Learning in Near-Potential Games with Applications to Load Balancing in Heterogeneous Networks

Date: 
19.5.2016 11:00

 

Infotech Oulu Lecture Series

Lecturer: Shabbir Ali, Telecom Paris Tech,  France

Date May 19, 2016
Time: 11:00-12:00
Room: TS107

 

Abstract

 
We present a novel approach for distributed load balancing in heterogeneous networks that uses cell range expansion (CRE) for user association and almost blank subframe (ABS) for interference management. First, we formulate the problem as a minimization of an alpha-fairness objective function with load and outage constraints. Depending on alpha, different objectives in terms of network performance or fairness can be achieved. Next, we model the interactions among the base stations for load balancing as a near-potential game, in which the potential function is the alpha-fairness function. The optimal pure Nash equilibrium (PNE) of the game is found by using distributed learning algorithms. We propose log-linear and binary log-linear learning algorithms for complete and partial information settings, respectively. We give a detailed proof of convergence of learning algorithms for a near-potential game. We provide sufficient conditions under which the learning algorithms converge to the optimal PNE. By running extensive simulations, we show that the proposed algorithms converge within few hundreds of iterations. The convergence speed in the case of partial information setting is comparable to that of the complete information setting. Finally, we show that outage can be controlled and a better load balancing can be achieved by introducing ABS.
 


 

Last updated: 1.2.2017