An Adaptive Deep Learning Algorithm Based Autoencoder for Interference Channels
This work addresses interference mitigation in wireless communication for improved physical layer performance, but it is incremental as it applies existing DL methods to a specific channel model with adaptive tuning.
The paper tackles interference channels in communication systems by designing a deep learning autoencoder that adapts to different interference levels, showing it can mitigate poor SNR and high INR effects but performance depends on accurate knowledge of the coupling parameter α, with offsets up to 10% for weak interference but stricter limits for stronger cases.
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.