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Multi-user full duplex transceiver design for mmWave systems using learning-aided channel prediction

Multi-user full duplex transceiver design for mmWave systems using learning-aided channel prediction
Multi-user full duplex transceiver design for mmWave systems using learning-aided channel prediction
Millimeter Wave (mmWave) technology coupled with full duplex (FD) communication has the potential of increasing the spectral efficiency. However, the self-interference (SI) encountered in the FD mode and the ubiquitous multi-user interference (MI) contaminates the signal. Furthermore, the system performance may also be limited by channel aging that arises because of the time-varying nature of the channel. Therefore, in this paper, we conceive FD hybrid beamforming (HBF) for K -user multiple-input multiple-output (MIMO)-aided orthogonal frequency division multiplexing (OFDM) using learning-aided channel prediction. We first derive a joint precoder and combiner design for full duplex K -user MIMO-OFDM interference channels, where we aim for minimizing both the residual SI and the MI, followed by an iterative hybrid decomposition technique developed for OFDM systems. Then, we propose a learning-aided channel prediction technique for systems suffering from channel aging relying on a radial basis neural network, where we show by simulation that upon using sufficient training, learning-assisted channel prediction can faithfully estimate the current channel. Furthermore, we demonstrate by simulations that our proposed joint hybrid precoder and combiner design outperforms the popular Eigen beamforming (EBF) technique by about 5 dB for a 128×32 -element MIMO aided OFDM system having 32 sub-carriers.
2169-3536
66068 - 66083
Katla, Satyanarayana
f3436daa-e5da-4b3c-ab4b-ad07a0cef99a
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Katla, Satyanarayana
f3436daa-e5da-4b3c-ab4b-ad07a0cef99a
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Katla, Satyanarayana, El-Hajjar, Mohammed and Hanzo, Lajos (2019) Multi-user full duplex transceiver design for mmWave systems using learning-aided channel prediction. IEEE Access, 66068 - 66083. (doi:10.1109/ACCESS.2019.2916799).

Record type: Article

Abstract

Millimeter Wave (mmWave) technology coupled with full duplex (FD) communication has the potential of increasing the spectral efficiency. However, the self-interference (SI) encountered in the FD mode and the ubiquitous multi-user interference (MI) contaminates the signal. Furthermore, the system performance may also be limited by channel aging that arises because of the time-varying nature of the channel. Therefore, in this paper, we conceive FD hybrid beamforming (HBF) for K -user multiple-input multiple-output (MIMO)-aided orthogonal frequency division multiplexing (OFDM) using learning-aided channel prediction. We first derive a joint precoder and combiner design for full duplex K -user MIMO-OFDM interference channels, where we aim for minimizing both the residual SI and the MI, followed by an iterative hybrid decomposition technique developed for OFDM systems. Then, we propose a learning-aided channel prediction technique for systems suffering from channel aging relying on a radial basis neural network, where we show by simulation that upon using sufficient training, learning-assisted channel prediction can faithfully estimate the current channel. Furthermore, we demonstrate by simulations that our proposed joint hybrid precoder and combiner design outperforms the popular Eigen beamforming (EBF) technique by about 5 dB for a 128×32 -element MIMO aided OFDM system having 32 sub-carriers.

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Accepted/In Press date: 11 May 2019
e-pub ahead of print date: 14 May 2019

Identifiers

Local EPrints ID: 430975
URI: https://eprints.soton.ac.uk/id/eprint/430975
ISSN: 2169-3536
PURE UUID: 61e0eeff-7b23-4292-8587-5577c958eccb
ORCID for Satyanarayana Katla: ORCID iD orcid.org/0000-0002-5411-3962
ORCID for Mohammed El-Hajjar: ORCID iD orcid.org/0000-0002-7987-1401
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 20 May 2019 16:30
Last modified: 20 Jul 2019 04:01

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