  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.
