Deep learning based autoencoder for m-user wireless interference channel physical layer design

Wu, Dehao, Nekovee, Maziar and Wang, Yue (2020) Deep learning based autoencoder for m-user wireless interference channel physical layer design. IEEE Access, 8. pp. 174679-174691. ISSN 2169-3536

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Deep learning (DL) based autoencoder (AE) has been proposed recently as a promising, and potentially disruptive approach to design the physical layer of beyond-5G communication systems. Compared to a traditional communication system with a multiple-block structure, the DL based AE approach provides a new paradigm to physical layer design with a pure data-driven and end-to-end learning based solution. In this paper, we address the dynamic interference in a multi-user Gaussian interference channel. We show that standard constellation are not optimal for this context, in particular, for a high interference condition. We propose a novel adaptive DL based AE to overcome this problem. With our approach, dynamic interference can be learned and predicted, which updates the learning processing for the decoder. Compared to other machine learning approaches, our method does not rely on a fixed training function, but is adaptive and applicable to practical systems. In comparison with the conventional system using n-psk or n-QAM modulation schemes with zero force (ZF) and minimum mean square error (MMSE) equalizer, the proposed adaptive deep learning (ADL) based AE demonstrates a significant achievable BER in the presence of interference, especially in strong and very strong interference scenarios. The proposed approach has laid the foundation of enabling adaptable constellation for 5G and beyond communication systems, where dynamic and heterogeneous network conditions are envisaged.

Item Type: Article
Keywords: Deep learning, Autoencoder, 5G physical layer, Interference channel
Schools and Departments: School of Engineering and Informatics > Engineering and Design
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 25 Sep 2020 15:09
Last Modified: 05 Feb 2021 15:30

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