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Two-Phase Deep Reinforcement Learning of Dynamic Resource Allocation and Client Selection for Hierarchical Federated Learning.

, , , , , , and . ICCC, page 518-523. IEEE, (2022)

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Evaluation for TCP with delayed ACK option in wireless multi-hop networks., and . VTC Fall, page 267-271. IEEE, (2001)Revealing the problems with 802.11 medium access control protocol in multi-hop wireless ad hoc networks, and . Comput. Networks, 38 (4): 531--548 (2002)Performance Evaluation for LTE-V based Vehicle-to-Vehicle Platooning Communication., , , and . APCC, page 15-20. IEEE, (2018)MBPANet: Solving Multiple Power Allocation Optimization Problems by a Universal Neural Network Architecture., , , , , and . ICC Workshops, page 1-6. IEEE, (2020)CFAD: Coarse-to-Fine Action Detector for Spatiotemporal Action Localization., , , , , , and . ECCV (16), volume 12361 of Lecture Notes in Computer Science, page 510-527. Springer, (2020)An End-to-End Quantization Framework for Fixed Point Fast Fourier Transform Hardware Implementation via Deep Neural Network., , , , and . ICDSP, page 183-187. ACM, (2020)Comparison of TCP Reno and Vegas in Wireless Mobile Ad Hoc Networks., , and . LCN, page 42-43. IEEE Computer Society, (2000)A Prototype Performance Analysis for V2V Communications using USRP-based Software Defined Radio Platform., , , and . GlobalSIP, page 1218-1222. IEEE, (2018)A Deep Learning Based Resource Allocation Scheme in Vehicular Communication Systems., , , , and . WCNC, page 1-6. IEEE, (2019)On the bandwidth-power tradeoff for heterogeneous networks with site sleeping and inter-cell interference., , , and . WCNC, page 3794-3799. IEEE, (2013)