On Deep Learning Assisted Self-Interference Estimation in a Full-Duplex Relay Link
Konstantin Muranov, Md Atiqul Islam, Besma Smida, Natasha Devroye
Abstract
We propose and evaluate two new methods for digital mitigation of the non-linear Self-Interference (SI) signal in the context of a Full-Duplex (FD) relay link. The proposed methods utilize Deep Neural Networks (DNN) for the reconstruction of the non-linear SI components. Existing DNN-based SI mitigation techniques require a large number of on-line training samples, which takes resources away from actual data communication. The proposed methods allow a significant reduction in or complete elimination of on-line training by utilizing transfer learning and decoupling the reconstruction of non-linear transmitted SI signal from the estimation of the SI propagation channel, respectively. We utilize link-level simulation to demonstrate the SI cancellation performance of the proposed approaches compared to the existing state-of-the-art DNN-based solutions. The simulation results confirm that the proposed SI mitigation techniques achieve robust Bit Error Rate (BER) performance for Signal-to-Interference-Ratio (SIR) levels as low as −20dB. We derive an upper bound and an approximation for the probability of generalization error that is caused by an insufficient training set size.