# 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.

## Key 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.

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Source: https://tomesphere.com/paper/1902.06841