Conjectural Variations in Multi-Agent Reinforcement Learning for Energy-Efficient Cognitive Wireless Mesh Networks

Provided by: Zhejiang Sci-Tech University
Topic: Mobility
Format: PDF
As energy saving and environmental protection become an inevitable trend, researchers need to shift their focus to "Green" oriented architecture design. Recent advances in the area of Cognitive Radio (CR) have significant potential towards "Green" communications. One of the critical challenges for operating CRs in a wireless mesh network is how to efficiently allocate transmission powers and frequency resource among the Secondary Users (SUs) while satisfying the quality-of-service constraints of primary users. Due to the SUs' intelligent and selfish properties, this paper focuses on the non-cooperative spectrum sharing in cognitive wireless mesh networks formed by a number of clusters.

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