| Speaker : | Mohd Shabbir Ali |
| TPT | |
| Date: | 19/04/2017 |
| Time: | 2:00 pm - 3:00 pm |
| Location: | LINCS Seminars room |
Abstract
We present a novel approach for distributed load balancing in
heterogeneous networks that use cell range expansion (CRE) for user
association and almost blank subframe (ABS) for interference
management. First, we formulate the problem as a minimisation of an
alphaâfairness 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
alphaâfairness 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.Joint work with Pierre
Coucheney, and Marceau Coupechoux, in IEEE Transactions on Wireless
Communications (Vol. 15, No. 7, 2016).