On green-energy-powered cognitive radio networks
A green-energy-powered cognitive radio (CR) network is capable of liberating the wireless
access networks from spectral and energy constraints. The limitation of the spectrum is …
access networks from spectral and energy constraints. The limitation of the spectrum is …
Deep reinforcement learning for dynamic spectrum sensing and aggregation in multi-channel wireless networks
Y Li, W Zhang, CX Wang, J Sun… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
In this paper, the problem of dynamic spectrum sensing and aggregation is investigated in a
wireless network containing N correlated channels, where these channels are occupied or …
wireless network containing N correlated channels, where these channels are occupied or …
Deep reinforcement learning for robust beamforming in IRS-assisted wireless communications
Intelligent reflecting surface (IRS) is a promising technology to assist downlink information
transmissions from a multi-antenna access point (AP) to a receiver. In this paper, we …
transmissions from a multi-antenna access point (AP) to a receiver. In this paper, we …
An actor-critic deep reinforcement learning approach for transmission scheduling in cognitive internet of things systems
H Yang, X Xie - IEEE Systems Journal, 2019 - ieeexplore.ieee.org
The cognitive Internet of Things (CIoT) has attracted much interest recently in wireless
networks due to its wide applications in smart cities, intelligent transportation systems, and …
networks due to its wide applications in smart cities, intelligent transportation systems, and …
Deep reinforcement learning for dynamic spectrum access in wireless networks
Y Xu, J Yu, WC Headley… - MILCOM 2018-2018 IEEE …, 2018 - ieeexplore.ieee.org
This paper investigates the use of deep reinforcement learning (DRL) to solve the dynamic
spectrum access problem. Specifically, we examine the scenario where multiple discrete …
spectrum access problem. Specifically, we examine the scenario where multiple discrete …
On the outage performance of ambient backscatter communications
Ambient backscatter communications (AmBackComs) have been recognized as a spectrum-
and energy-efficient technology for the Internet of Things, as it allows passive backscatter …
and energy-efficient technology for the Internet of Things, as it allows passive backscatter …
Intelligent resource allocation for IRS-enhanced OFDM communication systems: A hybrid deep reinforcement learning approach
Orthogonal frequency division multiplexing (OFDM) systems have been widely applied in
practice since OFDM has diverse outstanding advantages. However, their performance …
practice since OFDM has diverse outstanding advantages. However, their performance …
Deep reinforcement learning for backscatter-aided data offloading in mobile edge computing
Wireless network optimization has been becoming very challenging as the problem size and
complexity increase tremendously, due to close couplings among network entities with …
complexity increase tremendously, due to close couplings among network entities with …
RDRL: A recurrent deep reinforcement learning scheme for dynamic spectrum access in reconfigurable wireless networks
Reconfigurable wireless network can flexibly provide efficient spectrum access service and
keep stable operation in highly dynamic environment. In this paper, a primary-prioritized …
keep stable operation in highly dynamic environment. In this paper, a primary-prioritized …
Intelligent power control for spectrum sharing in cognitive radios: A deep reinforcement learning approach
We consider the problem of spectrum sharing in a cognitive radio system consisting of a
primary user and a secondary user. The primary user and the secondary user work in a non …
primary user and a secondary user. The primary user and the secondary user work in a non …