Optimizing Coverage and Capacity in Cellular Networks using Machine Learning| IEEE ICASSP 2021
Автор: Robert Heath
Загружено: 2023-01-10
Просмотров: 800
Wireless cellular towers have many parameters that are normally tuned upon deployment and re-tuned as the network changes. Optimizing these parameters across a large network requires significant time and resources. In Ryan’s presentation at ICASSP 2021, we present two black-box optimization strategies—deep reinforcement learning and Bayesian optimization—as potential solutions to optimizing transmit power and elevation downtilt. Comparisons are made between the two paradigms in terms of pareto frontiers and sample efficiency.
[1] R. M. Dreifuerst et al., "Optimizing Coverage and Capacity in Cellular Networks using Machine Learning," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021.
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Websites:
Ryan Dreifuerst: https://ryandry1st.github.io/
Robert W. Heath Jr.: https://ece.ncsu.edu/people/rwheath2/
#reinforcementlearning
#BayesianOptimization
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