Machine Learning-Based 5G RAN Slicing for Broadcasting Services | IEEE Journals & Magazine | IEEE Xplore

Machine Learning-Based 5G RAN Slicing for Broadcasting Services


Abstract:

Along with the commercialization of evolved multimedia broadcast multicast services (eMBMS), the number of mobile broadcasting users is growing notably. Previous works re...Show More

Abstract:

Along with the commercialization of evolved multimedia broadcast multicast services (eMBMS), the number of mobile broadcasting users is growing notably. Previous works reveal that the accuracy of mobile channel estimation will significantly impact the quality of broadcasting services. Motivated by this fact, we apply machine learning (ML) to the fifth-generation Radio Access Network (5G RAN) slicing in this paper for the estimation and the prediction of the channel status in mobile scenarios. More specifically, a cascaded convolutional neural network (CNN)-long short term memory network (LSTM) architecture is developed to achieve channel estimation for mobile broadcasting users. The energy efficiency of the base station (BS) is modeled mathematically, and the sub-optimal solution is achieved by deep Q-Network (DQN) based on the available channel status. Finally, we present the simulation results to justify the performance of our proposed schemes.
Published in: IEEE Transactions on Broadcasting ( Volume: 68, Issue: 2, June 2022)
Page(s): 295 - 304
Date of Publication: 28 October 2021

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