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- W4387487471 abstract "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." @default.
- W4387487471 created "2023-10-11" @default.
- W4387487471 creator A5022799182 @default.
- W4387487471 date "2023-08-25" @default.
- W4387487471 modified "2023-10-16" @default.
- W4387487471 title "Data Driven Decision Making Algorithm for Self Organizing Networks by Q-Learning Methods for Heterogeneous Networks" @default.
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- W4387487471 doi "https://doi.org/10.1109/asiancon58793.2023.10269840" @default.
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