Data Driven Decision Making Algorithm for Self Organizing Networks by Q-Learning Methods for Heterogeneous Networks

Q-learning is an algorithm for data-driven decision making for self-organizing networks (SONs). This approach utilizes reinforcement learning principles to automatically optimize various network parameters, enabling SONs to adapt and self-organize in order to maximize network performance metrics such as mean throughput, system capacity, and handover rate. Q-learning has been used in several SONs, such as those for LTEs, WiMaxs, and Mobile WiFis. Q-learning works by considering various network parameters as states or actions, defining a reward function based on desired network performance metrics such as mean throughput, and leveraging algorithms to learn which actions produce higher rewards. With this approach, Q-learning can be used to self-configure, self-optimize, and self-protect SONs, allowing networks to evolve autonomously and remain reliable under diverse conditions.