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Found 16 papers, 1 papers with code
Auto-CsiNet: Scenario-customized Automatic Neural Network Architecture Generation for Massive MIMO CSI Feedback
This paper proposes using neural architecture search (NAS) to automate the generation of scenario-customized CSI feedback NN architectures, thereby maximizing the potential of deep learning in exclusive environments.
Knowledge-driven Meta-learning for CSI Feedback
In this article, a knowledge-driven meta-learning approach is proposed, where the DL model initialized by the meta model obtained from meta training phase is able to achieve rapid convergence when facing a new scenario during target retraining phase.
Demo: Reconfigurable Distributed Antennas and Reflecting Surface (RDARS)-aided Integrated Sensing and Communication System
Integrated sensing and communication (ISAC) system has been envisioned as a promising technology to be applied in future applications requiring both communication and high-accuracy sensing.
Communication-efficient Personalized Federated Edge Learning for Massive MIMO CSI Feedback
Additionally, since the heterogeneity of CSI datasets in different UEs can degrade the performance of the FEEL-based framework, we introduce a personalization strategy to improve feedback performance.
Lightweight Neural Network with Knowledge Distillation for CSI Feedback
Firstly, an autoencoder KD-based method is introduced by training a student autoencoder to mimic the reconstructed CSI of a pretrained teacher autoencoder.
AI for CSI Feedback Enhancement in 5G-Advanced
This article provides a guideline for the standardization study of AI-based CSI feedback enhancement.
Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems
Many performance gains achieved by massive multiple-input and multiple-output depend on the accuracy of the downlink channel state information (CSI) at the transmitter (base station), which is usually obtained by estimating at the receiver (user terminal) and feeding back to the transmitter.
Multi-task Learning-based CSI Feedback Design in Multiple Scenarios
One efficient CSI feedback method is the Auto-Encoder (AE) structure based on deep learning, yet facing problems in actual deployments, such as selecting the deployment mode when deploying in a cell with multiple complex scenarios.
Deep Learning-based Implicit CSI Feedback in Massive MIMO
By using environment information, the NNs can achieve a more refined mapping between the precoding matrix and the PMI compared with codebooks.
Anisotropic active Brownian particle with a fluctuating propulsion force
We present a 2D model where the fluctuations arise from nonthermal noise in a propelling force acting at a single point, such as that due to a flagellum.
Soft Condensed Matter Statistical Mechanics Fluid Dynamics
CAnet: Uplink-aided Downlink Channel Acquisition in FDD Massive MIMO using Deep Learning
The user equipment in the latter one directly feeds back the received pilot signals to the base station.
Lightweight Convolutional Neural Networks for CSI Feedback in Massive MIMO
We develop a DL based CSI feedback network in this study to complete the feedback of CSI effectively.
Information Theory Signal Processing Information Theory
Deep Learning-based CSI Feedback for RIS-assisted Multi-user Systems
These enhancements are tasked with the precise retrieval and fusion of shared and individual data.
Information Theory Signal Processing Information Theory
Compression and Acceleration of Neural Networks for Communications
Deep learning (DL) has achieved great success in signal processing and communications and has become a promising technology for future wireless communications.
Information Theory Signal Processing Information Theory
Convolutional Neural Network based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis
Massive multiple-input multiple-output (MIMO) is a promising technology to increase link capacity and energy efficiency.
Signal Processing Information Theory Information Theory
A deep learning-based method for relative location prediction in CT scan images
Relative location prediction in computed tomography (CT) scan images is a challenging problem.
