An Adaptive Deep Learning Algorithm Based Autoencoder for Interference Channels

TL;DR

This paper introduces a deep learning autoencoder designed for interference channels, demonstrating its ability to improve signal decoding under various interference levels and uncertainties in interference parameters.

Contribution

It presents a novel DL autoencoder tailored for interference channels, analyzing its robustness to interference parameter variations and different interference regimes.

Findings

DL autoencoder mitigates poor SNR and high INR effects.

Performance depends on interference parameter knowledge.

Effective with up to 10 ext% interference offset in weak interference.

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 {\alpha}, 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 {\alpha} known or partially known, where we assume that {\alpha} 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 {\alpha} as well as the interference levels. The proposed DL approach performs well with {\alpha} up to 10\% offset for weak interference level. For strong and very strong interference channel, the offset of {\alpha} needs to be constrained to less than 5\% and 2\%, respectively, to maintain similar performance as {\alpha} is known.