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An Adaptive Deep Learning Algorithm Based Autoencoder for Interference Channels

Abstract : Deep learning (DL) based autoencoder (AE) has been proposed recently as a promising, and potentially disruptive Physical Layer (PHY) design for beyond-5G communication systems. Compared to a traditional communication system with a multiple-block structure, the DL based AE provides a new PHY paradigm with a pure data-driven and end-to-end learning based solution. However, significant challenges are to be overcome before this approach becomes a serious contender for practical beyond-5G systems. One of such challenges is the robustness of AE under interference channels. In this paper, we first evaluate the performance and robustness of an AE in the presence of an interference channel. Our results show that AE performs well under weak and moderate interference condition, while its performance degrades substantially under strong and very strong interference condition. We further propose a novel online adaptive deep learning (ADL) algorithm to tackle the performance issue of AE under strong and very strong interference, where level of interference can be predicted in real time for the decoding process. The performance of the proposed algorithm for different interference scenarios is studied and compared to the existing system using a conventional DL-assist AE through an offline learning method. Our results demonstrate the robustness of the proposed ADL-assist AE over the entire range of interference levels, while existing AE fail to perform in the presence of strong and very strong interference. The work proposed in this paper is an important step towards enabling AE for practical 5G and beyond communication systems with dynamic and heterogeneous interference.
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https://hal.inria.fr/hal-03266473
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Submitted on : Monday, June 21, 2021 - 5:32:25 PM
Last modification on : Friday, July 30, 2021 - 3:59:51 PM
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Dehao Wu, Maziar Nekovee, yue Wang. An Adaptive Deep Learning Algorithm Based Autoencoder for Interference Channels. 2nd International Conference on Machine Learning for Networking (MLN), Dec 2019, Paris, France. pp.342-354, ⟨10.1007/978-3-030-45778-5_23⟩. ⟨hal-03266473⟩

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