Green Cell-Free Massive MIMO for ISAC

QC 20260828

Abstract

Integrated sensing and communication (ISAC) has emerged as a key paradigm for future wireless networks, enabling communication infrastructures to support data transmission and environmental sensing within a unified framework. This integration, however, introduces new challenges in deployment, signal processing, and resource allocation. Cell-free massive multiple-input multiple-output (CF-mMIMO) networks, characterized by a large number of distributed access points (APs), provide a promising platform for ISAC. Through coordinated operation among distributed APs, CF-mMIMO systems can support flexible bi-static and multi-static sensing configurations, thereby avoiding the full-duplex requirement of conventional mono-static sensing systems. Moreover, the distributed AP architecture provides spatial diversity and multiplexing gains, making CF-mMIMO well-suited for advanced sensing and communication services.

Despite these advantages, integrating sensing into CF-mMIMO networks increases overall network power consumption and imposes additional demands on radio, fronthaul, and cloud-processing resources. Therefore, the effective realization of green CF-mMIMO ISAC requires joint system design and resource allocation frameworks that account for both sensing and communication requirements.

This thesis studies ISAC in CF-mMIMO systems, with a focus on efficient resource allocation, reliable sensing and communication, and end-to-end network power consumption. The main objective is to develop green CF-mMIMO ISAC frameworks that jointly design sensing and communication functionalities while addressing reliability, energy-efficiency, and scalability challenges.

The thesis first investigates power allocation for target detection in CF-mMIMO systems. By exploiting both communication signals and dedicated sensing signals, the work characterizes the trade-off between sensing and communication performance. Maximum a posteriori ratio test (MAPRT)-based detectors are developed to enable reliable target detection from signals received at distributed APs under both clutter-free and cluttered sensing environments.

Building on this foundation, the thesis extends the analysis to ultra-reliable low-latency communication (URLLC) scenarios, where sensing information is used to support target-aware actuation use cases. In such scenarios, sensing information must be delivered reliably and within stringent latency constraints. A joint power and blocklength optimization framework is proposed to minimize energy consumption across the radio and cloud domains. The results characterize the interplay among sensing performance, communication reliability, latency, and processing workload, highlighting the importance of jointly optimizing system parameters under strict quality-of-service requirements.

A central contribution of the thesis is the development of end-to-end network power models for CF-mMIMO ISAC systems. Unlike conventional approaches that focus primarily on transmit power, the proposed models incorporate radio, fronthaul, and cloud-processing power consumption. This enables a more comprehensive evaluation of energy efficiency in ISAC networks and reveals the impact of sensing-related processing and signaling overhead on the total network power consumption.

To address scalability challenges in large-scale deployments, the thesis further investigates distributed sensing architectures. Two sensing-information levels, namely fully-informed and partially-informed systems, are considered to capture different trade-offs among sensing accuracy, computational complexity, and fronthaul signaling overhead. A cross-layer optimization framework is then developed to jointly manage radio, fronthaul, and cloud resources, substantially reducing total network power consumption while maintaining reliable sensing and communication performance.

Overall, the results demonstrate that CF-mMIMO is a promising architecture for green ISAC by enabling flexible resource allocation, scalable sensing architectures, and significant energy savings. The proposed methods provide practical insights into the design of next-generation wireless networks that integrate sensing and communication in an energy-efficient and scalable manner.

Link to DiVA