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 then developed. In Phase I, each CCN autonomously classifies ISAC services as either local or federated and partitions its resources into dedicated and shared segments. In Phase II, each CCN employs its dedicated resources for local ISAC services, while the aggregated CFN consolidates shared resources from its constituent CCNs to cooperatively deliver federated services. Furthermore, we design a utility-to-signaling ratio (USR) to quantify the tradeoff between sensing/communication utility and signaling overhead. Consequently, a USR maximization problem is formulated by jointly optimizing the network topology (i.e., service classification and CCN aggregation) and the allocation of dedicated and shared resources. However, this problem is challenging due to its distributed optimization nature and the absence of complete channel state information. To address this problem efficiently, we propose a multi-agent deep reinforcement learning (MADRL) framework with centralized training and decentralized execution.

翻译：传统移动网络，包括局域化的以小区为中心的网络（CCN）和协作无小区网络（CFN），均建立在刚性网络拓扑之上。然而，由于时-空分布的异构通感一体化（ISAC）服务需求与可用无线电资源之间的对齐日益困难，这两种架构均不足以灵活地支持分布式通感一体化服务。本文针对分布式通感一体化服务供给提出了一种弹性网络拓扑（ENT），其中多个共存的局域化CCN可动态聚合为具有扩展边界的CFN，以实现联邦式网络运行。该拓扑通过弹性编排局域CCN与联邦CFN的边界，在信令开销与分布式资源利用之间取得平衡，从而支持高效的ISAC服务供给。随后，我们开发了一种两阶段操作协议。在第一阶段，每个CCN自主地将ISAC服务分类为局域服务或联邦服务，并将其资源划分为专用资源段和共享资源段。在第二阶段，每个CCN利用其专用资源提供局域ISAC服务，而聚合后的CFN则整合其组成CCN的共享资源，以协作方式提供联邦服务。此外，我们设计了效用-信令比（USR）来量化感知/通信效用与信令开销之间的权衡。据此，通过联合优化网络拓扑（即服务分类与CCN聚合）以及专用和共享资源的分配，提出了一个USR最大化问题。然而，该问题因其分布式优化特性以及缺乏完整信道状态信息而具有挑战性。为高效解决此问题，我们提出了一种采用集中式训练与分布式执行的多智能体深度强化学习（MADRL）框架。