| Abstract: | Modern cellular mobile networks are becoming more complicated and too expensive in terms of deployment, operation, and maintenance. Traffic demand in cellular networks typically experiences spatio‐temporal variations because of users' mobility and usage behaviour, which lead some of the cells to get overloaded without fully utilizing network capacity. To tackle these challenges, nowadays, self‐organizing networks (SONs) become an essential feature. This paper offers the development of an optimization framework for SONs based on channel quality indicator (CQI) and loading condition without detail knowledge of the network environment. Since the electrical tilt plays a key role in optimizing both coverage and capacity, the main motive is to ensure efficient network operation by electrical tilt‐based radio frequency (RF) performance optimization using a machine learning approach. This novel methodology shows two‐step optimization algorithms: (a) cluster formation based on handover success rate using k‐means algorithm and (b) reinforcement learning‐based optimization. Simulation and field trial shows that the proposed approach provides better results than the conventional method of prediction using genetic algorithm (GA) and other online approaches. |
