Beamforming Towards Seamless Sensing Coverage for Cellular Integrated Sensing and Communication

TL;DR

This paper proposes a beamforming optimization approach for 6G cellular networks to achieve seamless sensing coverage while satisfying multiple users' communication requirements, addressing the challenge of differing coverage needs between sensing and communication.

Contribution

It introduces a novel optimization framework for joint sensing and communication coverage, including a closed-form solution for simple cases and an efficient algorithm for complex scenarios.

Findings

Achieves seamless sensing coverage in the prescribed region.

Guarantees communication requirements of multiple UEs.

Demonstrates effectiveness through numerical simulations.

Abstract

The sixth generation (6G) mobile communication networks are expected to offer a new paradigm of cellular integrated sensing and communication (ISAC). However, due to the intrinsic difference between sensing and communication in terms of coverage requirement, current cellular networks that are deliberately planned mainly for communication coverage are difficult to achieve seamless sensing coverage. To address this issue, this paper studies the beamforming optimization towards seamless sensing coverage for a basic bi-static ISAC system, while ensuring that the communication requirements of multiple users equipment (UEs) are satisfied. Towards this end, an optimization problem is formulated to maximize the worst-case sensing signal-to-noise ratio (SNR) in a prescribed coverage region, subject to the signal-to-interference-plus-noise ratio (SINR) requirement for each UE. To gain some insights, we first investigate the special case with one single UE and one single sensing point, for which a closed-from expression of the optimal beamforming is obtained. For the general case with multiple communication UEs and contiguous regional sensing coverage, an efficient algorithm based on successive convex approximation (SCA) is proposed to solve the non-convex beamforming optimization problem. Numerical results demonstrate that the proposed design is able to achieve seamless sensing coverage in the prescribed region, while guaranteeing the communication requirements of the UEs.