Energy Efficiency for Massive MIMO Integrated Sensing and Communication Systems

Energy efficiency; integrated sensing and communication; massive multiple-input multiple-output; orthogonal frequency-division multiplexing; zero-forcing; Communications systems; Energy; Integrated sensing; Integrated sensing and communication; Massive multiple-input multiple-output; Multiple inputs; Multiple outputs; Orthogonal frequency-division multiplexing; Sensing systems; Zero-forcing; Computer Networks and Communications; Electrical and Electronic Engineering

Abstract :

[en] 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 × 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.

Disciplines :

Computer science

Author, co-author :

Nguyen, Huy T. ; Van Lang University, Smart and Autonomous Systems Research Group, Faculty of Information Technology, School of Technology, Ho Chi Minh City, Viet Nam

Nguyen, Van-Dinh ; Trinity College Dublin, School of Computer Science and Statistics, Ireland

Nguyen, Nhan Thanh ; University of Oulu, Centre for Wireless Communications, Finland

Luong, Nguyen Cong ; PHENIKAA University, Phenikaa School of Computing, Hanoi, Viet Nam

Bao, Vo-Nguyen Quoc ; Van Lang University, Faculty of Information Technology, School of Technology, Ho Chi Minh City, Viet Nam

Ngo, Hien Quoc ; Queen’s University Belfast, Centre for Wireless Innovation (CWI), Belfast, United Kingdom

Niyato, Dusit ; Nanyang Technological University, College of Computing and Data Science, Singapore

CHATZINOTAS, Symeon ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom ; Kyung Hee University, College of Electronics & Information, Yongin-si, South Korea

External co-authors :

yes

Language :

English

Title :

Energy Efficiency for Massive MIMO Integrated Sensing and Communication Systems

Publication date :

17 September 2025

Journal title :

IEEE Journal on Selected Areas In Communications

ISSN :

0733-8716

Publisher :

Institute of Electrical and Electronics Engineers Inc.

Pages :

1-1

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

http://xplorestaging.ieee.org/ielx8/49/5594698/11168825.pdf?arnumber=11168825

Funding text :

This research is supported by the project Sustainable Multifunctional Satellite Systems (SMS2) funded by Fonds National de la Recherche (FNR) under Contract C24/IS/18957132/SMS2, the Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.02-2023.43, the Research Council of Finland through 6G Flagship Program (grant no. 369116); project DIRECTION (grant no. 354901); project DYNAMICS (grant no. 367702), and project S6GRAN (grant no. 370561); CHIST-ERA through the project PASSIONATE (grant number 359817); the U.K. Research and Innovation Future Leaders Fellowships under Grant MR/X010635/1; a research grant from the Department for the Economy Northern Ireland under the US-Ireland R&D Partnership Programme; Seatrium New Energy Laboratory, Singapore Ministry of Education (MOE) Tier 1 (RG87/22 and RG24/24), the NTU Centre for Computational Technologies in Finance (NTU-CCTF), and the RIE2025 Industry Alignment Fund - Industry Collaboration Projects (IAF-ICP) (Award I2301E0026), administered by A*STAR. (Corresponding author: Van-Dinh Nguyen) H. T. Nguyen is with Smart and Autonomous Systems Research Group, Faculty of Information Technology, School of Technology, Van Lang University, Ho Chi Minh City, 70000, Vietnam. (e-mail: huy.nt@vlu.edu.vn).

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since 27 November 2025

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