1 K.B. Letaief, "The roadmap to 6G: AI empowered wireless networks" 57 (57): 84-90, 2019
2 H.-S. Lee, "Resource allocation in wireless networks with deep reinforcement learning: A circumstance-independent approach" 14 (14): 2589-2592, 2020
3 H.H. Zhuo, "Lin, Federated deep reinforcement learning"
4 F. Al-Tam, "Learn to schedule (LEASCH):A deep reinforcement learning approach for radio resource scheduling in the 5G MAC layer" 8 : 108088-108101, 2020
5 W. Shi, "Joint device scheduling and resource allocation for latency constrained wireless federated learning" 20 (20): 453-467, 2021
6 V. Mnih, "Human-level control through deep reinforcement learning" 518 (518): 529-533, 2015
7 C.T. Dinh, "Federated learning over wireless networks:Convergence analysis and resource allocation" 29 (29): 398-409, 2021
8 N. Zhao, "Deep reinforcement learning for user association and resource allocation in heterogeneous cellular networks" 18 (18): 5141-5152, 2019
9 O. Naparstek, "Deep multi-user reinforcement learning for distributed dynamic spectrum access" 18 (18): 310-323, 2019
10 S. Wang, "Content-based vehicle selection and resource allocation for federated learning in iov" 2021
1 K.B. Letaief, "The roadmap to 6G: AI empowered wireless networks" 57 (57): 84-90, 2019
2 H.-S. Lee, "Resource allocation in wireless networks with deep reinforcement learning: A circumstance-independent approach" 14 (14): 2589-2592, 2020
3 H.H. Zhuo, "Lin, Federated deep reinforcement learning"
4 F. Al-Tam, "Learn to schedule (LEASCH):A deep reinforcement learning approach for radio resource scheduling in the 5G MAC layer" 8 : 108088-108101, 2020
5 W. Shi, "Joint device scheduling and resource allocation for latency constrained wireless federated learning" 20 (20): 453-467, 2021
6 V. Mnih, "Human-level control through deep reinforcement learning" 518 (518): 529-533, 2015
7 C.T. Dinh, "Federated learning over wireless networks:Convergence analysis and resource allocation" 29 (29): 398-409, 2021
8 N. Zhao, "Deep reinforcement learning for user association and resource allocation in heterogeneous cellular networks" 18 (18): 5141-5152, 2019
9 O. Naparstek, "Deep multi-user reinforcement learning for distributed dynamic spectrum access" 18 (18): 310-323, 2019
10 S. Wang, "Content-based vehicle selection and resource allocation for federated learning in iov" 2021
11 J. Xu, "Client selection and bandwidth allocation in wireless federated learning networks: A long-term perspective" 20 (20): 1188-1200, 2021
12 H.-S. Lee, "Adaptive transmission scheduling in wireless networks for asynchronous federated learning" 39 (39): 3673-3687, 2021
13 J. Fan, "A theoretical analysis of deep Qlearning" 486-489, 2020
14 C. Fiandrino, "A machinelearning-based framework for optimizing the operation of future networks" 58 (58): 20-25, 2020
15 M. Chen, "A joint learning and communications framework for federated learning over wireless networks" 20 (20): 269-283, 2021