Learning-Enabled Elastic Network Topology for Distributed ISAC Service Provisioning
Jie Chen, Xianbin Wang

TL;DR
This paper introduces an elastic network topology for distributed ISAC services, enabling dynamic aggregation of localized networks into federated networks to optimize resource use and service provisioning.
Contribution
It proposes a novel elastic topology and a multi-agent deep reinforcement learning framework for flexible, efficient distributed ISAC service provisioning.
Findings
The USR metric effectively quantifies the tradeoff between utility and signaling overhead.
The MADRL framework optimizes network topology and resource allocation in a distributed setting.
The proposed approach enhances the flexibility and efficiency of ISAC service provisioning.
Abstract
Conventional mobile networks, including both localized cell-centric and cooperative cell-free networks (CCN/CFN), are built upon rigid network topologies. However, neither architecture is adequate to flexibly support distributed integrated sensing and communication (ISAC) services, due to the increasing difficulty of aligning spatiotemporally distributed heterogeneous service demands with available radio resources. In this paper, we propose an elastic network topology (ENT) for distributed ISAC service provisioning, where multiple co-existing localized CCNs can be dynamically aggregated into CFNs with expanded boundaries for federated network operation. This topology elastically orchestrates localized CCN and federated CFN boundaries to balance signaling overhead and distributed resource utilization, thereby enabling efficient ISAC service provisioning. A two-phase operation protocol is…
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