Docitive Learning Mechanism for Interference Management in Femtocell Networks

In order to achieve the enhancement in the throughput and the coverage for cellular communication, femtocell is a viable solution. Moreover, self-organization is proved to be an attractive option because of the uncertain deployment of femtocells. In this study, we are projecting the docitive Q-learning mechanism for the joint resource allocation and power control in femtocell network. Additionally, the comparison is carried out with the independent Q-learning mechanism. The problem function is modeled as the maximization of femtocells capacity while maintaining the quality of service requirement of macrousers. The analysis of the considered learning mechanisms, docitive and independent, on the femtocells capacity and network capacity is analyzed in terms of convergence and femtocells density. Simulation results validate the better performance of proposed docitive Q-learning as compared to its counterpart.