Deep Reinforcement Learning-Based Resource Allocation for Smart Grid in RAN Network Slice

[1]  Song Guo,et al.  Green Resource Allocation Based on Deep Reinforcement Learning in Content-Centric IoT , 2018, IEEE Transactions on Emerging Topics in Computing.

[2]  Shiwen Mao,et al.  Dealing with Limited Backhaul Capacity in Millimeter-Wave Systems: A Deep Reinforcement Learning Approach , 2018, IEEE Communications Magazine.

[3]  Yasir Saleem,et al.  Internet of Things-Aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions , 2017, IEEE Access.

[4]  Christian Wietfeld,et al.  On the economic benefits of software-defined networking and network slicing for smart grid communications , 2018, NETNOMICS: Economic Research and Electronic Networking.

[5]  Gustavo de Veciana,et al.  Network Slicing for Guaranteed Rate Services: Admission Control and Resource Allocation Games , 2018, IEEE Transactions on Wireless Communications.

[6]  Zhiguo Ding,et al.  QoE-Based Resource Allocation for Multi-Cell NOMA Networks , 2018, IEEE Transactions on Wireless Communications.

[7]  Rong Chen,et al.  A Deep Reinforcement Learning-Based Framework for Dynamic Resource Allocation in Multibeam Satellite Systems , 2018, IEEE Communications Letters.

[8]  Tao Jiang,et al.  Toward Cross-Layer Design for Non-Orthogonal Multiple Access: A Quality-of-Experience Perspective , 2018, IEEE Wireless Communications.

[9]  Pingzhi Fan,et al.  A QoE-Aware Resource Allocation Strategy for Multi-Cell NOMA Networks , 2017, 2017 IEEE Globecom Workshops (GC Wkshps).

[10]  Kiseon Kim,et al.  A Fair and Efficient Resource Allocation Scheme for Multi-Server Distributed Systems and Networks , 2016, IEEE Transactions on Mobile Computing.

[11]  Mei Song,et al.  Wireless virtual network embedding based on spectrum sharing allocation , 2016, 2016 11th International Conference on Computer Science & Education (ICCSE).