Beamforming Design for Integrated Sensing and Communications Using Uplink-Downlink Duality

TL;DR

This paper introduces a new optimization framework for beamforming in integrated sensing and communication systems, significantly reducing computational complexity by leveraging uplink-downlink duality and a tractable reformulation.

Contribution

It develops a novel, computationally efficient beamforming design method using uplink-downlink duality for integrated sensing and communication systems.

Findings

Reduced computational complexity compared to existing methods

Provides a new understanding of Cramér-Rao bound optimization

Develops an efficient algorithm for beamforming design

Abstract

This paper presents a novel optimization framework for beamforming design in integrated sensing and communication systems where a base station seeks to minimize the Bayesian Cram\'er-Rao bound of a sensing problem while satisfying quality of service constraints for the communication users. Prior approaches formulate the design problem as a semidefinite program for which acquiring a beamforming solution is computationally expensive. In this work, we show that the computational burden can be considerably alleviated. To achieve this, we transform the design problem to a tractable form that not only provides a new understanding of Cram\'er-Rao bound optimization, but also allows for an uplink-downlink duality relation to be developed. Such a duality result gives rise to an efficient algorithm that enables the beamforming design problem to be solved at a much lower complexity as compared to the-state-of-the-art methods.