Self-Interference Channel Estimation and Cancellation Using Compressed-Sensing and Subspace Approaches

Abstract

This chapter presents a two-stage self-interference (SI) cancellation for full-duplex MIMO communications systems. By exploiting the SI channel sparsity, a compressed-sensing based SI channel estimation technique is developed and used in the first SI cancellation radio-frequency (RF) stage to reduce the SI power prior to the receiver low-noise amplifier (LNA) and the analog-to-digital converter (ADC) to avoid overloading. Subsequently, a subspace-based algorithm is proposed to jointly estimate the coefficients of both the residual SI and intended channels, and transceiver impairments for the second SI cancellation baseband stage to further reduce the residual SI. Unlike other previous works, the intended signal is taken into consideration during the estimation process to reduce the overhead. It is demonstrated that the SI channel coefficients can be perfectly estimated without any knowledge of the intended signal, and only a few training symbols are needed for ambiguity removal in intended-channel estimation. Simulation results show that the proposed algorithms outperform the Least Square (LS) algorithms and offer the remarkable signal-to-residual-SI-and-noise ratio (SINR) approaching the signal-to-noise ratio (SNR).