Deep multi-user reinforcement learning for distributed dynamic spectrum access
O Naparstek, K Cohen - IEEE transactions on wireless …, 2018 - ieeexplore.ieee.org
We consider the problem of dynamic spectrum access for network utility maximization in
multichannel wireless networks. The shared bandwidth is divided into K orthogonal …
multichannel wireless networks. The shared bandwidth is divided into K orthogonal …
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 …
Radio resource allocation for device-to-device underlay communication using hypergraph theory
Device-to-device (D2D) communication has been recognized as a promising technique to
offload the traffic for the evolved Node B (eNB). However, D2D transmission as an underlay …
offload the traffic for the evolved Node B (eNB). However, D2D transmission as an underlay …
Multi-agent reinforcement learning for dynamic resource management in 6G in-X subnetworks
The 6G network enables a subnetwork-wide evolution, resulting in a “network of
subnetworks”. However, due to the dynamic mobility of wireless subnetworks, the data …
subnetworks”. However, due to the dynamic mobility of wireless subnetworks, the data …
Double deep Q-network based distributed resource matching algorithm for D2D communication
Y Yuan, Z Li, Z Liu, Y Yang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Device-to-Device (D2D) communication with short communication distance is an efficient
way to improve spectrum efficiency and mitigate interference. To realize the optimal …
way to improve spectrum efficiency and mitigate interference. To realize the optimal …
Distributed learning over Markovian fading channels for stable spectrum access
We consider the problem of multi-user spectrum access in wireless networks. The bandwidth
is divided into orthogonal channels, and users aim to access the spectrum. Each user …
is divided into orthogonal channels, and users aim to access the spectrum. Each user …
Dynamic spectrum access for D2D-enabled Internet of Things: A deep reinforcement learning approach
Device-to-device (D2D) communication is regarded as a promising technology to support
spectral-efficient Internet of Things (IoT) in beyond fifth-generation (5G) and sixth-generation …
spectral-efficient Internet of Things (IoT) in beyond fifth-generation (5G) and sixth-generation …
Spectrum intelligent radio: Technology, development, and future trends
The advent of Industry 4.0 with massive connectivity places significant strains on the current
spectrum resources, and challenges the industry and regulators to respond promptly with …
spectrum resources, and challenges the industry and regulators to respond promptly with …
Distributed learning algorithms for spectrum sharing in spatial random access wireless networks
We consider distributed optimization over orthogonal collision channels in spatial random
access networks. Users are spatially distributed and each user is in the interference range of …
access networks. Users are spatially distributed and each user is in the interference range of …
Deep recurrent reinforcement learning-based distributed dynamic spectrum access in multichannel wireless networks with imperfect feedback
This paper investigates Dynamic Spectrum Access (DSA) paradigm with imperfect feedback
for multiuser wireless network. Each user selects an orthogonal channel in particular time …
for multiuser wireless network. Each user selects an orthogonal channel in particular time …