Cooperative Fuzzy Q-Learning for self-organized coverage and capacity optimization

The paper presents a self-organized approach for coverage and capacity optimization by vertical antenna tilt adaptation. The approach is based on Cooperative Fuzzy Q-Learning and operates in a fully autonomous manner. To speedup the learning process, the proposed learning mechanism is cooperative in exploration phase and fully distributed in exploitation phase. The solution is capable of handling diverse network scenarios like initial configuration of antenna tilt at deployment, optimization of tilt during network operation and recovery from an outage in the neighborhood. This can significantly save the operational expenditure (OPEX) by minimizing the costly manual optimizations. For performance evaluation, system level simulations of LTE networks are performed and the results show that cells can learn to quickly converge to the global optimal configurations in normal operation and deployment phases. Moreover, dynamic antenna tilt adaptation can also deliver higher performance than networks with fixed configurations during cell outages in the neighborhood.