Learning To Communicate Over An Unknown Shared Network
Shivangi Agarwal, Adi Asija, Sanjit K. Kaul, Arani Bhattacharya, Saket Anand

TL;DR
This paper introduces a DRL-based policy, QNet, enabling agents to learn when to communicate over shared wireless networks with limited feedback, generalizing across various network conditions without specific training.
Contribution
The paper presents a novel simulation-to-real framework for training a network-agnostic DRL policy, QNet, that adapts to diverse wireless network scenarios for efficient agent communication.
Findings
QNet outperforms baseline policies in diverse network conditions.
The approach generalizes well to different numbers of agents and network types.
Experiments show effective communication decision-making in WiFi and cellular networks.
Abstract
As robots (edge-devices, agents) find uses in an increasing number of settings and edge-cloud resources become pervasive, wireless networks will often be shared by flows of data traffic that result from communication between agents and corresponding edge-cloud. In such settings, agent communicating with the edge-cloud is unaware of state of network resource, which evolves in response to not just agent's own communication at any given time but also to communication by other agents, which stays unknown to the agent. We address challenge of an agent learning a policy that allows it to decide whether or not to communicate with its cloud node, using limited feedback it obtains from its own attempts to communicate, to optimize its utility. The policy generalizes well to any number of other agents sharing the network and must not be trained for any particular network configuration. Our…
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Taxonomy
TopicsIoT and Edge/Fog Computing · Age of Information Optimization · Wireless Networks and Protocols
