a Markov Chain Monte Carlo (MCMC) multiple-input–multiple-output (MIMO) detector is proposed, in which the arithmetic operations are implemented by simple logic structures. Specifically, we introduce two new techniques, namely a sliding window generator (SWG) and a log-likelihood ratio based updating method (LUM), to achieve an efficient design. The SWG utilizes the variance in stochastic computations to increase the transition probability of the MCMC detector, while the LUM reduces the hardware cost. As a case study, we design a fully-parallel stochastic MCMC detector for a 4$\, \times $4 16-QAM MIMO system using 130 nm CMOS technology. The proposed detector achieves a throughput of 1.5 Gbps with only a 0.2 dB performance loss compared to a traditional floating-point detection method. Our design has a 30% better ratio of gate count to scaled throughput compared to other recent MIMO detectors.
Year
DOI
Venue
2014
10.1109/TSP.2014.2301131
IEEE Transactions on Signal Processing
Keywords
DocType
Volume
lum,bit rate 1.5 gbit/s,transition probability,log-likelihood ratio based updating method,mimo systems,monte carlo methods,mcmc,stochastic mimo detector,size 130 nm,swg,markov processes,cmos integrated circuits,stochastic computing framework,quadrature amplitude modulation,sliding window generator,floating-point detection method,multiple-input–multiple-output (mimo) detector,markov chain monte carlo (mcmc),markov chain monte carlo algorithm,cmos technology,16-qam mimo system,stochastic logic,signal detection,mimo,materials,throughput,detectors
Journal
62
Issue
ISSN
Citations 
6
1053-587X
10
PageRank 
References 
Authors
0.60
21
3
Name
Order
Citations
PageRank
Jienan Chen18413.64
Hu Jianhao29620.56
Gerald E. Sobelman322544.78