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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Owing to the ever-increasing demand in wireless spectrum, Cognitive Radio(CR) was introduced as a technique to attain high spectral efficiency. As thenumber of secondary users (SUs) connecting to the cognitive radio network is onthe rise, there is an imminent need for centralized algorithms that providehigh throughput and energy efficiency of the SUs while ensuring minimuminterference to the licensed users. In this work, we propose a multi-stagealgorithm that - 1) effectively assigns the available channel to the SUs, 2)employs a non-parametric learning framework to estimate the primary trafficdistribution to minimize sensing, and 3) proposes an adaptive framework toensure that the collision to the primary user is below the specified threshold.We provide comprehensive empirical validation of the method with otherapproaches.

A Centralized Multi-stage Non-parametric Learning Algorithm for Opportunistic Spectrum Access