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 consider a mesh-structured unmanned aerial vehicle (UAV)networks exploiting partially overlapping channels (POCs). For generaldata-collection tasks in UAV networks, we aim to optimize the networkthroughput with constraints on transmission power and quality of service (QoS).As far as the highly mobile and constantly changing UAV networks are concerned,unfortunately, most existing methods rely on definite information which isvulnerable to the dynamic environment, rendering system performance to be lesseffective. In order to combat dynamic topology and varying interference of UAVnetworks, a robust and distributed learning scheme is proposed. Rather than theperfect channel state information (CSI), we introduce uncertainties tocharacterize the dynamic channel gains among UAV nodes, which are theninterpreted with fuzzy numbers. Instead of the traditional observation spacewhere the channel capacity is a crisp reward, we implement the learning anddecision process in a mapped fuzzy space. This allows the system to achieve asmoother and more robust performance by optimizing in an alternate space. Tothis end, we design a fuzzy payoffs function (FPF) to describe the fluctuatedutility, and the problem of POCs assignment is formulated as a fuzzy payoffsgame (FPG). Assisted by an attractive property of fuzzy bi-matrix games, theexistence of fuzzy Nash equilibrium (FNE) for our formulated FPG is proved. Ourrobust fuzzy-learning algorithm could reach the equilibrium solution via aleast-deviation method. Finally, numerical simulations are provided todemonstrate the advantages of our new scheme over the existing scheme.

Robust Fuzzy-Learning For Partially Overlapping Channels Allocation In UAV Communication Networks