English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper proposes a novel scalable reinforcement learning approach forsimultaneous routing and spectrum access in wireless ad-hoc networks. In mostprevious works on reinforcement learning for network optimization, the networktopology is assumed to be fixed, and a different agent is trained for eachtransmission node -- this limits scalability and generalizability. Further,routing and spectrum access are typically treated as separate tasks. Moreover,the optimization objective is usually a cumulative metric along the route,e.g., number of hops or delay. In this paper, we account for the physical-layersignal-to-interference-plus-noise ratio (SINR) in a wireless network andfurther show that bottleneck objective such as the minimum SINR along the routecan also be optimized effectively using reinforcement learning. Specifically,we propose a scalable approach in which a single agent is associated with eachflow and makes routing and spectrum access decisions as it moves along thefrontier nodes. The agent is trained according to the physical-layercharacteristics of the environment using a novel rewarding scheme based on theMonte Carlo estimation of the future bottleneck SINR. It learns to avoidinterference by intelligently making joint routing and spectrum allocationdecisions based on the geographical location information of the neighbouringnodes.

Scalable Deep Reinforcement Learning for Routing and Spectrum Access in Physical Layer