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Adapting to Dynamic LEO-B5G Systems: Meta-Critic Learning Based Efficient Resource Scheduling.

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Learning-Based Resource Allocation: Efficient Content Delivery Enabled by Convolutional Neural Network., , , , and . SPAWC, page 1-5. IEEE, (2019)Sparsification and Optimization for Energy-Efficient Federated Learning in Wireless Edge Networks., , , , , and . GLOBECOM, page 3071-3076. IEEE, (2022)Joint User Grouping and Power Allocation for MISO Systems: Learning to Schedule., , , , and . EUSIPCO, page 1-5. IEEE, (2019)Actor-Critic Deep Reinforcement Learning for Energy Minimization in UAV-Aided Networks., , , , and . EuCNC, page 348-352. IEEE, (2020)ProxSGD: Training Structured Neural Networks under Regularization and Constraints., , , , , and . ICLR, OpenReview.net, (2020)AG-MS: A User Grouping Scheme for DASH Multicast over Wireless Networks., , and . VTC Spring, page 1-5. IEEE, (2017)Adapting to Dynamic LEO-B5G Systems: Meta-Critic Learning Based Efficient Resource Scheduling., , , , , and . IEEE Trans. Wirel. Commun., 21 (11): 9582-9595 (2022)Actor-critic learning-based energy optimization for UAV access and backhaul networks., , , , , and . EURASIP J. Wirel. Commun. Netw., 2021 (1): 78 (2021)Optimality Analysis and Efficient Scheduling for Massive IoT-LEO Satellite Networks., , , , and . SPAWC, page 476-480. IEEE, (2023)Energy Minimization in UAV-Aided Networks: Actor-Critic Learning for Constrained Scheduling Optimization., , , , , and . IEEE Trans. Veh. Technol., 70 (5): 5028-5042 (2021)