Interference Mitigation Recommender System using U-Net Autoencoders

TL;DR

This paper presents a modular recommender system that automatically selects the optimal interference mitigation strategy using U-Net autoencoders, improving robustness and reducing bit error rate in dynamic communication environments.

Contribution

It introduces a novel adaptive framework combining classification, SIR prediction, and specialized autoencoders for interference mitigation.

Findings

Improved robustness across diverse SIR levels.

Reduced bit error rate compared to single-method approaches.

Demonstrated effectiveness in various modulation environments.

Abstract

Building on the previous work on interference mitigation, this paper introduces a modular recommender system that automatically selects the most effective interference mitigation strategy based on the interference characteristics present in the received signal. The system integrates three key stages: an SPS classifier module, a SIR predictor, and a bank of specialized U-Net autoencoders designed for different interference conditions. The classification block identifies the parameters required for cancellation. The recommender then directs the signal to the appropriate mitigation model, optionally incorporating SIR-based decisions for scenarios where successive interference cancellation may be advantageous. Experiments conducted across diverse SIR levels and modulation environments show that the recommender strategy improves robustness and reduces BER compared to using any single mitigation method alone. The results demonstrate the potential of adaptive, model-selective architectures to enhance interference resilience in dynamic communication environments.