Green Cell-Free Massive MIMO for ISAC: Joint Cloud, Fronthaul and Radio Resource Allocation
Abstract
In this paper, we develop a cross-layer end-to-end (E2E) resource orchestration framework for green cell-free massive multiple-input multiple-output (CF-mMIMO) integrated sensing and communication (ISAC) systems with distributed multi-target detection. We propose a distributed sensing approach in which receive access points (RX-APs) compute local test statistics, which are aggregated at the cloud using weights based on sensing interference and channel quality. We derive the local maximum a posteriori ratio test (MAPRT) detectors under fully informed and partially informed operation, representing different levels of transmit-signal information at the RX-APs. We further characterize their processing and fronthaul requirements and derive a network power model incorporating radio transmission, AP and cloud processing, and fronthaul infrastructure. We formulate a mixed-integer non-convex problem that jointly optimizes transmit powers, AP modes, user equipment (UE) and sensing-area associations, RX-AP assignments, and active fronthaul and cloud resources subject to communication, sensing, power, processing-capacity, and fronthaul constraints. A two-stage iterative algorithm based on Big-M reformulation, convex- concave programming, penalty-based relaxation, and structured discrete recovery is developed. Numerical results show that the proposed E2E framework reduces total power by up to 50% compared with transmit-power-only optimization and by approximately 11-17% compared with joint radio optimization under full coordination, while maintaining detection probabilities above 0:95.
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BibTeX
@misc{behdad2026green,
title = {{Green Cell-Free Massive MIMO for ISAC: Joint Cloud, Fronthaul and Radio Resource Allocation}},
author = {Zinat Behdad and Özlem Tuğfe Demir and Ki Won Sung and Pei Xiao and Cicek Cavdar},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2607.27778},
eprint = {2607.27778},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.27778},
}
RIS
TY - UNPB
TI - Green Cell-Free Massive MIMO for ISAC: Joint Cloud, Fronthaul and Radio Resource Allocation
AU - Zinat Behdad
AU - Özlem Tuğfe Demir
AU - Ki Won Sung
AU - Pei Xiao
AU - Cicek Cavdar
PY - 2026
DA - 2026/10/21
PB - arXiv
DO - 10.48550/arxiv.2607.27778
UR - https://arxiv.org/abs/2607.27778
AB - In this paper, we develop a cross-layer end-to-end (E2E) resource orchestration framework for green cell-free massive multiple-input multiple-output (CF-mMIMO) integrated sensing and communication (ISAC) systems with distributed multi-target detection. We propose a distributed sensing approach in which receive access points (RX-APs) compute local test statistics, which are aggregated at the cloud using weights based on sensing interference and channel quality. We derive the local maximum a posteriori ratio test (MAPRT) detectors under fully informed and partially informed operation, representing different levels of transmit-signal information at the RX-APs. We further characterize their processing and fronthaul requirements and derive a network power model incorporating radio transmission, AP and cloud processing, and fronthaul infrastructure. We formulate a mixed-integer non-convex problem that jointly optimizes transmit powers, AP modes, user equipment (UE) and sensing-area associations, RX-AP assignments, and active fronthaul and cloud resources subject to communication, sensing, power, processing-capacity, and fronthaul constraints. A two-stage iterative algorithm based on Big-M reformulation, convex- concave programming, penalty-based relaxation, and structured discrete recovery is developed. Numerical results show that the proposed E2E framework reduces total power by up to 50% compared with transmit-power-only optimization and by approximately 11-17% compared with joint radio optimization under full coordination, while maintaining detection probabilities above 0:95.
ER -