Nonlinear Self-Interference Cancellation With Adaptive Orthonormal Polynomials for Full-Duplex Wireless Systems

Abstract

Nonlinear self-interference cancellation (SIC) techniques are essential for enabling full-duplex communication systems, which can offer spectral efficiencies twice that of traditional half-duplex systems. The challenge of nonlinear SIC is similar to the classic problem of system identification in adaptive filter theory, whose crux lies in constructing the optimal nonlinear basis functions of a nonlinear system. This becomes especially difficult when the system input has a non-stationary distribution, as is the case in practical wireless systems. In this paper, we propose a novel algorithm for nonlinear digital SIC that adaptively constructs orthonormal polynomial basis functions according to the non-stationary moments of the transmit signal. By combining these basis functions with the least mean squares (LMS) algorithm, we introduce a new SIC technique, called the adaptive orthonormal polynomial LMS (AOP-LMS) algorithm. To reduce computational complexity for practical systems, we augment our approach with a precomputed look-up table, which maps a given modulation and coding scheme to its corresponding basis functions. Numerical simulation indicates that our proposed method surpasses existing state-of-the-art SIC algorithms in terms of convergence speed and mean squared error when the transmit signal is non-stationary, such as with adaptive modulation and coding. Experimental evaluation with a wireless testbed further confirms that our proposed approach outperforms existing digital SIC algorithms in practical systems.