BEM Adaptive filtering for SI cancellation in full-duplex underwater acoustic systems

Abstract

Self-interference (SI) cancellation (SIC) is the key technology for achieving full-duplex (FD) communications in underwater acoustic systems. In practice, SI channels are often fast-varying, e.g., due to reflections from surface waves. Classical adaptive filters, such as the recursive least squares (RLS) algorithm, predict the channel impulse response when used for channel estimation. If a tracking delay is acceptable, interpolating estimators capable of providing more accurate estimates of time-varying impulse responses can be used. Interpolating estimators with good tracking performance are normally of high complexity. In this paper, we propose low-complexity interpolating adaptive filters which combine the basis expansion model (BEM) approach with the sliding-window RLS (SRLS) algorithm. Specifically, we use the Legendre polynomials as the basis functions and solve the system of equations using dichotomous coordinate descent (DCD) iterations, thus the name the SRLS-L-DCD adaptive filter. A sparse algorithm (HSRLS-L-DCD) based on homotopy iterations is then proposed to exploit the sparsity in the expansion coefficients. The identification performance of the adaptive filters is investigated by a simulation which mimics an FD lake experiment. Both the simulation and lake experiments show that significant improvement in the SIC performance is achieved with the proposed low-complexity algorithms compared to the classical SRLS algorithm.