Access Management in Joint Sensing and Communication Systems: Efficiency versus Fairness

TL;DR

This paper addresses bandwidth allocation in distributed joint sensing and communication systems, proposing algorithms to optimize efficiency and fairness, and demonstrating near-optimal solutions through simulations.

Contribution

It introduces a non-convex optimization framework for bandwidth allocation in DJSC systems and proposes approximation and heuristic algorithms with proven performance bounds.

Findings

Proposed algorithms achieve near-optimal solutions in simulations.

The approximation algorithm guarantees an accuracy bound of ε.

Heuristic algorithm offers lower complexity with competitive performance.

Abstract

In this paper, we consider a distributed joint sensing and communication (DJSC) system in which multiple radar sensors are deployed. Each sensor is equipped with a sensing function and a communication function, and thus it is a JSC node. The JSC nodes are able to perform sensing their surrounding environments, e.g., weather conditions or available spectrum. Furthermore, they can cooperatively detect and track a common target. The information, i.e., of the environment and target, collected by the JSC nodes is transmitted to a base station (BS) for further processing. As such, different aspects of the target to be viewed simultaneously, which significantly improves the performance of the target detection and tracking. However, both the sensing function and communication function require a certain amount of bandwidth for their operations, and deploying multiple JSC nodes may consume a large amount of bandwidth. Therefore, we investigate the bandwidth allocation problem for the DJSC system. In particular, we aim to optimize the bandwidth allocation to the sensing function and the communication function of the JSC nodes. To improve the allocation efficiency while benefiting the spatial diversity advantage of the DJSC systems, the objective is to maximize the sum of sensing performances, i.e., estimation rates, communication performances, i.e., communication data rates, and fairnesses of all the users. The optimization problem is non-convex and difficult to be solved. For this, we propose a fully polynomial time approximation algorithm, and we prove that the approximation algorithm can guarantee a near-optimal solution with an accuracy bound of $\epsilon$. Furthermore, we propose to use a heuristic algorithm with lower complexity. The simulation results show that both the proposed algorithms are able to achieve the solutions close to the optimum in a computationally efficient fashion.