Dynamic multichannel access via multi-agent reinforcement learning: Throughput and fairness guarantees
M Sohaib, J Jeong, SW Jeon - IEEE Transactions on Wireless …, 2021 - ieeexplore.ieee.org
We consider a multichannel random access system in which each user accesses a single
channel at each time slot to communicate with an access point (AP). Users arrive to the …
channel at each time slot to communicate with an access point (AP). Users arrive to the …
Smart multi-RAT access based on multiagent reinforcement learning
The ongoing increasing traffic in the era of big data yields unprecedented demands in user
experience and network capacity expansion. The users of next generation mobile networks …
experience and network capacity expansion. The users of next generation mobile networks …
Deep reinforcement learning for RSMA-based multi-functional wireless networks
The upcoming sixth generation (6G) is expected to support a wide range of applications that
require efficient sensing, accurate localization, and reliable communication capabilities …
require efficient sensing, accurate localization, and reliable communication capabilities …
Joint radio map construction and dissemination in MEC networks: a deep reinforcement learning approach
X Liu, L Zhou, X Zhang, X Tan… - … and Mobile Computing, 2022 - Wiley Online Library
With the development of 6G, the rapidly increasing number of smart devices deployed in the
Industrial Internet of Things (IIoT) environment has been witnessed. The radio environment …
Industrial Internet of Things (IIoT) environment has been witnessed. The radio environment …
Distributed convolutional deep reinforcement learning based OFDMA MAC for 802.11 ax
D Kotagiri, K Nihei, T Li - ICC 2021-IEEE International …, 2021 - ieeexplore.ieee.org
The IEEE 802.11 ax also known as Wi-Fi 6, incorporates multi-user (MU) Orthogonal
Frequency Division Multiple Access (OFDMA) based distributed up-link communication, in …
Frequency Division Multiple Access (OFDMA) based distributed up-link communication, in …
Multi-agent driven resource allocation and interference management for deep edge networks
Sixth generation mobile networks (6G) may experience a huge evolution on vertical industry
scenarios, where deep edge networks () become an important network structure for the …
scenarios, where deep edge networks () become an important network structure for the …
Deep reinforcement learning based massive access management for ultra-reliable low-latency communications
With the rapid deployment of the Internet of Things (IoT), fifth-generation (5G) and beyond
5G networks are required to support massive access of a huge number of devices over …
5G networks are required to support massive access of a huge number of devices over …
Low latency radio access in 3GPP local area data networks for V2X: Stochastic optimization and learning
The next generation vehicular applications substantially shifting the paradigm of human
activity have been projected to empower intelligent transportation systems. Targeting at …
activity have been projected to empower intelligent transportation systems. Targeting at …
Heterogeneous machine-type communications in cellular networks: Random access optimization by deep reinforcement learning
Z Chen, DB Smith - 2018 IEEE International Conference on …, 2018 - ieeexplore.ieee.org
One of the significant challenges for managing machine-to-machine (M2M) communication
in cellular networks, such as LTE-A, is the overload of the radio access network due to very …
in cellular networks, such as LTE-A, is the overload of the radio access network due to very …
Deep reinforcement learning for user association and resource allocation in heterogeneous cellular networks
Heterogeneous cellular networks can offload the mobile traffic and reduce the deployment
costs, which have been considered to be a promising technique in the next-generation …
costs, which have been considered to be a promising technique in the next-generation …