3 citations · 3 across the 3 of their papers we have counts for
6 papers
Mode Selection and Resource Allocation in Sliced Fog Radio Access Networks: A Reinforcement Learning Approach
Hongyu Xiang, Mugen Peng, Yaohua Sun +1
The mode selection and resource allocation in fog radio access networks (F-RANs) have been advocated as key techniques to improve spectral and energy efficiency. In this paper, we…
Deep Reinforcement Learning Based Mode Selection and Resource Allocation for Cellular V2X Communications
Xinran Zhang, Mugen Peng, Shi Yan +1
Cellular vehicle-to-everything (V2X) communication is crucial to support future diverse vehicular applications. However, for safety-critical applications, unstable vehicle-to-vehic…
Deep Reinforcement Learning Based Mode Selection and Resource Management for Green Fog Radio Access Networks
Yaohua Sun, Mugen Peng, Shiwen Mao
Fog radio access networks (F-RANs) are seen as potential architectures to support services of internet of things by leveraging edge caching and edge computing. However, current wor…
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
Yaohua Sun, Mugen Peng, Yangcheng Zhou +2
As a key technique for enabling artificial intelligence, machine learning (ML) is capable of solving complex problems without explicit programming. Motivated by its successful appl…
Resource Allocation in Cloud Radio Access Networks with Device-to-Device Communications
Yitao Mo, Mugen Peng, Hongyu Xiang +2
To alleviate the burdens on the fronthaul and reduce the transmit latency, the device-to-device (D2D) communication is presented in cloud radio access networks (C-RANs). Considerin…
Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues
Mugen Peng, Yaohua Sun, Xuelong Li +2
As a promising paradigm to reduce both capital and operating expenditures, the cloud radio access network (C-RAN) has been shown to provide high spectral efficiency and energy effi…