Deep-reinforcement learning multiple access for heterogeneous wireless networks
This paper investigates a deep reinforcement learning (DRL)-based MAC protocol for
heterogeneous wireless networking, referred to as a Deep-reinforcement Learning Multiple …
heterogeneous wireless networking, referred to as a Deep-reinforcement Learning Multiple …
A Dueling Deep Recurrent Q‐Network Framework for Dynamic Multichannel Access in Heterogeneous Wireless Networks
H Chen, H Zhao, L Zhou, J Zhang, Y Liu… - Wireless …, 2022 - Wiley Online Library
This paper investigates a deep reinforcement learning algorithm based on dueling deep
recurrent Q‐network (Dueling DRQN) for dynamic multichannel access in heterogeneous …
recurrent Q‐network (Dueling DRQN) for dynamic multichannel access in heterogeneous …
Model-based deep learning optimization of IEEE 802.11 VANETs for safety applications
IEEE 802.11 p/bd driven Vehicular Ad Hoc Networks (VANETs) have been investigated for
safety-critical applications with high reliability and low transmission latency. However, due to …
safety-critical applications with high reliability and low transmission latency. However, due to …
Artificial intelligence based learning for wireless application–A survey
The wireless networks of the future generation are evolved as complex systems due to
broadening in service prerequisites, application heterogeneity and networking of gadgets. In …
broadening in service prerequisites, application heterogeneity and networking of gadgets. In …
Deep Q-learning-based transmission power control of a high altitude platform station with spectrum sharing
A High Altitude Platform Station (HAPS) can facilitate high-speed data communication over
wide areas using high-power line-of-sight communication; however, it can significantly …
wide areas using high-power line-of-sight communication; however, it can significantly …
A deep reinforcement learning scheme for sum rate and fairness maximization among d2d pairs underlaying cellular network with noma
Device-to-device (D2D) communication is an emerging technology in 5G and the upcoming
6G networks due to its properties to enhanced sum rate. Despite these advantages, co …
6G networks due to its properties to enhanced sum rate. Despite these advantages, co …
Collaborative multi-BS power management for dense radio access network using deep reinforcement learning
Network energy efficiency is a main pillar in the design and operation of wireless
communication systems. In this paper, we investigate a dense radio access network (dense …
communication systems. In this paper, we investigate a dense radio access network (dense …
RO‐RAW: Run‐Time Restricted Access Window Optimization in IEEE 802.11 ah Network with Extended Kalman Filter
Z Liu, P Lv - Wireless Communications and Mobile Computing, 2020 - Wiley Online Library
In 2016, the IEEE task group ah (TGah) published a new standard IEEE 802.11 ah, aimed at
providing network connectivity among a large number of Internet of Things (IoT) devices …
providing network connectivity among a large number of Internet of Things (IoT) devices …
Deep reinforcement learning for dynamic spectrum access in the multi-channel wireless local area networks
S Bhandari, N Ranjan, YC Kim… - … Conference on Electronics …, 2022 - ieeexplore.ieee.org
In recent years, the rapid proliferation of wireless local area networks (WLANs) has led to a
scarcity of radio spectrum. Dynamic Spectrum Access (DSA) is considered a promising …
scarcity of radio spectrum. Dynamic Spectrum Access (DSA) is considered a promising …
Improving IEEE 802.11 ax UORA performance: Comparison of reinforcement learning and heuristic approaches
Machine learning (ML) has gained attention from the network research community because
it can help solve difficult problems and potentially lead to groundbreaking achievements. In …
it can help solve difficult problems and potentially lead to groundbreaking achievements. In …