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

With the introduction of caching capabilities into small cell networks(SCNs), new backaul management mechanisms need to be developed to prevent thepredicted files that are downloaded by the at the small base stations (SBSs) tobe cached from jeopardizing the urgent requests that need to be served via thebackhaul. Moreover, these mechanisms must account for the heterogeneity of thebackhaul that will be encompassing both wireless backhaul links at variousfrequency bands and a wired backhaul component. In this paper, theheterogeneous backhaul management problem is formulated as a minority game inwhich each SBS has to define the number of predicted files to download, withoutaffecting the required transmission rate of the current requests. For theformulated game, it is shown that a unique fair proper mixed Nash equilibrium(PMNE) exists. Self-organizing reinforcement learning algorithm is proposed andproved to converge to a unique Boltzmann-Gibbs equilibrium which approximatesthe desired PMNE. Simulation results show that the performance of the proposedapproach can be close to that of the ideal optimal algorithm while itoutperforms a centralized greedy approach in terms of the amount of data thatis cached without jeopardizing the quality-of-service of current requests.

The 5G Cellular Backhaul Management Dilemma: To Cache or to Serve