China Association of Artificial Intelligence (CAAI) has just announced the results of 2021 Wu Wenjun Artificial Intelligence Science and Technology Award (吴文俊人工智能科学技术奖). Together with the other 14 scholars, Dr. Rongpeng Li has been awarded the Wu Wenjun Artificial Intelligence Excellent Youth Award, for its contribution to the integration of artificial intelligence and cellular networks (especially network slicing).
According to the regulations on state science and technology awards, the Wu Wenjun Artificial Intelligence Science and Technology Award will reward innovation achievements in the field of intelligent science and technology in China. Scientists and managers, who have made significant breakthroughs in the field of intelligent science and technology and have made outstanding contributions to the field, will be granted the prize. Initiated in 2008, the Award was supported by the pioneer of China’s intelligent science research and the winner of the first Highest Science and Technology Awards Wu Wenjun, and was organized by CAAI. In 2021, 66 projects/scholars have been awarded.
The promotion takes effect since Dec. 31, 2020.
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
Prof. Jon Crowcroft is the host for this visit.
Computing networks is assumed to an integral part of B5G/6G networks. In this regard, the Internet of intelligence is indispensable. We talk the potential contribution of collective intelligence to both communications and intelligence.
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.