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

In this paper, we study the resource allocation in D2D underlaying cellularnetwork with uncertain channel state information (CSI). For satisfying thediversity requirements of different users, i.e. the minimum rate requirementfor cellular user and the reliability requirement for D2D user, we attempt tomaximize the cellular user's throughput whilst ensuring a chance constraint forD2D user. Then, a robust resource allocation framework is proposed for solvingthe highly intractable chance constraint about D2D reliability requirement,where the CSI uncertainties are represented as a deterministic set and thereliability requirement is enforced to hold for any uncertain CSI within it.Then, a symmetrical-geometry-based learning approach is developed to model theuncertain CSI into polytope, ellipsoidal and box. After that, we derive therobust counterpart of the chance constraint under these uncertainty sets as thecomputation convenient convex sets. To overcome the conservatism of thesymmetrical-geometry-based uncertainty sets, we develop a support vectorclustering (SVC)-based approach to model uncertain CSI as a compact convexuncertainty set. Based on that, the chance constraint of D2D is converted intoa linear convex set. Then, we develop a bisection search-based power allocationalgorithm for solving the resource allocation in D2D underlaying cellularnetwork with different robust counterparts. Finally, we conduct the simulationto compare the proposed robust optimization approaches with the non-robust one.

Learning-Based Robust Resource allocation for D2D Underlaying Cellular Network