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Service-Aware Provisioning for Greener & Smarter Cellular Networks

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If you have a question about this talk, please contact Srinivasan Keshav.

Cellular Networks has evolved into the fundamental infrastructure of the information and communication technology (ICT) industry. Faced with the increasingly diversified service demands and heterogeneous network architecture, it is becoming essential to further integrate the artificial intelligence and cellular networks. In this talk, I will first provide a general overview of our research on service-aware provisioning, which we believe is an essential ingredient for intelligent cellular networks. Afterwards, I will dip into one of my recent works on reinforcement learning-based resource management for network slicing. Specifically, we consider a scenario that contains several slices in a radio access network with base stations that share the same physical resources (e.g., bandwidth or slots), and try to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, we leverage deep reinforcement learning to solve this problem by considering the varying service demands as the environment state and the allocated resources as the environment action. In order to reduce the effects of the annoying randomness and noise embedded in the received service level agreement satisfaction ratio and spectrum efficiency, we primarily propose generative adversarial network-powered deep distributional Q network (GAN-DDQN) to learn the action-value distribution driven by minimizing the discrepancy between the estimated action-value distribution and the target action-value distribution. Finally, we verify the performance of the proposed GAN -DDQN algorithms through extensive simulations.

Bio: Dr. Li is now an assistant professor in College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou China. From August 2015 to September 2016, he was a research engineer with Wireless Communication Laboratory, Huawei Technologies Co. Ltd., Shanghai, China. His research interests currently focus on Reinforcement Learning, Data Mining and all broad-sense network problems (e.g., resource management, security, etc) and he has authored/coauthored tens of papers in the related fields. He serves as an Editor of China Communications.

This talk is part of the Computer Laboratory Systems Research Group Seminar series.

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