[HTML][HTML] Deep reinforcement learning-based resource allocation for D2D communications in heterogeneous cellular networks
Abstract Device-to-Device (D2D) communication-enabled Heterogeneous Cellular Networks
(HCNs) have been a promising technology for satisfying the growing demands of smart …
(HCNs) have been a promising technology for satisfying the growing demands of smart …
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 …
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 …
Multi-agent deep reinforcement learning based spectrum allocation for D2D underlay communications
Device-to-device (D2D) communication underlay cellular networks is a promising technique
to improve spectrum efficiency. In this situation, D2D transmission may cause severe …
to improve spectrum efficiency. In this situation, D2D transmission may cause severe …
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 …
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 …
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 …
Federated reinforcement learning-based resource allocation in D2D-enabled 6G
The current 5G and conceived 6G era with ultra-high density, ultra-high frequency
bandwidth, and ultra-low latency can support emerging applications like Extended Reality …
bandwidth, and ultra-low latency can support emerging applications like Extended Reality …
Deep reinforcement learning empowered joint mode selection and resource allocation for RIS-aided D2D communications
L Guo, J Jia, J Chen, A Du, X Wang - Neural Computing and Applications, 2023 - Springer
Abstract Device-to-device (D2D) communication has been regarded as a promising solution
to alleviate the mobile traffic explosion problem for its capabilities of improving system data …
to alleviate the mobile traffic explosion problem for its capabilities of improving system data …
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