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

Area of Science:

• Electrical Engineering
• Computer Science
• Signal Processing

Background:

• Integrated Sensing and Communication (ISAC) systems face performance trade-offs between sensing and communication in urban settings.
• Conventional optimization methods struggle with local optima and sub-optimal results for joint sensing-communication problems.
• Existing solutions often fail to effectively balance conflicting objectives in ISAC.

Purpose of the Study:

• To propose a novel hierarchical beamforming optimization solution for ISAC-enabled Roadside Units (RSUs).
• To address the performance trade-offs between target sensing and multi-user communication in complex urban environments.
• To jointly optimize communication SINR and sensing SCNR for enhanced accuracy and reliability.

Main Methods:

• Decomposed the joint sensing-communication problem into a two-level "leader-follower" hierarchical structure.
• Employed a max-min strategy with the bisection method in the leader layer to solve communication beamforming.
• Utilized Semi-Definite Programming (SDP) in the follower layer to maximize sensing SCNR, incorporating spatiotemporal resource isolation.

Main Results:

• The hierarchical optimization effectively balances communication SINR and sensing SCNR.
• The proposed method demonstrates simultaneous improvement in sensing accuracy and communication reliability.
• Simulation results validate the effectiveness of the joint optimization strategy.

Conclusions:

• The hierarchical beamforming approach offers a viable solution for ISAC systems in urban environments.
• This method effectively mitigates the inherent trade-offs between sensing and communication performance.
• The proposed technique enhances both the sensing capabilities and communication reliability of ISAC-enabled RSUs.