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.

中文翻译：

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适用于广播服务的基于机器学习的 5G RAN 切片


随着演进多媒体广播多播服务（eMBMS）的商业化，移动广播用户数量显着增长。先前的工作表明，移动信道估计的准确性将显着影响广播服务的质量。受此事实的启发，我们在本文中将机器学习（ML）应用于第五代无线接入网络（5G RAN）切片，以估计和预测移动场景中的信道状态。更具体地说，开发了级联卷积神经网络（CNN）-长期短期记忆网络（LSTM）架构来实现移动广播用户的信道估计。对基站（BS）的能量效率进行数学建模，并通过基于可用信道状态的深度Q网络（DQN）实现次优解决方案。最后，我们提出仿真结果来证明我们提出的方案的性能。