Managing Femto to Macro Interference without X2 Interface Support through POMDP

We present a self-organized downlink power control for interference management when Home eNodeBs (HeNBs) work in co-channel operation with the macrocell system. The main novelty with regards to previous works is that we provide a completely autonomous framework, considering 3GPP release?11 hypothesis of non availability of X2 interface between evolved NodeBs (eNBs) and HeNBs. In this situation, the HeNB has to make autonomous decisions without receiving any feedback from the macro network. We model the HeNBs as a multiagent system where each node is an independent agent able to learn through Reinforcement Learning (RL) techniques a downlink power allocation policy for different interference situations. To deal with the lack of information in the scenario, we rely on the theory of Partially Observable Markov Decision Process (POMDP). POMDP works on the basis of a set of beliefs that the HeNB builds considering the impact it causes to the macrocell system. To gather this system performance information, we propose that HeNBs use spatial interpolation techniques, such as ordinary Kriging. Results show that the proposed approach allows HeNBs to autonomously learn a power allocation policy to coexist with the macro network, in a 3GPP compliant fashion, and without introducing overhead signaling in the system.