The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Este artigo aborda a contaminação piloto em uplink massivo de múltiplas entradas e múltiplas saídas (MIMO). A contaminação piloto é causada pela reutilização de sequências piloto idênticas em células adjacentes. Para resolver a contaminação piloto, a estação base utiliza diferenças entre os quadros de transmissão de diferentes usuários, que são detectados via canal conjunto e estimativa de dados. A estimativa conjunta é considerada um problema de inferência bilinear em sensoriamento comprimido. A propagação de expectativa (EP) é usada para propor um canal iterativo e um algoritmo de estimativa de dados. As estimativas iniciais do canal são obtidas através de pilotos deslocados no tempo, sem explorar informações sobre desvanecimento em grande escala. O EP proposto modifica dois pontos na passagem aproximada de mensagens do vetor adaptativo bilinear convencional (BAd-VAMP). Uma é que o EP utiliza estimativas de dados após a decisão suave na estimativa do canal, enquanto o BAd-VAMP os utiliza antes da decisão suave. O outro ponto é que o EP pode utilizar a distribuição anterior da matriz do canal, enquanto o BAd-VAMP não pode, em princípio. Simulações numéricas mostram que o EP converge muito mais rápido que o BAd-VAMP em MIMO espacialmente correlacionado, no qual a passagem aproximada de mensagens não consegue convergir para o mesmo ponto fixo que o EP e o BAd-VAMP.
Wataru TATSUNO
Toyohashi University of Technology
Keigo TAKEUCHI
Toyohashi University of Technology
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Wataru TATSUNO, Keigo TAKEUCHI, "Pilot Decontamination in Spatially Correlated Massive MIMO Uplink via Expectation Propagation" in IEICE TRANSACTIONS on Fundamentals,
vol. E104-A, no. 4, pp. 723-733, April 2021, doi: 10.1587/transfun.2020EAP1073.
Abstract: This paper addresses pilot contamination in massive multiple-input multiple-output (MIMO) uplink. Pilot contamination is caused by reuse of identical pilot sequences in adjacent cells. To solve pilot contamination, the base station utilizes differences between the transmission frames of different users, which are detected via joint channel and data estimation. The joint estimation is regarded as a bilinear inference problem in compressed sensing. Expectation propagation (EP) is used to propose an iterative channel and data estimation algorithm. Initial channel estimates are attained via time-shifted pilots without exploiting information about large scale fading. The proposed EP modifies two points in conventional bilinear adaptive vector approximate message-passing (BAd-VAMP). One is that EP utilizes data estimates after soft decision in the channel estimation while BAd-VAMP uses them before soft decision. The other point is that EP can utilize the prior distribution of the channel matrix while BAd-VAMP cannot in principle. Numerical simulations show that EP converges much faster than BAd-VAMP in spatially correlated MIMO, in which approximate message-passing fails to converge toward the same fixed-point as EP and BAd-VAMP.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2020EAP1073/_p
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@ARTICLE{e104-a_4_723,
author={Wataru TATSUNO, Keigo TAKEUCHI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Pilot Decontamination in Spatially Correlated Massive MIMO Uplink via Expectation Propagation},
year={2021},
volume={E104-A},
number={4},
pages={723-733},
abstract={This paper addresses pilot contamination in massive multiple-input multiple-output (MIMO) uplink. Pilot contamination is caused by reuse of identical pilot sequences in adjacent cells. To solve pilot contamination, the base station utilizes differences between the transmission frames of different users, which are detected via joint channel and data estimation. The joint estimation is regarded as a bilinear inference problem in compressed sensing. Expectation propagation (EP) is used to propose an iterative channel and data estimation algorithm. Initial channel estimates are attained via time-shifted pilots without exploiting information about large scale fading. The proposed EP modifies two points in conventional bilinear adaptive vector approximate message-passing (BAd-VAMP). One is that EP utilizes data estimates after soft decision in the channel estimation while BAd-VAMP uses them before soft decision. The other point is that EP can utilize the prior distribution of the channel matrix while BAd-VAMP cannot in principle. Numerical simulations show that EP converges much faster than BAd-VAMP in spatially correlated MIMO, in which approximate message-passing fails to converge toward the same fixed-point as EP and BAd-VAMP.},
keywords={},
doi={10.1587/transfun.2020EAP1073},
ISSN={1745-1337},
month={April},}
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TY - JOUR
TI - Pilot Decontamination in Spatially Correlated Massive MIMO Uplink via Expectation Propagation
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 723
EP - 733
AU - Wataru TATSUNO
AU - Keigo TAKEUCHI
PY - 2021
DO - 10.1587/transfun.2020EAP1073
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E104-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 2021
AB - This paper addresses pilot contamination in massive multiple-input multiple-output (MIMO) uplink. Pilot contamination is caused by reuse of identical pilot sequences in adjacent cells. To solve pilot contamination, the base station utilizes differences between the transmission frames of different users, which are detected via joint channel and data estimation. The joint estimation is regarded as a bilinear inference problem in compressed sensing. Expectation propagation (EP) is used to propose an iterative channel and data estimation algorithm. Initial channel estimates are attained via time-shifted pilots without exploiting information about large scale fading. The proposed EP modifies two points in conventional bilinear adaptive vector approximate message-passing (BAd-VAMP). One is that EP utilizes data estimates after soft decision in the channel estimation while BAd-VAMP uses them before soft decision. The other point is that EP can utilize the prior distribution of the channel matrix while BAd-VAMP cannot in principle. Numerical simulations show that EP converges much faster than BAd-VAMP in spatially correlated MIMO, in which approximate message-passing fails to converge toward the same fixed-point as EP and BAd-VAMP.
ER -