Brain-Inspired Wireless Communications: Where Reservoir Computing Meets MIMO-OFDM

Abstract

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.

Department(s)

Electrical and Computer Engineering

Sponsor(s)

National Science Foundation (U.S.)

Comments

The work of L. Liu and Y. Yi was supported by NSF under Grant ECCS-1731928

Keywords and Phrases

Bit error rate; Channel estimation; Codes (symbols); Communication channels (information theory); Data handling; Dynamical systems; Feedback control; Frequency estimation; Gain control; Hardware; Nonlinear analysis; Nonlinear dynamical systems; Orthogonal frequency division multiplexing; Personnel training; Petroleum reservoirs; Receivers (containers); Recurrent neural networks; Signal detection; Signal receivers; Telecommunication repeaters; Wireless telecommunication systems; Echo state networks; Nonlinear channel; Reservoir Computing; Symbol detector; Wireless communications; MIMO systems; Echo state network (ESN); Multiple input multiple output (MIMO); Nonlinear channel; Orthogonal frequency division multiplexing (OFDM); Power amplifiers (PA); Reservoir computing (RC); Reservoirs; Training; Wireless communication

International Standard Serial Number (ISSN)

2162-237X

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Dec 2017

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