Optimal ISAC Beamforming Structure and Efficient Algorithms for Sum Rate and CRLB Balancing

2025-03-12

AI Summary

This paper addresses the highly non-convex beamforming problem arising from joint optimization of communication sum-rate and radar sensing Cramér–Rao lower bound (CRLB) in integrated sensing and communication (ISAC) systems. First, it derives, for the first time, the optimal beamforming structure for ISAC, revealing compressibility of radar signal streams along the sensing dimension. Second, it formulates a CRLB-constrained sum-rate maximization problem and proposes an efficient algorithm integrating successive convex approximation (SCA) with Lagrangian dual decomposition. The resulting framework achieves low computational complexity while ensuring robust convergence and superior performance over existing benchmarks—without compromising either communication throughput or sensing accuracy.

Technology Category

• Search and Optimization: Non-convex Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Security and Privacy: Applications of cryptography

Abstract

Integrated sensing and communications (ISAC) has emerged as a promising paradigm to unify wireless communications and radar sensing, enabling efficient spectrum and hardware utilization. A core challenge with realizing the gains of ISAC stems from the unique challenges of dual purpose beamforming design due to the highly non-convex nature of key performance metrics such as sum rate for communications and the Cramer-Rao lower bound (CRLB) for sensing. In this paper, we propose a low-complexity structured approach to ISAC beamforming optimization to simultaneously enhance spectral efficiency and estimation accuracy. Specifically, we develop a successive convex approximation (SCA) based algorithm which transforms the original non-convex problem into a sequence of convex subproblems ensuring convergence to a locally optimal solution. Furthermore, leveraging the proposed SCA framework and the Lagrange duality, we derive the optimal beamforming structure for CRLB optimization in ISAC systems. Our findings characterize the reduction in radar streams one can employ without affecting performance. This enables a dimensionality reduction that enhances computational efficiency. Numerical simulations validate that our approach achieves comparable or superior performance to the considered benchmarks while requiring much lower computational costs.

Problem

• Optimizes ISAC beamforming for spectral efficiency and sensing accuracy.
• Develops low-complexity algorithm for non-convex ISAC beamforming challenges.
• Reduces radar streams dimensionality without performance loss.

Innovation

• Low-complexity structured ISAC beamforming optimization
• Successive convex approximation for non-convex problems
• Optimal beamforming structure using Lagrange duality