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On Unsupervised Deep Learning Solutions for Coherent MU-SIMO Detection in Fading Channels

Xue, Songyan, Ma, Yi, Li, Ang, Yi, Na and Tafazolli, Rahim (2019) On Unsupervised Deep Learning Solutions for Coherent MU-SIMO Detection in Fading Channels In: The 55th IEEE International Conference on Communications (ICC 2019), 20-24 May 2019, Shanghai, China.

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Abstract

In this paper, unsupervised deep learning solutions for multiuser single-input multiple-output (MU-SIMO) coherent detection are extensively investigated. According to the ways of utilizing the channel state information at the receiver side (CSIR), deep learning solutions are divided into two groups. One group is called equalization and learning, which utilizes the CSIR for channel equalization and then employ deep learning for multiuser detection (MUD). The other is called direct learning, which directly feeds the CSIR, together with the received signal, into deep neural networks (DNN) to conduct the MUD. It is found that the direct learning solutions outperform the equalizationand- learning solutions due to their better exploitation of the sequence detection gain. On the other hand, the direct learning solutions are not scalable to the size of SIMO networks, as current DNN architectures cannot efficiently handle many cochannel interferences. Motivated by this observation, we propose a novel direct learning approach, which can combine the merits of feedforward DNN and parallel interference cancellation. It is shown that the proposed approach trades off the complexity for the learning scalability, and the complexity can be managed due to the parallel network architecture.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Xue, Songyansongyan.xue@surrey.ac.uk
Ma, YiY.Ma@surrey.ac.uk
Li, Angang.li@surrey.ac.uk
Yi, NaN.Yi@surrey.ac.uk
Tafazolli, RahimR.Tafazolli@surrey.ac.uk
Date : 3 February 2019
Funders : European Commission under the framework of the Horizon2020 5G-Drive project, 5G Innovation Centre (5GIC) HEFEC
Copyright Disclaimer : © Copyright 2019 IEEE – All rights reserved.
Depositing User : Diane Maxfield
Date Deposited : 18 Feb 2019 15:14
Last Modified : 18 Feb 2019 15:14
URI: http://epubs.surrey.ac.uk/id/eprint/850459

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