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 based traffic-and channel-aware OFDMA resource allocation
R Balakrishnan, K Sankhe… - 2019 IEEE Global …, 2019 - ieeexplore.ieee.org
Efficient radio resource allocation is a fundamental optimization problem for wireless
networks, and has been widely studied in the past. However, wireless systems are evolving …
networks, and has been widely studied in the past. However, wireless systems are evolving …
Deep reinforcement learning based power minimization for RIS-assisted MISO-OFDM systems
In this paper, we investigate the downlink orthogonal frequency division multiplexing
(OFDM) transmission system assisted by reconfigurable intelligent surfaces (RISs) …
(OFDM) transmission system assisted by reconfigurable intelligent surfaces (RISs) …
Resource allocation in information-centric wireless networking with D2D-enabled MEC: A deep reinforcement learning approach
Recently, information-centric wireless networks (ICWNs) have become a promising Internet
architecture of the next generation, which allows network nodes to have computing and …
architecture of the next generation, which allows network nodes to have computing and …
Resource allocation based on deep reinforcement learning in IoT edge computing
By leveraging mobile edge computing (MEC), a huge amount of data generated by Internet
of Things (IoT) devices can be processed and analyzed at the network edge. However, the …
of Things (IoT) devices can be processed and analyzed at the network edge. However, the …
Joint user scheduling, phase shift control, and beamforming optimization in intelligent reflecting surface-aided systems
In this paper, we formulate a joint uplink scheduling, phase shift control, and beamforming
optimization problem in intelligent reflecting surface (IRS)-aided systems. We consider …
optimization problem in intelligent reflecting surface (IRS)-aided systems. We consider …
Dynamic channel access and power control in wireless interference networks via multi-agent deep reinforcement learning
Due to the scarcity in the wireless spectrum and limited energy resources especially in
mobile applications, efficient resource allocation strategies are critical in wireless networks …
mobile applications, efficient resource allocation strategies are critical in wireless networks …
[HTML][HTML] Deep reinforcement learning-based resource allocation for D2D communications in heterogeneous cellular networks
Abstract Device-to-Device (D2D) communication-enabled Heterogeneous Cellular Networks
(HCNs) have been a promising technology for satisfying the growing demands of smart …
(HCNs) have been a promising technology for satisfying the growing demands of smart …
Multi-agent deep reinforcement learning-based power control and resource allocation for D2D communications
H Xiang, Y Yang, G He, J Huang… - IEEE Wireless …, 2022 - ieeexplore.ieee.org
Device-to-device (D2D) communications are envisioned as a critical technology to support
future ubiquitous mobile communications applications. However, the requirements of high …
future ubiquitous mobile communications applications. However, the requirements of high …
Deep reinforcement learning based resource management for DNN inference in industrial IoT
Performing deep neural network (DNN) inference in real time requires excessive network
resources, which poses a big challenge to the resource-limited industrial Internet of things …
resources, which poses a big challenge to the resource-limited industrial Internet of things …
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