Multi-agent deep reinforcement learning-based power control and resource allocation for D2D communications
H Xiang, Y Yang, G He, J Huang… - IEEE Wireless …, 2022 - ieeexplore.ieee.org
Device-to-device (D2D) communications are envisioned as a critical technology to support
future ubiquitous mobile communications applications. However, the requirements of high …
future ubiquitous mobile communications applications. However, the requirements of high …
Deep reinforcement learning based power allocation for D2D network
Z Bi, W Zhou - 2020 IEEE 91st vehicular technology conference …, 2020 - ieeexplore.ieee.org
In device-to-device (D2D) networks, when D2D communication shares cellular network
spectrum resources, it will cause serious co-channel interference. The problem of solving …
spectrum resources, it will cause serious co-channel interference. The problem of solving …
Distributed DRL-based resource allocation for multicast D2D communications
Device-to-device (D2D) communication is one of the promising solutions to improve
spectrum efficiency and alleviate the mobile traffic explosion. However, interference …
spectrum efficiency and alleviate the mobile traffic explosion. However, interference …
Power control based on multi-agent deep q network for d2d communication
S Gengtian, T Koshimizu, M Saito… - … in Information and …, 2020 - ieeexplore.ieee.org
In device-to-device (D2D) communication under a cell with resource sharing mode the
spectrum resource utilization of the system will be improved. However, if the interference …
spectrum resource utilization of the system will be improved. However, if the interference …
Power control for D2D communication using multi-agent reinforcement learning
M Zhao, Y Wei, M Song, G Da - 2018 IEEE/CIC International …, 2018 - ieeexplore.ieee.org
Device-to-device (D2D) communication is a promising and rapidly evolving technology and
it plays a significant role in reducing pressure on the base station. In this paper, we focus on …
it plays a significant role in reducing pressure on the base station. In this paper, we focus on …
Deep reinforcement learning for joint channel selection and power control in D2D networks
Device-to-device (D2D) technology, which allows direct communications between proximal
devices, is widely acknowledged as a promising candidate to alleviate the mobile traffic …
devices, is widely acknowledged as a promising candidate to alleviate the mobile traffic …
Weighted cooperative reinforcement learning‐based energy‐efficient autonomous resource selection strategy for underlay D2D communication
Underlay Device‐to‐Device (D2D) communication is a key technology responsible for high
data rate, ultra‐low latency with high spectral and energy efficiency in 5G cellular networks …
data rate, ultra‐low latency with high spectral and energy efficiency in 5G cellular networks …
Power optimization in device-to-device communications: A deep reinforcement learning approach with dynamic reward
Device-to-Device (D2D) communication can be used to improve system capacity and energy
efficiency (EE) in cellular networks. One of the critical challenges in D2D communications is …
efficiency (EE) in cellular networks. One of the critical challenges in D2D communications is …
DRL-based sum-rate maximization in D2D communication underlaid uplink cellular networks
D Ron, JR Lee - IEEE Transactions on Vehicular Technology, 2021 - ieeexplore.ieee.org
Device-to-device (D2D) communication affords the benefits of improved network spectral
efficiency, throughput, energy efficiency, and delay performance. However, D2D …
efficiency, throughput, energy efficiency, and delay performance. However, D2D …
Make smart decisions faster: Deciding D2D resource allocation via stackelberg game guided multi-agent deep reinforcement learning
Device-to-Device (D2D) communication enabling direct data transmission between two
mobile users has emerged as a vital component for 5G cellular networks to improve …
mobile users has emerged as a vital component for 5G cellular networks to improve …
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