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Found 7 papers, 1 papers with code
Joint Active and Passive Beamforming for IRS-Aided Wireless Energy Transfer Network Exploiting One-Bit Feedback
To reap the active and passive beamforming gain in an intelligent reflecting surface (IRS)-aided wireless network, a typical way is to first acquire the channel state information (CSI) relying on the pilot signal, and then perform the joint beamforming design.
Digital Twin-Enhanced Deep Reinforcement Learning for Resource Management in Networks Slicing
In this paper, a framework consisting of a digital twin and reinforcement learning agents is present to handle the issue.
Near-Field Channel Estimation for Extremely Large-Scale Array Communications: A model-based deep learning approach
In particular, we first formulate the XL-MIMO near-field channel estimation task as a compressed sensing problem using the spatial gridding-based sparsifying dictionary, and then solve the resulting problem by applying the Learning Iterative Shrinkage and Thresholding Algorithm (LISTA).
Unsupervised Recurrent Federated Learning for Edge Popularity Prediction in Privacy-Preserving Mobile Edge Computing Networks
In this article, we propose an unsupervised and privacy-preserving popularity prediction framework for MEC-enabled IIoT.
Hybrid Policy Learning for Energy-Latency Tradeoff in MEC-Assisted VR Video Service
Virtual reality (VR) is promising to fundamentally transform a broad spectrum of industry sectors and the way humans interact with virtual content.
Joint User Association and Power Allocation in Heterogeneous Ultra Dense Network via Semi-Supervised Representation Learning
We model the HUDNs as a heterogeneous graph and train a Graph Neural Network (GNN) to approach this representation function by using semi-supervised learning, in which the loss function is composed of the unsupervised part that helps the GNN approach the optimal representation function and the supervised part that utilizes the previous experience to reduce useless exploration.
ConvTransformer: A Convolutional Transformer Network for Video Frame Synthesis
To the best of our knowledge, this is the first time that ConvTransformer architecture is proposed and applied to video frame synthesis.
