Learning for Detection: MIMO-OFDM Symbol Detection Through Downlink Pilots

Learning for Detection: MIMO-OFDM Symbol Detection Through Downlink Pilots
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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
Chang, Hao-Hsuan
中科院分区:
计算机科学1区
文献类型:
--
作者:
Zhou, Zhou;Liu, Lingjia;Chang, Hao-Hsuan

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在本文中,我们介绍了水库计算(RC)结构,即加窗回声状态网络(WESN),多输入多输出正交频分复用(MIMO-OFDM)符号检测。我们表明,在输入层中添加缓冲区能够为标准回声状态网络带来增强的短期记忆(STM)。为引入的WESN MIMO-OFDM符号检测器开发了一个统一的训练框架,该框架使用梳状图案和散射图案,其中训练集大小与3GPP LTE/LTE-Advanced标准中采用的训练集大小兼容。复杂度分析表明,当OFDM子载波数较多时,基于WESN的符号检测器优于现有的符号检测器,其中基准检测方法为线性最小均方误差(LMMSE)检测和球形译码。数值计算表明,WESN可以显着提高符号检测性能,以及有效地减轻模型失配的影响,使用非常有限的训练符号。
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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