[1] |
YANG Z , DING Z G , FAN P Z . Performance analysis of cloud radio access networks with uniformly distributed base stations[J]. IEEE Transactions on Vehicular Technology, 2016,65(1): 472-477.
|
[2] |
SUN C , LI H , LI X H ,et al. Convergence of recommender systems and edge computing:a comprehensive survey[J]. IEEE Access, 2020,8: 47118-47132.
|
[3] |
PENG M G , YAN S , ZHANG K C ,et al. Fog-computing-based radio access networks:issues and challenges[J]. IEEE Network, 2015,30(4): 46-53.
|
[4] |
SUN Y H , PENG M G , MAO S W . Deep reinforcement learning-based mode selection and resource management for green fog radio access networks[J]. IEEE Internet of Things Journal, 2019,6(2): 1960-1971.
|
[5] |
XIANG H Y , PENG M G , SUN Y H ,et al. Mode selection and resource allocation in sliced fog radio access networks:a reinforcement learning approach[J]. IEEE Transactions on Vehicular Technology, 2020,69(4): 4271-4284.
|
[6] |
LETAIEF K B , CHEN W , SHI Y M ,et al. The roadmap to 6G:AI empowered wireless networks[J]. IEEE Communications Magazine, 2019,57(8): 84-90.
|
[7] |
WEI Y F , YU R , SONG M ,et al. Joint optimization of caching,computing,and radio resources for fog-enabled IoT using natural actor-critic deep reinforcement learning[J]. IEEE Internet of Things Journal, 2018,6(2): 2061-2073.
|
[8] |
LUONG N C , HOANG D T , GONG S M ,et al. Applications of deep reinforcement learning in communications and networking:a survey[J]. IEEE Communications Surveys & Tutorials, 2019(99): 1.
|
[9] |
YU Y , LI H L , CHEN R N ,et al. Enabling secure intelligent network with cloud-assisted privacy-preserving machine learning[J]. IEEE Network, 2019,33(3): 82-87.
|
[10] |
LIU X , XU Y H , JIA L L ,et al. Anti-jamming communications using spectrum waterfall:a deep reinforcement learning approach[J]. IEEE Communications Letters, 2018,22(5): 998-1001.
|