Energy Efficiency for Massive MIMO Integrated Sensing and Communication Systems

2025-09-12

AI Summary

This paper addresses energy efficiency (EE) maximization for OFDM-based massive MIMO integrated sensing and communication (ISAC) systems under joint constraints of communication rate and sensing accuracy—quantified by the Cramér–Rao bound (CRB). We derive a closed-form EE expression and propose a unified optimization framework integrating zero-forcing transmission and mono-static radar sensing, jointly exploiting multi-carrier channel diversity and spatial beamforming. A novel initialization strategy is introduced, combining the Dinkelbach algorithm with successive convex approximation (SCA) for efficient power allocation. Theoretical analysis reveals that, in high-spectral-efficiency regimes, sensing-related power consumption dominates EE degradation. Simulation results demonstrate that the proposed method significantly outperforms baseline schemes: even under sensing-dominant, high-spectral-efficiency conditions, unoptimized designs incur a 16.7% EE loss, thereby validating the necessity and effectiveness of joint communication-sensing optimization.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Constraint 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

This paper explores the energy efficiency (EE) of integrated sensing and communication (ISAC) systems employing massive multiple-input multiple-output (mMIMO) techniques to leverage spatial beamforming gains for both communication and sensing. We focus on an mMIMO-ISAC system operating in an orthogonal frequency-division multiplexing setting with a uniform planar array, zero-forcing downlink transmission, and mono-static radar sensing to exploit multi-carrier channel diversity. By deriving closed-form expressions for the achievable communication rate and Cramér-Rao bounds (CRBs), we are able to determine the overall EE in closed-form. A power allocation problem is then formulated to maximize the system's EE by balancing communication and sensing efficiency while satisfying communication rate requirements and CRB constraints. Through a detailed analysis of CRB properties, we reformulate the problem into a more manageable form and leverage Dinkelbach's and successive convex approximation (SCA) techniques to develop an efficient iterative algorithm. A novel initialization strategy is also proposed to ensure high-quality feasible starting points for the iterative optimization process. Extensive simulations demonstrate the significant performance improvement of the proposed approach over baseline approaches. Results further reveal that as communication spectral efficiency rises, the influence of sensing EE on the overall system EE becomes more pronounced, even in sensing-dominated scenarios. Specifically, in the high $ω$ regime of $2 imes 10^{-3}$, we observe a 16.7% reduction in overall EE when spectral efficiency increases from $4$ to $8$ bps/Hz, despite the system being sensing-dominated.

Problem

• Optimizing energy efficiency in massive MIMO integrated sensing and communication systems
• Balancing communication and sensing performance through power allocation
• Developing iterative algorithms to maximize system energy efficiency

Innovation

• Massive MIMO with orthogonal frequency-division multiplexing
• Closed-form EE expressions with Cramér-Rao bounds
• Dinkelbach and SCA iterative optimization algorithm