Gibbs sampling based distributed OFDMA resource allocation

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• 作者：Virgile Garcia (1) (2)
  Chung Shue Chen (3)
  YiQing Zhou (1) (2)
  JingLin Shi (1) (2)
  
• 关键词：radio resource allocation ; power control ; interference management ; distributed optimization ; Gibbs sampling ; Metropolis ; Hastings
• 刊名：SCIENCE CHINA Information Sciences
• 出版年：2014
• 出版时间：April 2014
• 年：2014
• 卷：57
• 期：4
• 页码：1-12
• 全文大小：382 KB
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• 作者单位：Virgile Garcia (1) (2)
  Chung Shue Chen (3)
  YiQing Zhou (1) (2)
  JingLin Shi (1) (2)
  
  1. Wireless Technology Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China
  2. Beijing Key Laboratory of Mobile Computing and Pervasive Device, Beijing, 100190, China
  3. Alcatel-Lucent Bell Labs, Centre de Villarceaux, 91620, Nozay, France
  
• ISSN：1869-1919

文摘

In this article, we present a distributed resource and power allocation scheme for multiple-resource wireless cellular networks. The global optimization of multi-cell multi-link resource allocation problem is known to be NP-hard in the general case. We use Gibbs sampling based algorithms to perform a distributed optimization that would lead to the global optimum of the problem. The objective of this article is to show how to use the Gibbs sampling (GS) algorithm and its variant the Metropolis-Hastings (MH) algorithm. We also propose an enhanced method of the MH algorithm, based on a priori known target state distribution, which improves the convergence speed without increasing the complexity. Also, we study different temperature cooling strategies and investigate their impact on the network optimization and convergence speed. Simulation results have also shown the effectiveness of the proposed methods.