Abstract:
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, femtocells have brought many technical challenges on the way to be deployed 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 thesis gives self-optimizing and self-healing technique that utilizes Fuzzy Q-learning algorithm for the objectives of enhancement in the network spectral efficiency. In our proposed scheme, each macro BS acts as an agent which interacts with its local environment (all the femtocells and MSs under its coverage area), gathers the information and takes the suitable actions correspondingly. For the objective of controlling cross-tier interference, Macro users are rescheduled in such an intelligent way that performance of the femto users, 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 SON requirements set by 3GPP.
Index Terms: - Femtocell, interference optimization, self-organized network, Fuzzy Q-Learning, convergence, spectral efficiency.
