Learning for Detection: MIMO-OFDM Symbol Detection Through Downlink Pilots
Learning for Detection: MIMO-OFDM Symbol Detection Through Downlink Pilots
复制标题
检测学习:通过下行链路导频进行 MIMO-OFDM 符号检测
DOI:
10.1109/twc.2020.2976004
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发表时间:
2020
影响因子:
10.4
通讯作者:
Chang, Hao-Hsuan
中科院分区:
文献类型:
--
作者:
Zhou, Zhou;Liu, Lingjia;Chang, Hao-Hsuan
In this paper, we introduce a reservoir computing (RC) structure, namely, windowed echo state network (WESN), for multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) symbol detection. We show that adding buffers in input layers is able to bring an enhanced short-term memory (STM) to the standard echo state network. A unified training framework is developed for the introduced WESN MIMO-OFDM symbol detector using both comb and scattered patterns, where the training set size is compatible with those adopted in 3GPP LTE/LTE-Advanced standards. Complexity analysis demonstrates the advantages of WESN based symbol detector over state-of-the-art symbol detectors when the number of OFDM sub-carriers is large, where the benchmark methods are chosen as linear minimum mean square error (LMMSE) detection and sphere decoder. Numerical evaluations suggest that WESN can significantly improve the symbol detection performance as well as effectively mitigate model mismatch effects using very limited training symbols.
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DOI:
10.1109/tnnls.2017.2766162
发表时间:
2018-10-01
影响因子:
10.4
作者:
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通讯作者:
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DOI:
--
发表时间:
2018
期刊:
International Workshop on Signal Processing Advances in Wireless Communications
影响因子:
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影响因子:
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Zhang, Jianzhong (Charlie)
DOI:
--
发表时间:
2006
期刊:
--
影响因子:
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作者:
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通讯作者:
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