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Found 32 papers, 3 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.
Low-Complexity Joint Beamforming for RIS-Assisted MU-MISO Systems Based on Model-Driven Deep Learning
Reconfigurable intelligent surfaces (RIS) can improve signal propagation environments by adjusting the phase of the incident signal.
Semantic Communications using Foundation Models: Design Approaches and Open Issues
Foundation models (FMs), including large language models, have become increasingly popular due to their wide-ranging applicability and ability to understand human-like semantics.
Gradient-Based Markov Chain Monte Carlo for MIMO Detection
However, optimal MIMO detection is associated with a complexity that grows exponentially with the MIMO dimensions and quickly becomes impractical.
RIS-Enhanced Semantic Communications Adaptive to User Requirements
Simulation results demonstrate the adaptability and efficiency of the RIS-SC framework across diverse channel conditions and user requirements.
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.
Wireless Semantic Transmission via Revising Modules in Conventional Communications
Therefore, the novel semantic-based coding methods and performance metrics have been investigated and the designed semantic systems consist of various modules as in the conventional communications but with improved functions.
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.
Wireless Semantic Communications for Video Conferencing
In this paper, we initially establish a basal semantic video conferencing (SVC) network, which dramatically reduces transmission resources while only losing detailed expressions.
Deep Source-Channel Coding for Sentence Semantic Transmission with HARQ
Even if semantic communication has been successfully applied in the sentence transmission to reduce semantic errors, existing architecture is usually fixed in the codeword length and is inefficient and inflexible for the varying sentence length.
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.
Adaptive Channel Estimation Based on Model-Driven Deep Learning for Wideband mmWave Systems
Channel estimation in wideband millimeter-wave (mmWave) systems is very challenging due to the beam squint effect.
Fast Antenna and Beam Switching Method for mmWave Handsets with Hand Blockage
In this method, only one antenna module is used for the reception to predict the best beam of other antenna modules.
Phase Retrieval using Expectation Consistent Signal Recovery Algorithm based on Hypernetwork
In particular, we introduce a hypernetwork to generate the damping factors for GEC-SR.
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.
Meta Learning-based MIMO Detectors: Design, Simulation, and Experimental Test
An unfolded turbo decoding module, called TurboNet, is used for channel decoding.
Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO: A Model-Driven Unsupervised Learning Approach
By utilizing the Stein's unbiased risk estimator loss, the LDGEC network can be trained only with limited measurements corresponding to the pilot symbols, instead of the real channel data.
Model-Driven Deep Learning for Massive Multiuser MIMO Constant Envelope Precoding
Constant envelope (CE) precoding design is of great interest for massive multiuser multi-input multi-output systems because it can significantly reduce hardware cost and power consumption.
Model-Driven DNN Decoder for Turbo Codes: Design, Simulation and Experimental Results
The TurboNet inherits the superiority of the max-log-MAP algorithm and DL tools and thus presents excellent error-correction capability with low training cost.
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
Model-Driven Deep Learning for MIMO Detection
In this paper, we investigate the model-driven deep learning (DL) for MIMO detection.
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
Gridless Variational Bayesian Channel Estimation for Antenna Array Systems with Low Resolution ADCs
Employing low-resolution analog-to-digital converters (ADCs) coupled with large antenna arrays at the receivers has drawn considerable interests in the millimeter wave (mm-wave) system.
Signal Processing Information Theory Information Theory
Deep Learning Based on Orthogonal Approximate Message Passing for CP-Free OFDM
The DL-OAMP receiver includes a channel estimation neural network (CE-Net) and a signal detection neural network based on OAMP, called OAMP-Net.
Artificial Intelligence-aided Receiver for A CP-Free OFDM System: Design, Simulation, and Experimental Test
The AI receiver includes a channel estimation neural network (CE-NET) and a signal detection neural network based on orthogonal approximate message passing (OAMP), called OAMP-NET.
Information Theory Information Theory
AI-Aided Online Adaptive OFDM Receiver: Design and Experimental Results
From the OTA test, the AI-aided OFDM receivers, especially the SwitchNet receiver, are robust to real environments and promising for future communication systems.
Model-Driven Deep Learning for Physical Layer Communications
Intelligent communication is gradually considered as the mainstream direction in future wireless communications.
