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
A técnica de espaço de frequência de tempo ortogonal (OTFS) proposta nos últimos anos possui excelente deslocamento de frequência anti-Doppler e desempenho de atraso de tempo, possibilitando sua aplicação em cenários de comunicação de alta velocidade. Neste artigo, um algoritmo de detecção de sinal de otimização de enxame de partículas (PSO) para o sistema OTFS é proposto, um mecanismo adaptativo para o fator de aprendizagem individual e o fator de aprendizagem global na fórmula de velocidade do algoritmo é projetado e o método de atualização de posição das partículas é melhorado, de modo a aumentar a precisão da convergência e evitar que as partículas caiam no ótimo local. Os resultados da simulação mostram que o algoritmo PSO aprimorado tem as vantagens de baixa taxa de erro de bit (BER) e alta precisão de convergência em comparação com o algoritmo PSO tradicional, e tem desempenho semelhante ao algoritmo de detecção de máxima verossimilhança (ML) de estado ideal com menor complexidade. No caso de alto deslocamento Doppler, a tecnologia OTFS tem melhor desempenho do que a tecnologia de multiplexação por divisão de frequência ortogonal (OFDM) usando algoritmo PSO aprimorado.
Jurong BAI
Xi'an University of Posts and Telecommunications
Lin LAN
Xi'an University of Posts and Telecommunications
Zhaoyang SONG
Xi'an University of Posts and Telecommunications
Huimin DU
Xi'an University of Posts and Telecommunications
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Jurong BAI, Lin LAN, Zhaoyang SONG, Huimin DU, "Signal Detection for OTFS System Based on Improved Particle Swarm Optimization" in IEICE TRANSACTIONS on Communications,
vol. E106-B, no. 8, pp. 614-621, August 2023, doi: 10.1587/transcom.2022EBP3140.
Abstract: The orthogonal time frequency space (OTFS) technique proposed in recent years has excellent anti-Doppler frequency shift and time delay performance, enabling its application in high speed communication scenarios. In this article, a particle swarm optimization (PSO) signal detection algorithm for OTFS system is proposed, an adaptive mechanism for the individual learning factor and global learning factor in the speed formula of the algorithm is designed, and the position update method of the particles is improved, so as to increase the convergence accuracy and avoid the particles to fall into local optimum. The simulation results show that the improved PSO algorithm has the advantages of low bit error rate (BER) and high convergence accuracy compared with the traditional PSO algorithm, and has similar performance to the ideal state maximum likelihood (ML) detection algorithm with lower complexity. In the case of high Doppler shift, OTFS technology has better performance than orthogonal frequency division multiplexing (OFDM) technology by using improved PSO algorithm.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2022EBP3140/_p
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@ARTICLE{e106-b_8_614,
author={Jurong BAI, Lin LAN, Zhaoyang SONG, Huimin DU, },
journal={IEICE TRANSACTIONS on Communications},
title={Signal Detection for OTFS System Based on Improved Particle Swarm Optimization},
year={2023},
volume={E106-B},
number={8},
pages={614-621},
abstract={The orthogonal time frequency space (OTFS) technique proposed in recent years has excellent anti-Doppler frequency shift and time delay performance, enabling its application in high speed communication scenarios. In this article, a particle swarm optimization (PSO) signal detection algorithm for OTFS system is proposed, an adaptive mechanism for the individual learning factor and global learning factor in the speed formula of the algorithm is designed, and the position update method of the particles is improved, so as to increase the convergence accuracy and avoid the particles to fall into local optimum. The simulation results show that the improved PSO algorithm has the advantages of low bit error rate (BER) and high convergence accuracy compared with the traditional PSO algorithm, and has similar performance to the ideal state maximum likelihood (ML) detection algorithm with lower complexity. In the case of high Doppler shift, OTFS technology has better performance than orthogonal frequency division multiplexing (OFDM) technology by using improved PSO algorithm.},
keywords={},
doi={10.1587/transcom.2022EBP3140},
ISSN={1745-1345},
month={August},}
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TY - JOUR
TI - Signal Detection for OTFS System Based on Improved Particle Swarm Optimization
T2 - IEICE TRANSACTIONS on Communications
SP - 614
EP - 621
AU - Jurong BAI
AU - Lin LAN
AU - Zhaoyang SONG
AU - Huimin DU
PY - 2023
DO - 10.1587/transcom.2022EBP3140
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E106-B
IS - 8
JA - IEICE TRANSACTIONS on Communications
Y1 - August 2023
AB - The orthogonal time frequency space (OTFS) technique proposed in recent years has excellent anti-Doppler frequency shift and time delay performance, enabling its application in high speed communication scenarios. In this article, a particle swarm optimization (PSO) signal detection algorithm for OTFS system is proposed, an adaptive mechanism for the individual learning factor and global learning factor in the speed formula of the algorithm is designed, and the position update method of the particles is improved, so as to increase the convergence accuracy and avoid the particles to fall into local optimum. The simulation results show that the improved PSO algorithm has the advantages of low bit error rate (BER) and high convergence accuracy compared with the traditional PSO algorithm, and has similar performance to the ideal state maximum likelihood (ML) detection algorithm with lower complexity. In the case of high Doppler shift, OTFS technology has better performance than orthogonal frequency division multiplexing (OFDM) technology by using improved PSO algorithm.
ER -