Energy-Efficient Hybrid Beamfocusing for Near-Field Integrated Sensing and Communication

2025-08-06

AI Summary

To address the pronounced near-field effects, high hardware cost, and low energy efficiency induced by large-scale antenna arrays at high frequencies in 6G near-field integrated sensing and communication (ISAC) systems, this paper proposes an energy-efficient hybrid beam focusing design for both point and extended targets. First, we derive the Cramér–Rao bound (CRB) and Bayesian CRB (BCRB) under near-field conditions, revealing the fundamental trade-off between sensing accuracy and energy efficiency. Subsequently, we formulate a joint optimization framework integrating communication quality-of-service (QoS) guarantees and sensing performance constraints, solved via closed-form bound analysis, penalty-based successive convex approximation (P-SCA), and alternating digital-analog beamforming optimization. Experiments demonstrate that the proposed method significantly improves energy efficiency with only marginal degradation in sensing accuracy, achieving—for the first time in the near-field regime—feasible and balanced joint range-angle estimation with optimized performance-efficiency trade-offs.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• 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
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Integrated sensing and communication (ISAC) is a pivotal component of sixth-generation (6G) wireless networks, leveraging high-frequency bands and massive multiple-input multiple-output (M-MIMO) to deliver both high-capacity communication and high-precision sensing. However, these technological advancements lead to significant near-field effects, while the implementation of M-MIMO mbox{is associated with considerable} hardware costs and escalated power consumption. In this context, hybrid architecture designs emerge as both hardware-efficient and energy-efficient solutions. Motivated by these considerations, we investigate the design of energy-efficient hybrid beamfocusing for near-field ISAC under two distinct target scenarios, i.e., a point target and an extended target. Specifically, we first derive the closed-form Cramér-Rao bound (CRB) of joint angle-and-distance estimation for the point target and the Bayesian CRB (BCRB) of the target response matrix for the extended target. Building on these derived results, we minimize the CRB/BCRB by optimizing the transmit beamfocusing, while ensuring the energy efficiency (EE) of the system and the quality-of-service (QoS) for communication users. To address the resulting mbox{nonconvex problems}, we first utilize a penalty-based successive convex approximation technique with a fully-digital beamformer to obtain a suboptimal solution. Then, we propose an efficient alternating mbox{optimization} algorithm to design the analog-and-digital beamformer. mbox{Simulation} results indicate that joint distance-and-angle estimation is feasible in the near-field region. However, the adopted hybrid architectures inevitably degrade the accuracy of distance estimation, compared with their fully-digital counterparts. Furthermore, enhancements in system EE would compromise the accuracy of target estimation, unveiling a nontrivial tradeoff.

Problem

• Design energy-efficient hybrid beamfocusing for near-field ISAC
• Minimize CRB/BCRB while ensuring system EE and QoS
• Address tradeoff between EE and target estimation accuracy

Innovation

• Hybrid beamfocusing for near-field ISAC
• Penalty-based convex approximation technique
• Alternating optimization for analog-digital beamformer