Y Guo, FR Yu, J An, K Yang, C Yu… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
… (MEC) under time-varying wireless channels. We propose a joint … By modeling the wireless channel as a finite state Markov … By using deep reinforcementlearning (DRL) algorithm, we …
… section, Monte Carlo and reinforcementlearning simulations are done to evaluate the proposed resource management schemes in LoRa wirelessnetworks by averaging up to 10000 …
… reinforcementlearning for energy optimization and resource allocation in wirelessnetworks, … AND/OR” combinations of them; ”deep reinforcementlearning,” ”DRL,” ”energy optimization,…
K Nakashima, S Kamiya, K Ohtsu, K Yamamoto… - IEEE …, 2020 - ieeexplore.ieee.org
… ABSTRACT For densely deployed wireless local area networks (WLANs), this paper proposes a deep reinforcementlearning-based channel allocation scheme that enables the efficient …
H Jiang, R Gui, Z Chen, L Wu, J Dang, J Zhou - IEEE Access, 2019 - ieeexplore.ieee.org
… long short-term memory network-based deep learning method for predicting the downlink … -free reinforcementlearning algorithm in wireless communication networks that combines …
Y Yu, SC Liew, T Wang - IEEE Transactions on Mobile …, 2021 - ieeexplore.ieee.org
… to share a common wireless spectrum and each network is unaware of the … reinforcement learning (DRL) based MAC protocol for a particular network, and the objective of this network is …
… In this paper, a new game theory approach based on reinforcementlearning to recover Coverage Holes in a distributed way is proposed. For the formulated potential game, sensor …
… We study reinforcementlearning at the sensors to find a transmission … in wireless powered sensor networks. Numerical results demonstrate that the proposed reinforcementlearning …
… communication, which uses multiagent reinforcementlearning (MARL) to maximize sys… reinforcementlearning (DRL) in D2D-enabled MEC, enabling mobile users to automatically learn …