Supervised-Learning for Multi-Hop MU-MIMO Communications with One-Bit Transceivers
- Authors
- Daeun, Kim; Hong, Song nam; Namyoon, Lee
- Issue Date
- Nov-2019
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Multi-user multiple-input multiple-output (MU-MIMO); multihop relay networks; machine learning
- Citation
- IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, v.37, no.11, pp.2559 - 2572
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
- Volume
- 37
- Number
- 11
- Start Page
- 2559
- End Page
- 2572
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/11687
- DOI
- 10.1109/JSAC.2019.2933965
- ISSN
- 0733-8716
- Abstract
- This paper considers a nonlinear multi-hop multi-user multiple-input multiple-output (MU-MIMO) relay channel, in which multiple users send information symbols to a multi-antenna base station (BS) with one-bit analog-to-digital converters via intermediate relays, each with one-bit transceiver. To understand the fundamental limit of the detection performance, the optimal maximum-likelihood (ML) detector is proposed with the assumption of perfect and global channel state information (CSI) at the BS. This multi-user detector, however, is not practical due to the unrealistic CSI assumption and the overwhelming detection complexity. These limitations are addressed by presenting a novel detection framework inspired by supervised-learning. The key idea is to model the complicated multi-hop MU-MIMO channel as a simplified channel with much fewer and learnable parameters. One major finding is that, even using the simplified channel model, a near ML detection performance is achievable with a reasonable amount of pilot overheads in a certain condition. In addition, an online supervised-learning detector is proposed, which adaptively tracks channel variations. The idea is to update the model parameters with a reliably detected data symbol by treating it as a new training (labeled) data. Lastly, a multi-user detector using a deep neural network is proposed. Unlike the model-based approaches, this model-free approach enables to remove the errors in the simplified channel model, while increasing the computational complexity for parameter learning. Via simulations, the detection performances of classical, model-based, and model-free detectors are thoroughly compared to demonstrate the effectiveness of the supervised-learning approaches in this channel.
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