IntLearner: AI-enabled Interference Mitigation for Wireless Networks

TL;DR

IntLearner leverages AI and neural networks to accurately estimate and mitigate interference in 6G wireless networks using only physical-layer information, significantly improving channel estimation and reducing interference.

Contribution

The paper introduces IntLearner, a novel AI-based interference mitigation method that uses domain-guided neural networks to enhance wireless communication performance.

Findings

Increases uplink channel estimation accuracy by up to 7.4x.

Reduces downlink SINR requirement by 1.5dB for the same BLER.

Effective interference mitigation with PHY-only information.

Abstract

The future Six-Generation (6G) envisions massive access of wireless devices in the network, leading to more serious interference from concurrent transmissions between wireless devices in the same frequency band. Existing interference mitigation approaches takes the interference signals as Gaussian white noise, which cannot precisely estimate the non-Gaussian interference signals from other devices. In this paper, we present IntLearner, a new interference mitigation technique that estimates and mitigates the impact of interference signals with only physical-layer (PHY) information available in base-station (BS) and user-equipment (UE), including channel estimator and constellations. More specifically, IntLearner utilizes the power of AI to estimate the features in interference signals, and removes the interference from the interfered received signal with neural network (NN). IntLearner's NN adopts a modular NN design, which takes the domain knowledge of BS and UE PHY as the guidance to NN design for minimizing training confusion and NN complexity. Simulation results show IntLearner increases Uplink (UL) channel estimation accuracy up to 7.4x, and reduces the Downlink (DL) Signal to Interference Ratio plus Noise Ratio (SINR) requirement to achieve the same Block Error Rate (BLER) by 1.5dB in a conventional multi-cell scenario.