Brain-Inspired Wireless Communications: Where Reservoir Computing Meets MIMO-OFDM
Brain-Inspired Wireless Communications: Where Reservoir Computing Meets MIMO-OFDM
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DOI：
10.1109/tnnls.2017.2766162
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发表时间：
2018-10-01
影响因子：
10.4
通讯作者：
Yi, Yang
中科院分区：
文献类型：
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作者：
Mosleh, Somayeh (Susanna);Liu, Lingjia;Yi, Yang
水库计算（RC）是一类神经形态计算方法，特别适合处理时间序列预测任务。它显着降低了递归神经网络的训练复杂性，也适用于硬件实现，其中设备物理被用于执行数据处理。在本文中，RC的概念被应用到检测多输入多输出正交频分复用（MIMO-OFDM）系统中的发送符号。由于无线传播，发射的信号在到达接收器之前可能经历严重的失真。由发射机处的功率放大器引入的非线性失真可能使该过程进一步复杂化。因此，有效的符号检测策略变得至关重要。传统的接收机符号检测方法需要对MIMO-OFDM系统进行精确的信道估计。然而，在本文中，我们介绍了一种新的符号检测方案的MIMO-OFDM信道的估计变得不必要。所介绍的方案利用回声状态网络（ESN），这是一个特殊的类RC。ESN作为一个黑盒系统建模的目的，可以预测非线性动态系统在一个有效的方式。对非线性MIMO-OFDM系统未编码误码率的仿真结果表明，该方案优于传统的符号检测方法。
Reservoir computing (RC) is a class of neuromorphic computing approaches that deals particularly well with time-series prediction tasks. It significantly reduces the training complexity of recurrent neural networks and is also suitable for hardware implementation whereby device physics are utilized in performing data processing. In this paper, the RC concept is applied to detecting a transmitted symbol in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Due to wireless propagation, the transmitted signal may undergo severe distortion before reaching the receiver. The nonlinear distortion introduced by the power amplifier at the transmitter may further complicate this process. Therefore, an efficient symbol detection strategy becomes critical. The conventional approach for symbol detection at the receiver requires accurate channel estimation of the underlying MIMO-OFDM system. However, in this paper, we introduce a novel symbol detection scheme where the estimation of the MIMO-OFDM channel becomes unnecessary. The introduced scheme utilizes an echo state network (ESN), which is a special class of RC. The ESN acts as a black box for system modeling purposes and can predict nonlinear dynamic systems in an efficient way. Simulation results for the uncoded bit error rate of nonlinear MIMO-OFDM systems show that the introduced scheme outperforms conventional symbol detection methods.