Non-linear self-interference cancellation on base of mixed Newton method

The paper investigates a potential solution to the problem of Self-Interference Cancellation (SIC) encountered in the design of In-Band Full-Duplex (IBFD) communication systems. The suppression of selfinterference is implemented in the digital domain using multilayer nonlinear models adapted via the gradient descent method. The presence of local optima and saddle points in the adaptation of multilayer models prevents the use of second-order methods due to the indefinite nature of the Hessian matrix.
This work proposes the use of the Mixed Newton Method (MNM), which incorporates information about the second-order mixed partial derivatives of the loss function, thereby enabling a faster convergence rate compared to traditional first-order methods. By constructing the Hessian matrix solely with mixed second-order partial derivatives, this approach mitigates the issue of “getting stuck” at saddle points when applying the Mixed Newton Method for adapting multilayer nonlinear self-interference compensators in full-duplex system design.
The Hammerstein model with complex parameters has been selected to represent nonlinear selfinterference. This choice is motivated by the model’s ability to accurately describe the underlying physical properties of self-interference formation. Due to the holomorphic property of the model output, the Mixed Newton Method provides a “repulsion” effect from saddle points in the loss landscape.
The paper presents convergence curves for the adaptation of the Hammerstein model using both the Mixed Newton Method and conventional gradient descent-based approaches. Additionally, it provides a derivation of the proposed method along with an assessment of its computational complexity.