A Q Learning and Fuzzy Q Learning Approach for Optimization of Interference Constellations in Femto–Macro Cellular Architecture in Downlink

The reduction in the cost and enhancement in the network coverage and capacity are the main objectives in the establishment of mobile networks. These objectives were the key force behind the idea of femtocells deployment. But, there are many technical issues for the femtocells deployment with already existing macro network. Cross-tier interference is one of the main challenge that must have to be resolved for smooth operation of macro–femto network. This paper gives self-optimizing and self-healing technique that utilizes Q-learning and fuzzy Q-learning algorithm for the objectives of enhancement in the network capacity and spectral efficiency. In our proposed scheme, each macro base station acts as an agent which interacts with its local environment (all the femtocells and mobile stations under its coverage area), gathers the information and takes the suitable actions correspondingly. For the objective of controlling cross-tier interference, macrousers are rescheduled in such an intelligent way that performance of the femtousers, located on the overlapped spectral portion, is not degraded. The simulation results confirm our proposed approach to improve the network capacity and spectral efficiency as well as sharp convergence, which designates its capability to meet the self organizing network requirements set by 3GPP.