An Adaptive Deep Learning Algorithm Based Autoencoder for Interference Channels

Dehao Wu; Maziar Nekovee; Yue Wang

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arXiv · 1902.06841

An Adaptive Deep Learning Algorithm Based Autoencoder for Interference Channels

Dehao Wu·Maziar Nekovee·Yue Wang

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Abstract

Deep learning (DL) based autoencoder has shown great potential to significantly enhance the physical layer performance. In this paper, we present a DL based autoencoder for interference channel. Based on a characterization of a k-user Gaussian interference channel, where the interferences are classified as different levels from weak to very strong interferences based on a coupling parameter α, a DL neural network (NN) based autoencoder is designed to train the data set and decode the received signals. The performance such a DL autoencoder for different interference scenarios are studied, with α known or partially known, where we assume that α is predictable but with a varying up to 10\% at the training stage. The results demonstrate that DL based approach has a significant capability to mitigate the effect induced by a poor signal-to-noise ratio (SNR) and a high interference-to-noise ratio (INR). However, the enhancement depends on the knowledge of α as well as the interference levels. The proposed DL approach performs well with α up to 10\% offset for weak interference level. For strong and very strong interference channel, the offset of α needs to be constrained to less than 5\% and 2\%, respectively, to maintain similar performance as α is known.

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BibTeX RIS

Dehao Wu, Maziar Nekovee, Yue Wang. 2019-12-17. An Adaptive Deep Learning Algorithm Based Autoencoder for Interference Channels. https://arxiv.org/abs/1902.06841

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