Cramér-Rao Bound Optimization for Joint Radar-Communication Beamforming

In this paper, we propose multi-input multi-output (MIMO) beamforming designs towards joint radar sensing and multi-user communications. We employ the Cramér-Rao bound (CRB) as a performance metric of target estimation, under both point and extended target scenarios. We then propose minimizing the CRB of radar sensing while guaranteeing a pre-defined level of signal-to-interference-plus-noise ratio (SINR) for each communication user. For the single-user scenario, we derive a closed form for the optimal solution for both cases of point and extended targets. For the multi-user scenario, we show that both problems can be relaxed into semidefinite programming by using the semidefinite relaxation approach, and prove that the global optimum can be generally obtained. Finally, we demonstrate numerically that the globally optimal solutions are reachable via the proposed methods, which provide significant gains in target estimation performance over state-of-the-art benchmarks.

I. Introduction

Wireless sensors and communication systems have shaped modern society in profound ways. 5G and beyond network is envisioned as an enabler for many emerging applications, such as intelligent connected vehicles and remote health-care. These applications demand wireless connectivity with tremendously increased data rates, substantially reduced latency, high-accuracy localization capability and support for massive devices [1], [2]. Indeed, in many location-aware services and applications, sensing and communications are recognized as a pair of intertwined functionalities, which are often required to operate simultaneously [3]–[5].