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

In this paper, we model, analyze and optimize the multi-user andmulti-order-reflection (MUMOR) intelligent reflecting surface (IRS) networks.We first derive a complete MUMOR IRS network model applicable for the arbitrarytimes of reflections, size and number of IRSs/reflectors. The optimal conditionfor achieving sum-rate upper bound with one IRS in a closed-form function andthe analytical condition to achieve interference-free transmission are derived,respectively. Leveraging this optimal condition, we obtain the MUMOR sum-rateupper bound of the IRS network with different network topologies, where thelinear graph (LG), complete graph (CG) and null graph (NG) topologies areconsidered. Simulation results verify our theories and derivations anddemonstrate that the sum-rate upper bounds of different network topologies areunder a K-fold improvement given K-piece IRS.

Intelligent Reflecting Surface Networks with Multi-Order-Reflection Effect: System Modelling and Critical Bounds