Tri-Hybrid Beamforming Design for integrated Sensing and Communications

2026-01-22

AI Summary

This work addresses the challenge of jointly optimizing energy efficiency for both communication and sensing performance in extremely large-scale antenna arrays. To this end, the paper proposes a novel triple-hybrid beamforming architecture tailored for integrated sensing and communication (ISAC) systems and, for the first time, applies it to programmable metasurface antenna scenarios. A multi-objective optimization framework is formulated to simultaneously maximize communication signal-to-noise ratio and sensing power toward target directions, subject to total power consumption and physical constraints. A closed-form iterative algorithm is devised to reduce computational complexity. Simulation results demonstrate that the proposed approach achieves significant improvements in spatial gain and energy efficiency compared to conventional hybrid beamforming schemes, with only a minor degradation in beam alignment accuracy.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Security and Privacy: Large-scale security measurements

Abstract

Tri-hybrid beamforming architectures have been proposed to enable energy-efficient communications systems in extra-largescale antenna arrays using low-cost programmable metasurface antennas. We study the tri-hybrid beamforming design for integrated sensing and communications (ISAC) to improve both communications and sensing performances. Specifically, we formulate a multi-objective optimization problem that balances communications signal-to-noise ratio (SNR) and the sensing power at a target direction, subject to constraints on the total power consumption and physical limitations inherent to the trihybrid beamforming architecture. We develop an efficient iterative algorithm in which the variables are updated in a closed form at each iteration, leading to a low-complexity and fast-execution design. Numerical results show that the tri-hybrid architecture improves spatial gain and energy efficiency, though with reduced beam alignment capability compared to conventional hybrid beamforming architectures.

Problem

• tri-hybrid beamforming
• integrated sensing and communications
• multi-objective optimization
• energy efficiency
• spatial gain

Innovation

• tri-hybrid beamforming
• integrated sensing and communications
• metasurface antennas
• multi-objective optimization
• energy efficiency