Optimization of URLLC and eMBB multiplexing via deep reinforcement learning
Y Li, C Hu, J Wang, M Xu - 2019 IEEE/CIC International …, 2019 - ieeexplore.ieee.org
In 5G mobile networks, multiple scenarios have emerged to meet different services
requirement. The limited spectrum resource becoming more and more crowed to meet …
requirement. The limited spectrum resource becoming more and more crowed to meet …
Optimizing aoi in uav-ris assisted iot networks: Off policy vs. on policy
In urban environments, tall buildings or structures can pose limits on the direct channel link
between a base station (BS) and an Internet of Thing device (IoTD) for wireless …
between a base station (BS) and an Internet of Thing device (IoTD) for wireless …
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 …
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 …
Deep multiagent reinforcement-learning-based resource allocation for internet of controllable things
B Gu, X Zhang, Z Lin, M Alazab - IEEE Internet of Things …, 2020 - ieeexplore.ieee.org
Ultrareliable and low-latency communication (URLLC) is a prerequisite for the successful
implementation of the Internet of Controllable Things. In this article, we investigate the …
implementation of the Internet of Controllable Things. In this article, we investigate the …
Adaptive partial offloading and resource harmonization in wireless edge computing-assisted ioe networks
Wireless backhaul is considered a portable and cost-effective solution for deploying small
cell-assisted communications in mobile Internet of Everything (IoE) networks. In this system …
cell-assisted communications in mobile Internet of Everything (IoE) networks. In this system …
[HTML][HTML] A reinforcement learning-based computing offloading and resource allocation scheme in F-RAN
F Jiang, R Ma, Y Gao, Z Gu - EURASIP Journal on Advances in Signal …, 2021 - Springer
This paper investigates a computing offloading policy and the allocation of computational
resource for multiple user equipments (UEs) in device-to-device (D2D)-aided fog radio …
resource for multiple user equipments (UEs) in device-to-device (D2D)-aided fog radio …
Power control with qos guarantees: A differentiable projection-based unsupervised learning framework
M Alizadeh, H Tabassum - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Deep neural networks (DNNs) are emerging as a potential solution to solve NP-hard
wireless resource allocation problems. However, in the presence of intricate constraints, eg …
wireless resource allocation problems. However, in the presence of intricate constraints, eg …
IRS-enhanced OFDMA: Joint resource allocation and passive beamforming optimization
Intelligent reflecting surface (IRS) is an emerging technique to enhance the wireless
communication spectral efficiency with low hardware and energy cost. In this letter, we …
communication spectral efficiency with low hardware and energy cost. In this letter, we …
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 …