An elastic network topology for distributed ISAC dynamically aggregates cell-centric networks into cell-free ones and uses multi-agent deep reinforcement learning to maximize a utility-to-signaling ratio by optimizing service classification and resource allocation.
Fundamental crb-rate tradeoff in multi-antenna ISAC systems with information multicasting and multi-target sensing,
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Learning-Enabled Elastic Network Topology for Distributed ISAC Service Provisioning
An elastic network topology for distributed ISAC dynamically aggregates cell-centric networks into cell-free ones and uses multi-agent deep reinforcement learning to maximize a utility-to-signaling ratio by optimizing service classification and resource allocation.