Multi-agent deep reinforcement learning based resource allocation for ultra-reliable low-latency internet of controllable things
Y Xiao, Y Song, J Liu - IEEE Transactions on Wireless …, 2023 - ieeexplore.ieee.org
As a promising technology in the 5G era, the artificial intelligence (AI) enabled Internet of
controllable things (IoCT) is expected to be an integral part of heterogeneous networks …
controllable things (IoCT) is expected to be an integral part of heterogeneous networks …
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 …
Multi-agent deep reinforcement learning for dynamic power allocation in wireless networks
This work demonstrates the potential of deep reinforcement learning techniques for transmit
power control in wireless networks. Existing techniques typically find near-optimal power …
power control in wireless networks. Existing techniques typically find near-optimal power …
Non-cooperative energy efficient power allocation game in D2D communication: A multi-agent deep reinforcement learning approach
Recently, there is the widespread use of mobile devices and sensors, and rapid emergence
of new wireless and networking technologies, such as wireless sensor network, device-to …
of new wireless and networking technologies, such as wireless sensor network, device-to …
A deep reinforcement learning for user association and power control in heterogeneous networks
Heterogeneous network (HetNet) is a promising solution to satisfy the unprecedented
demand for higher data rate in the next generation mobile networks. Different from the …
demand for higher data rate in the next generation mobile networks. Different from the …
Intelligent reflecting surface assisted multi-user OFDMA: Channel estimation and training design
To achieve the full passive beamforming gains of intelligent reflecting surface (IRS),
accurate channel state information (CSI) is indispensable but practically challenging to …
accurate channel state information (CSI) is indispensable but practically challenging to …
Joint beamforming and phase shift optimization for multicell IRS-aided OFDMA-URLLC systems
This paper investigates the resource allocation algorithm design for intelligent reflecting
surface (IRS) aided multiple-input single-output (MISO) orthogonal frequency division …
surface (IRS) aided multiple-input single-output (MISO) orthogonal frequency division …
iRAF: A deep reinforcement learning approach for collaborative mobile edge computing IoT networks
Recently, as the development of artificial intelligence (AI), data-driven AI methods have
shown amazing performance in solving complex problems to support the Internet of Things …
shown amazing performance in solving complex problems to support the Internet of Things …
Power optimization in device-to-device communications: A deep reinforcement learning approach with dynamic reward
Device-to-Device (D2D) communication can be used to improve system capacity and energy
efficiency (EE) in cellular networks. One of the critical challenges in D2D communications is …
efficiency (EE) in cellular networks. One of the critical challenges in D2D communications is …
Joint optimization of data offloading and resource allocation with renewable energy aware for IoT devices: A deep reinforcement learning approach
H Ke, J Wang, H Wang, Y Ge - IEEE Access, 2019 - ieeexplore.ieee.org
A large number of connected sensors and devices in Internet of Things (IoT) can generate
large amounts of computing data and increase massive energy consumption. Real-time …
large amounts of computing data and increase massive energy consumption. Real-time …