Self-optimization of coverage and capacity in LTE networks based on central control and decentralized fuzzy Q-Learning

To reduce capital expenditures (CAPEX) and operational expenditures (OPEX) in network operations, self-organizing network (SON) has been introduced as a key part of long-term-evolution (LTE) system. Self-optimization of coverage and capacity is one of the most important tasks in the context of SON. This paper proposes a central control mechanism that utilizes the fuzzy Q-learning algorithm in a decentralized fashion for this task. In our proposed approach, each eNB is a learning agent that tries to optimize its antenna downtilt automatically using information from its own and its neighboring cells, and the initialization and the termination of the optimization processes of all agents are in the control of the central entity. The simulation results verify that our proposed approach can achieve remarkable performance enhancement as well as fast convergence, indicating that it is able to meet different levels of demands defined by 3GPP for coverage and capacity optimization.