Choosing k best arms in adversarial bandit setting: application to inter-cell Interference Coordination
This talk address to Multi-Armed Bandit problem for Distributed Inter-Cell Interference Coordination. In order to achieve high data rates in future wireless packet switched cellular networks, aggressive frequency reuse is inevitable due to the scarcity of the radio resources. While intra-cell interference is mostly mitigated and can be ignored, inter-cell interference can severely degrade performances of end- users. Hence, Inter-Cell Interference Coordination is commonly identified as a key radio resource management mechanism to en- hance system performance of 4G networks. This talk addresses the problem of ICIC in the downlink of Long Term Evolution (LTE) systems where the Resource Blocks (RB) selection process is inspired from the reinforcement learning theory targeted to address the adversarial Multi-Armed Bandit problem. We adapt to the EXP3 algorithm whose goal is to steer autonomously the decision of each Base Station (BS) towards the least interfered RBs while ensuring reactivity to the possible changes that can occur in the common resource usage and radio channel quality. This talk is a joint work with Pierre Coucheney (MdC, Univeristé de Versailles) Kinda Khawam (MdC, Univeristé de Versailles).