A Fuzzy reinforcement learning approach for self-optimization of coverage in LTE networks

Optimization of antenna downtilt is an important aspect of coverage optimization in cellular networks. In this paper, an algorithm based on the combination of fuzzy logic and reinforcement learning is proposed and applied to the downtilt optimization problem to achieve the self-configuration, self-optimization, and self-healing functionalities required for future communication networks. To evaluate the efficiency of the proposed scheme, we use a detailed Long Term Evolution (LTE) simulation environment and employ an algorithm for configuring and optimizing the downtilt angle of the LTE base station antennas. This scheme is fully distributed and does not require any synchronization between network elements. Compared to an existing solution, evolutionary learning of fuzzy rules (ELF), the solution we propose provides up to 20 percent improvement in performance. In addition to self-x capabilities, the experiments further confirm the reliability and robustness of the algorithm in extremely noisy environments.

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