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/

Zendy Plus

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

Zendy Tools

Zendy Open

We consider a cognitive radio network with multiple primary users (PUs) andone secondary user (SU), where a spectrum server is utilized for spectrumsensing and scheduling the SU to transmit over one of the PU channelsopportunistically. One practical yet challenging scenario is when \textit{both}the PU occupancy and the channel fading vary over time and exhibit temporalcorrelations. Little work has been done for exploiting such temporal memory inthe channel fading and the PU occupancy simultaneously for opportunisticspectrum scheduling. A main goal of this work is to understand the intricatetradeoffs resulting from the interactions of the two sets of system states -the channel fading and the PU occupancy, by casting the problem as a partiallyobservable Markov decision process. We first show that a simple greedy policyis optimal in some special cases. To build a clear understanding of thetradeoffs, we then introduce a full-observation genie-aided system, where thespectrum server collects channel fading states from all PU channels. Thegenie-aided system is used to decompose the tradeoffs in the original systeminto multiple tiers, which are examined progressively. Numerical examplesindicate that the optimal scheduler in the original system, with observation onthe scheduled channel only, achieves a performance very close to thegenie-aided system. Further, as expected, the optimal policy in the originalsystem significantly outperforms randomized scheduling, pointing to the meritof exploiting the temporal correlation structure in both channel fading and PUoccupancy.

Exploiting Channel Correlation and PU Traffic Memory for Opportunistic Spectrum Scheduling