Coordinated Decentralized Resource Optimization for Cell-Free ISAC Systems

TL;DR

This paper introduces two decentralized algorithms for resource optimization in cell-free ISAC systems, enhancing scalability and reducing fronthaul overhead for future 6G networks.

Contribution

It presents novel decentralized beamforming and power allocation algorithms tailored for cell-free ISAC, addressing scalability issues of centralized solutions.

Findings

Decentralized algorithms reduce fronthaul overhead.

Algorithms achieve comparable performance to centralized methods.

Simulation validates practicality for 6G cell-free networks.

Abstract

Integrated Sensing and Communication (ISAC) is emerging as a key enabler for 6G wireless networks, allowing the joint use of spectrum and infrastructure for both communication and sensing. While prior ISAC solutions have addressed resource optimization, including power allocation, beamforming, and waveform design, they often rely on centralized architectures with full network knowledge, limiting their scalability in distributed systems. In this paper, we propose two coordinated decentralized optimization algorithms for beamforming and power allocation tailored to cell-free ISAC networks. The first algorithm employs locally designed fixed beamformers at access points (APs), combined with a centralized power allocation scheme computed at a central server (CS). The second algorithm jointly optimizes beamforming and power control through a fully decentralized consensus ADMM framework. Both approaches rely on local information at APs and limited coordination with the CS. Simulation results obtained using our proposed Python-based simulation framework evaluate their fronthaul overhead and system-level performance, demonstrating their practicality for scalable ISAC deployment in decentralized, cell-free architectures.