Sampled-data $H^{\infty}$ Optimization for Self-interference Suppression in Baseband Signal Subspaces

TL;DR

This paper introduces a novel sampled-data $H^{ ablafty}$ control approach for designing self-interference cancelers in wireless relay stations, considering practical implementation constraints within the baseband signal subspace.

Contribution

It formulates the self-interference cancellation problem as a sampled-data $H^{ ablafty}$ control problem and reduces it to a standard discrete-time $H^{ ablafty}$ problem using lifting techniques, accounting for implementation structures.

Findings

The proposed method effectively suppresses self-interference in simulations.

The control design accounts for practical sampler and hold implementations.

Simulation results demonstrate the method's effectiveness.

Abstract

In this article, we propose a design method of selfinterference cancelers for wireless relay stations taking account of the baseband signal subspace. The problem is first formulated as a sampled-data $H^{\infty}$ control problem with a generalized sampler and a generalized hold, which can be reduced to a discretetime $\ell^2$-induced norm minimization problem. Taking account of the implementation of the generalized sampler and hold, we adopt the filter-sampler structure for the generalized sampler, and the uspampler-filter-hold structure for the generalized hold. Under these implementation constraints, we reformulate the problem as a standard discrete-time $H^{\infty}$ control problem by using the discrete-time lifting technique. A simulation result is shown to illustrate the effectiveness of the proposed method.