Reinforcement learning based resource management for 6G-enabled mIoT with hypergraph interference model

J Huang, C Yang, S Zhang, F Yang… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
For the future 6G-enabled massive Internet of Things (mIoT), how to effectively manage
spectrum resources to support huge data traffic under the large-scale overlapping caused by …

Spectrum-efficient user grouping and resource allocation based on deep reinforcement learning for mmWave massive MIMO-NOMA systems

M Wang, X Liu, F Wang, Y Liu, T Qiu, M Jin - Scientific Reports, 2024 - nature.com
Millimeter-wave (mmWave) massive multiple-input multiple-output non-orthogonal multiple
access (MIMO-NOMA) is proven to be a primary technique for sixth-generation (6G) wireless …

Power Control of 5G-Connected Vehicular Network Using PPO-based Deep Reinforcement Learning Algorithm

M Raeisi, AB Sesay - IEEE Access, 2024 - ieeexplore.ieee.org
In this paper, we propose a novel power control in vehicular 5G-connected network using
Deep Reinforcement Learning (DRL) algorithm. We investigate power allocation for …

Optimization Algorithm for Efficient Channel Assignment and Performance Enhancement of Wireless Networks

R Krishan - SN Computer Science, 2024 - Springer
In the recent past, the frequency distribution in wireless networks was known to be a major
issue that resulted in the unfair utilization of wireless channels. The channel assignment …

[PDF][PDF] Resource Allocation in V2I Link Between Connected Autonomous Vehicles and 5G mm-Wave Band Small-Cells Using Machine Learning

M Raeisi Ziarani - Resource, 2024 - prism.ucalgary.ca
This dissertation presents innovative contributions aimed at enhancing resource allocation
for high-speed vehicular users within the Fifth-Generation (5G) networks. The study …