Gibbs Sampling Based Distributed OFDMA Resource Allocation

Virgile Garcia (1) , Chung Shue Chen (2, 3) , Yiqing Zhou (1) , Jinglin Shi (1)

1 CAS - Chinese Academy of Sciences [Changchun Branch]
2 Alcatel-Lucent Bell Labs France [Nozay]
3 LINCS - Laboratory of Information, Network and Communication Sciences

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.

Mots clés: Radio resource allocation; power control; interference management; distributed optimization; Gibbs sampling; Metropolis-Hastings

Domaines: Réseaux et télécommunications [cs.NI]

Virgile Garcia, Chung Shue Chen, Yiqing Zhou, Jinglin Shi. Gibbs Sampling Based Distributed OFDMA Resource Allocation. Science China Information Sciences, 2014. ⟨hal-00927286⟩