Reinforcement Learning for Distributed Power Control and Channel Access in Cognitive Wireless Mesh Networks

Xianfu Chen, Zhifeng Zhao, Honggang Zhang

Research output: Chapter in Book/Report/Conference proceedingChapter or book articleScientificpeer-review

2 Citations (Scopus)
Original languageEnglish
Title of host publicationCognitive Communications
Subtitle of host publicationDistributed Artificial Intelligence (DAI), Regulatory Policy and Economics, Implementation
PublisherWiley
Chapter7
Pages163-193
Number of pages31
ISBN (Print)978-1-11995-150-6
DOIs
Publication statusPublished - 24 Jul 2012
MoE publication typeA3 Part of a book or another research book

Keywords

  • CogMesh framework, two major technical issues
  • CogMesh, and problem of opportunistic spectrum access
  • CogMesh, self-organized/self-configured network architecture
  • Conjecture-based multi-agent Q-learning algorithm
  • Distributed learning algorithms and dynamic conjectures, OSA in CogMesh
  • E-greedy method, single-/multi-agent learning algorithms for balancing
  • Resource allocation in CogMesh, stochastic learning process
  • RL for distributed power control, in CogMesh
  • Stochastic power allocation, performance balancing competing objectives

Cite this

Chen, X., Zhao, Z., & Zhang, H. (2012). Reinforcement Learning for Distributed Power Control and Channel Access in Cognitive Wireless Mesh Networks. In Cognitive Communications: Distributed Artificial Intelligence (DAI), Regulatory Policy and Economics, Implementation (pp. 163-193). Wiley. https://doi.org/10.1002/9781118360316.ch7