Hierarchical Beamforming Optimization for ISAC-Enabled RSU Systems in Complex Urban Environments

TL;DR

This paper introduces a hierarchical beamforming method to balance sensing and communication performance in complex urban environments using ISAC-enabled roadside units.

Contribution

A hierarchical beamforming optimization solution is proposed to jointly optimize sensing and communication performance in ISAC-enabled RSU systems.

Findings

The proposed method uses a two-level optimization structure to balance sensing and communication objectives.

Simulation results show improved sensing accuracy and communication reliability in complex urban settings.

A spatiotemporal resource isolation mechanism enhances performance by projecting sensing beams into the communication channel's null space.

Abstract

Integrated Sensing and Communication (ISAC)-enabled Roadside Units (RSUs) encounter significant performance trade-offs between target sensing and multi-user communication in complex urban environments, where conventional optimization methods are prone to converging to local optima and joint optimization methods often yield sub-optimal results due to conflicting objectives. To address the challenge of trade-off between sensing and communication performance, this paper proposes a hierarchical beamforming optimization solution designed to tackle joint sensing–communication problems in such scenarios. The overall optimization problem is decomposed into a two-level “leader-follower” structure. In the leader layer, we introduce a max–min strategy based on the bisection method to transform the non-convex Signal-to-Interference-plus-Noise Ratio (SINR) optimization problem into a second-order cone constraint problem and solve the communication beamforming vector. In the follower layer, the Signal-to-Clutter-plus-Noise Ratio (SCNR) maximization problem is converted into a Semi-Definite Programming (SDP) problem solved via the CVX toolbox. Additionally, we introduce a “spatiotemporal resource isolation” mechanism to project the sensing beam onto the null space of the communication channel. The hierarchical optimization solution jointly optimizes communication SINR and sensing SCNR, enabling an effective balance between sensing accuracy and communication reliability. Simulation results demonstrate the proposed method’s effectiveness in simultaneously improving sensing accuracy and communication reliability.

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