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

Device-to-device (D2D) communication underlay cellular networks is apromising technique to improve spectrum efficiency. In this situation, D2Dtransmission may cause severe interference to both the cellular and other D2Dlinks, which imposes a great technical challenge to spectrum allocation.Existing centralized schemes require global information, which causes a largesignaling overhead. While existing distributed schemes requires frequentinformation exchange among D2D users and cannot achieve global optimization. Inthis paper, a distributed spectrum allocation framework based on multi-agentdeep reinforcement learning is proposed, named multi-agent actor critic (MAAC).MAAC shares global historical states, actions and policies during centralizedtraining, requires no signal interaction during execution and utilizescooperation among users to further optimize system performance. Moreover, inorder to decrease the computing complexity of the training, we further proposethe neighbor-agent actor critic (NAAC) based on the neighbor users' historicalinformation for centralized training. The simulation results show that theproposed MAAC and NAAC can effectively reduce the outage probability ofcellular links, greatly improve the sum rate of D2D links and converge quickly.

Multi-Agent Deep Reinforcement Learning based Spectrum Allocation for D2D Underlay Communications