Green Cell-Free Massive MIMO for ISAC: Joint Cloud, Fronthaul and Radio Resource Allocation

2026-07-30

AI Summary

This work addresses the cross-domain high energy consumption induced by integrated sensing and communication (ISAC) in cell-free massive MIMO (CF-mMIMO) systems by proposing the first end-to-end green resource co-optimization framework tailored for CF-mMIMO ISAC. The approach introduces a distributed sensing architecture wherein each receiving access point (AP) locally computes a detection statistic based on the maximum a posteriori probability ratio test (MAPRT) and uploads a weighted version to the cloud for fusion. Building upon this, the framework jointly optimizes transmit power, AP activation states, user-sensing associations, RX-AP assignments, and cloud/fronthaul resources. A mixed-integer nonconvex model is formulated and solved via a two-stage algorithm combining successive convex approximation with penalty-based relaxation. Compared to schemes optimizing only transmit power or wireless resources, the proposed method reduces total system power consumption by over 50% and 13–15%, respectively, while maintaining excellent detection performance.

Abstract

Cell-free massive MIMO (CF-mMIMO) combined with integrated sensing and communication (ISAC) is a promising architecture for future 6G networks, enabling new sensing-based applications. However, integrating sensing functionality increases power consumption across the radio, fronthaul, and cloud domains, which is not captured by conventional transmit power optimization approaches. In this paper, we develop a cross-layer end-to-end (E2E) optimization framework for green CF-mMIMO ISAC systems with distributed multi-target detection. We propose a distributed sensing approach in which receive access points (RX-APs) compute local test statistics and forward them to the cloud for aggregation via a weighted combination strategy. We derive maximum a posteriori ratio test (MAPRT) detectors under fully informed (FIS) and partially informed (PIS) scenarios, capturing different levels of side information available at the RX-APs. We formulate a joint optimization problem that minimizes total network power consumption by jointly optimizing transmit power allocation, AP operation modes, communication user and sensing associations, RX-AP assignments, and cloud/fronthaul resources, subject to communication and sensing constraints. The resulting mixed-integer non-convex problem is solved via a two-stage iterative algorithm based on successive convex approximation and penalty-based relaxation. Numerical results demonstrate that the proposed E2E framework significantly reduces total power consumption compared to benchmark schemes, achieving more than 50% savings over transmit-power-only optimization and approximately 13-15% over radio optimization, while maintaining competitive detection performance.

Problem

• Cell-free massive MIMO
• Integrated Sensing and Communication
• Power Consumption
• Resource Allocation
• Green Networks

Innovation

• Cell-free Massive MIMO
• Integrated Sensing and Communication (ISAC)
• Cross-layer Optimization
• Distributed Sensing
• Energy Efficiency