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- W2964034826 abstract "Effective power control, while essential for the operation of cellular communication systems, is most challenging in high-density cellular systems as the computational complexity for determining optimal base station transmission power levels grows exponentially with the number of system base stations and channel variation rate. A power level control scheme for high-density cellular systems, based on recent advances in machine learning and neural networks, is proposed in this thesis. Optimal power levels are computed using a greedy search in an offline phase to train a neural network predicting optimal power levels during the online phase. Multiple neural network structures are studied, along with regularization and optimization methods for fast and efficient training, the effect of the training size on the performance and effect of granularity of transmission power levels. Provisions for improving user quality of service are also introduced through a modified optimization objective function targeting minimum user throughput rates.%%%%M.A.S.%%%%2019-07-10 00:00:00" @default.
- W2964034826 created "2019-07-30" @default.
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- W2964034826 date "2018-06-01" @default.
- W2964034826 modified "2023-09-26" @default.
- W2964034826 title "Power Level Control in High-Density Cellular Communication Systems: a Deep Learning Based Approach" @default.
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