Multiaccess point coordination for next-gen Wi-Fi networks aided by deep reinforcement learning
Wi-Fi in the enterprise—characterized by overlapping Wi-Fi cells—constitutes the design
challenge for next-generation networks. Standardization for recently started IEEE 802.11 be …
challenge for next-generation networks. Standardization for recently started IEEE 802.11 be …
Machine Learning and Deep Reinforcement Learning in Wireless Networks and Communication Applications
Wireless networks and communication of the future will have to manage an ever-increasing
density of mobile users using a wide range of services and apps, as well as a surge in …
density of mobile users using a wide range of services and apps, as well as a surge in …
Artificial intelligence-based handoff management for dense WLANs: A deep reinforcement learning approach
Z Han, T Lei, Z Lu, X Wen, W Zheng, L Guo - IEEE Access, 2019 - ieeexplore.ieee.org
So far, the handoff management involved in the wireless local area network (WLAN) has
mainly fallen into the handoff mechanism and the decision algorithm. The traditional handoff …
mainly fallen into the handoff mechanism and the decision algorithm. The traditional handoff …
Deep Q‐learning based resource allocation in industrial wireless networks for URLLC
Ultra‐reliable low‐latency communication (URLLC) is one of the promising services offered
by fifth‐generation technology for an industrial wireless network. Moreover, reinforcement …
by fifth‐generation technology for an industrial wireless network. Moreover, reinforcement …
Performance optimization of QoS-supported dense WLANs using machine-learning-enabled enhanced distributed channel access (MEDCA) mechanism
Quality of service (QoS) implementation in a wireless local area network (WLAN) enables
the prediction of network performance and utilization of effective bandwidth for multimedia …
the prediction of network performance and utilization of effective bandwidth for multimedia …
Study of contention window adjustment for csma/ca by using machine learning
YW Chen, KC Kao - … 22nd Asia-Pacific Network Operations and …, 2021 - ieeexplore.ieee.org
In IEEE 802.11, CSMA/CA protocol applies the exponential backoff scheme to relax the
contention problems among different clients wishing to transmit data at the same time. A …
contention problems among different clients wishing to transmit data at the same time. A …
Resource slicing and customization in RAN with dueling deep Q-network
The emerging future generation 5G technology is expected to support service-oriented
virtualized networks where different network applications provide unique services. 5G …
virtualized networks where different network applications provide unique services. 5G …
Deep reinforcement learning for reducing latency in mission critical services
M Elsayed, M Erol-Kantarci - 2018 IEEE Global …, 2018 - ieeexplore.ieee.org
Next-generation wireless networks will be supporting mission critical services such as safety
related applications of connected autonomous vehicles, and real-time control of medical and …
related applications of connected autonomous vehicles, and real-time control of medical and …
A Q-learning approach for adjusting CWS and TxOP in LAA for Wi-Fi and LAA coexisting networks
TT Pan, IS Lai, SJ Kao… - International Journal of …, 2023 - inderscienceonline.com
This paper proposes a new approach to adjust values of both Contention Window Size
(CWS) and Transmission Opportunity (TxOP) simultaneously by Q-learning algorithm. We …
(CWS) and Transmission Opportunity (TxOP) simultaneously by Q-learning algorithm. We …
Survey on reinforcement learning applications in communication networks
Y Qian, J Wu, R Wang, F Zhu… - … of Communications and …, 2019 - ieeexplore.ieee.org
In recent years, intelligent communication has drawn huge research efforts in both academia
and industry. With the advent of 5G technology, intelligent wireless terminals and intelligent …
and industry. With the advent of 5G technology, intelligent wireless terminals and intelligent …