A Hybrid Quantum-Classical Machine Learning Approach for Self-Interference Cancellation in Full-Duplex Transceivers

Abstract

This letter proposes a hybrid quantum-classical machine learning (ML) approach for self-interference cancellation (SIC) in full-duplex transceivers. The proposed approach exploits a quantum long short-term memory (QLSTM) layer, integrated with a classical convolutional layer, to perform the non-linear SIC. QLSTM replaces the neural networks in the classical LSTM gates with variational quantum circuits, acting as feature extractors. Simulation results confirm the superiority of the proposed hybrid quantum-classical ML approach by achieving a significantly higher SIC performance than its fully classical counterpart at similar memory and computational requirements.