Randomized Iterative Sampling Decoding Algorithm For Large-Scale MIMO Detection

In this paper, the paradigm of the traditional iterative decoding schemes for the uplink large-scale MIMO detection is extended by sampling in an Markov chain Monte Carlo (MCMC) way. Different from iterative decoding whose performance is upper bounded by the suboptimal linear decoding scheme like ZF...

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Vydané v:	IEEE transactions on signal processing Ročník 72; s. 1 - 14
Hlavní autori:	Wang, Zheng, Xia, Yili, Ling, Cong, Huang, Yongming
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.01.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Convergence Iterative decoding iterative methods Jacobian matrices Large-scale MIMO detection Markov analysis Markov chains Markov processes massive MIMO detection Maximum likelihood decoding MCMC MIMO communication Performance enhancement Random sampling sampling decoding Signal processing algorithms
ISSN:	1053-587X, 1941-0476
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Abstract	In this paper, the paradigm of the traditional iterative decoding schemes for the uplink large-scale MIMO detection is extended by sampling in an Markov chain Monte Carlo (MCMC) way. Different from iterative decoding whose performance is upper bounded by the suboptimal linear decoding scheme like ZF or MMSE, the proposed iterative random sampling decoding (IRSD) algorithm is capable of achieving the optimal ML decoding performance with the increment of Markov moves, thus establishing a flexible trade-off between suboptimal and optimal decoding performance. According to convergence analysis, we show that the Markov chain induced by IRSD algorithm experiences the exponential convergence, and its related convergence rate is also derived in detail. Based on it, the Markov mixing becomes tractable, followed by the decoding optimization with respect to the standard deviation of the target distribution. Meanwhile, further decoding performance enhancement and parallel implementation are also studied so that the proposed IRSD algorithm is well suited for various cases of large-scale MIMO systems.
AbstractList	In this paper, the paradigm of the traditional iterative decoding schemes for the uplink large-scale MIMO detection is extended by sampling in an Markov chain Monte Carlo (MCMC) way. Different from iterative decoding whose performance is upper bounded by the suboptimal linear decoding scheme like ZF or MMSE, the proposed iterative random sampling decoding (IRSD) algorithm is capable of achieving the optimal ML decoding performance with the increment of Markov moves, thus establishing a flexible trade-off between suboptimal and optimal decoding performance. According to convergence analysis, we show that the Markov chain induced by IRSD algorithm experiences the exponential convergence, and its related convergence rate is also derived in detail. Based on it, the Markov mixing becomes tractable, followed by the decoding optimization with respect to the standard deviation of the target distribution. Meanwhile, further decoding performance enhancement and parallel implementation are also studied so that the proposed IRSD algorithm is well suited for various cases of large-scale MIMO systems.
Author	Xia, Yili Huang, Yongming Ling, Cong Wang, Zheng
Author_xml	– sequence: 1 givenname: Zheng surname: Wang fullname: Wang, Zheng email: wznuaa@gmail.com organization: School of Information Science and Engineering, and with Frontiers Science Center for Mobile Information Communication and Security, Southeast University, Nanjing, China – sequence: 2 givenname: Yili surname: Xia fullname: Xia, Yili organization: School of Information Science and Engineering, and with Frontiers Science Center for Mobile Information Communication and Security, Southeast University, Nanjing, China – sequence: 3 givenname: Cong surname: Ling fullname: Ling, Cong email: cling@ieee.org organization: Department of Electrical and Electronic Engineering, Imperial College London, London, U.K – sequence: 4 givenname: Yongming surname: Huang fullname: Huang, Yongming organization: School of Information Science and Engineering, and with Frontiers Science Center for Mobile Information Communication and Security, Southeast University, Nanjing, China
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SubjectTerms	Algorithms Convergence Iterative decoding iterative methods Jacobian matrices Large-scale MIMO detection Markov analysis Markov chains Markov processes massive MIMO detection Maximum likelihood decoding MCMC MIMO communication Performance enhancement Random sampling sampling decoding Signal processing algorithms
Title	Randomized Iterative Sampling Decoding Algorithm For Large-Scale MIMO Detection
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