A project titled “Research on Full Service Awareness based Intelligence-Endogenous Networks” has been approved by National Science Foundation of China.

Project name in Chinese (项目中文题目:基于业务感知的内生智能通信网络研究)

Abstract: Faced with the ever-growing services with increasingly diversified and scenario-based requirements, researchers from both academia and industry are resorting to a deeper integration of artificial intelligence (AI) and communications, so as to provide endogenous intelligence within cellular networks and further enhance the agility and flexibility of cellular networks. Unfortunately, the unique characteristics of cellular networks (e.g., heterogenous infrastructure, diversified and dynamic service demands, random wireless environment, as well as user mobility) makes it challenging to directly apply the AI results. Therefore, it is highly essential to conduct the research on intelligence-endogenous networks based on full service awareness. This project (1) studies how to comprehensively leverage the information from wireless data, models and knowledge, and propose novel intelligent methods for service awareness and networks resource management, so as to improve the robustness of learning results and lay the foundation for further optimization; (2) tackles the issues incurred by the existence of multiple network entities and objective goals and designs multi-scale multi-agent reinforcement learning algorithms correspondingly, thus robustly, flexibly and dynamically adjusting the network resource allocation according to the service demands; (3) theoretically analyzes the potential bounds for the impact of endogenous intelligence on network performance; (4) builds a simulation platform. With all four pillars including service-aware modeling, algorithm designing, theoretical analyzing and simulations, this project will lay the theoretical foundation and provide essential techniques to the next-generation cellular networks for better provisioning diversified services in a smarter and more coherently manner.

Duration: Jan. 2021 – Dec. 2024

Amount: 550 000 RMB

A paper titled “GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing” with Dr. Li as Corresponding Author has been accepted by IEEE JSAC.

Paper abstract:

Network slicing is a key technology in 5G communications system. Its purpose is to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, demand-aware resource allocation is of significant importance to network slicing. In this paper, 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). We leverage deep reinforcement learning (DRL) 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 (SLA) satisfaction ratio (SSR) and spectrum efficiency (SE), 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. We put forward a reward-clipping mechanism to stabilize GAN-DDQN training against the effects of widely-spanning utility values. Moreover, we further develop Dueling GAN-DDQN, which uses a specially designed dueling generator, to learn the action-value distribution by estimating the state-value distribution and the action advantage function. Finally, we verify the performance of the proposed GAN-DDQN and Dueling GAN-DDQN algorithms through extensive simulations.

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Another FOUR articles have been accepted for publications in IEEE Access, IEEE Communications Letters, or IET Communications since Jun. 2017.

Paper List:

  1. Zhifeng Zhao, Feng Hong, and Rongpeng Li, “SDN Based VxLAN Optimization in Cloud Computing Networks,” IEEE Access, Oct. 2017, accepted. (Corresponding Author)
  2. Zhifeng Zhao, Meng Li, Rongpeng Li, and Yifan Zhou, “Temporal-Spatial Distribution Nature of Traffic and Base Stations in Cellular Networks,” IET Commun., Sep. 2017, accepted. (Corresponding Author)
  3. Jiachen Sun, Liang Shen, Guoru Ding, Rongpeng Li, and Qihui Wu, “Predictability Analysis of Spectrum State Evolution: Performance Bounds and Real-World Data Analytics,” IEEE Access, Oct. 2017, accepted.
  4. Yifan Zhou, Zhifeng Zhao, Rongpeng Li, Honggang Zhang, and Yves Louet, “Cooperation Based Probabilistic Caching Strategy in Clustered Cellular Networks”, IEEE Commun. Lett., Jun. 2017, accepted.