Heterogeneously-Distributed Joint Radar Communications: Bayesian Resource Allocation

TL;DR

This paper proposes a Bayesian resource allocation method for heterogeneously-distributed joint radar-communication systems to enhance target tracking and communication throughput, addressing spectrum scarcity challenges.

Contribution

It introduces a Bayesian CRB-based optimization framework and an alternating descent-ascent algorithm for resource allocation in heterogeneous radar-communication networks.

Findings

Proposed scheme improves multi-target tracking performance.

Achieves balanced throughput for communication users.

Numerical results validate effectiveness of the allocation method.

Abstract

Due to spectrum scarcity, the coexistence of radar and wireless communication has gained substantial research interest recently. Among many scenarios, the heterogeneouslydistributed joint radar-communication system is promising due to its flexibility and compatibility of existing architectures. In this paper, we focus on a heterogeneous radar and communication network (HRCN), which consists of various generic radars for multiple target tracking (MTT) and wireless communications for multiple users. We aim to improve the MTT performance and maintain good throughput levels for communication users by a well-designed resource allocation. The problem is formulated as a Bayesian Cram\'er-Rao bound (CRB) based minimization subjecting to resource budgets and throughput constraints. The formulated nonconvex problem is solved based on an alternating descent-ascent approach. Numerical results demonstrate the efficacy of the proposed allocation scheme for this heterogeneous network.