Autonomous resource allocation for dense LTE networks: A Multi Armed Bandit formulation

Resource allocation is an important prerequisite for the effective deployment of Pico Cells (PCs). This topic becomes even more challenging in the case of heterogeneous networks, where autonomous interference management mechanisms are necessary. In this article, we propose a resource sharing method inspired from the reinforcement learning theory and particularly the methods used to solve the Multi Armed Bandit (MAB) problem. The main goal resides in giving the ability for each cell to make its decision autonomously while dynamically taking into account the resource occupation of each surrounding cell. We set up the global framework for the MAB based resource allocation strategies in the case of total frequency overlapping PCs. The performances of the proposed method are evaluated in the case of Long Term Evolution (LTE) Pico Cells deployment and compared to static allocation schemes. The results demonstrate the efficiency of our method.