Optimal Sensing Policy With Interference-Model Uncertainty

TL;DR

This paper develops an optimal sensing policy for interference management in a half-duplex system with unknown interference parameters, balancing sensing and communication to maximize data rate.

Contribution

It introduces a Markov decision process framework for the problem and derives computationally efficient algorithms for optimal sensing and communication policies.

Findings

Optimal policies can be computed with reduced complexity.

Sensing improves interference parameter estimates and link adaptation.

The approach outperforms traditional methods assuming known interference parameters.

Abstract

This paper considers a half-duplex scenario where an interferer behaves according to a parametric model but the values of the model parameters are unknown. We explore the necessary number of sensing steps to gather sufficient knowledge about the interferer's behavior. With more sensing steps, the reliability of the model-parameter estimates is improved, thereby enabling more effective link adaptation. However, in each time slot, the communication system experiencing interference must choose between sensing and communication. Thus, we propose to investigate the optimal policy for maximizing the expected sum communication data rate over a finite-time communication. This approach contrasts with most studies on interference management in the literature, which assume that the parameters of the interference model are perfectly known. We begin by showing that the problem under consideration can be modeled within the framework of a Markov decision process (MDP). Following this, we demonstrate that both the optimal open-loop and optimal closed-loop policies can be determined with reduced computational complexity compared to the standard backward-induction algorithm.