State-Space Adaptive Nonlinear Self-Interference Cancellation for Full-Duplex Communication

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 paper proposes a novel adaptive SI cancellation algorithm in the digital domain based on Kalman filter theory that creates the following advances: first, 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; second, 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; third, temporal variations of the SI channel are covered by a composite state-space 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. We implement our algorithm as a component of a practical prototype that demonstrates the benefits of a slender algorithmic design like the proposed cascade structure.