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.

External Link:

https://arxiv.org/abs/1905.03929

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.