Adaptive Nonlinear Self-Interference Cancellation for Full-Duplex Transceivers

Abstract

Full-duplex transmission comprises the ability to transmit and receive at the same time on the same frequency band. It allows for more efficient utilization of spectral resources, but raises the challenge of strong self-interference (SI). Cancellation of SI is generally implemented as a multi-stage approach. This work proposes a novel adaptive SI cancellation algorithm in the digital domain based on Kalman filter theory that creates the following advances: (i) the number of unknowns of the nonlinear SI model in cascade structure is significantly reduced compared to the conventional Hammerstein parallel model since it decouples the identification of linear and nonlinear elements; (ii) the remote signal-of-interest (SoI) is explicitly considered in the algorithm since the Kalman filter approach tunes its adaptation by the SoI power or performs successive cancellation; (iii) temporal variations of the SI channel are covered by a composite statespace model. In our simulation results, we analyze the performance by evaluating residual interference, system identification accuracy and communication rate. We show that our Kalman filter solution in cascade structure delivers good performance with low computational complexity. In this configuration, the performance lines up with that of the monolithic (parallel) Kalman filter or the recursive-least squares (RLS) algorithms with parallel Hammerstein models. The coefficients of the EF-relay are designed to attain the minimum mean-square error (MMSE) between the transmission symbols. m. The proposed system’s performance is evaluated in the presence of AWGN over non-selective MIMO channels. Simulation results are presented to demonstrate the bit-error rate (BER) performance as a function of the SNR, revealing a close match to the SI-free case for the proposed system.