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Found 17 papers, 7 papers with code
Enhancing Sample Utilization through Sample Adaptive Augmentation in Semi-Supervised Learning
Sample adaptive augmentation (SAA) is proposed for this stated purpose and consists of two modules: 1) sample selection module; 2) sample augmentation module.
Rogue Emitter Detection Using Hybrid Network of Denoising Autoencoder and Deep Metric Learning
Rogue emitter detection (RED) is a crucial technique to maintain secure internet of things applications.
Few-Shot Specific Emitter Identification via Hybrid Data Augmentation and Deep Metric Learning
Specific emitter identification (SEI) is a potential physical layer authentication technology, which is one of the most critical complements of upper layer authentication.
Semi-Supervised Specific Emitter Identification Method Using Metric-Adversarial Training
Specifically, pseudo labels are innovatively introduced into metric learning to enable semi-supervised metric learning (SSML), and an objective function alternatively regularized by SSML and virtual adversarial training (VAT) is designed to extract discriminative and generalized semantic features of radio signals.
Ultra Lite Convolutional Neural Network for Fast Automatic Modulation Classification in Low-Resource Scenarios
Automatic modulation classification (AMC) is a key technique for designing non-cooperative communication systems, and deep learning (DL) is applied effectively to AMC for improving classification accuracy.
Few-Shot Specific Emitter Identification via Deep Metric Ensemble Learning
Thus, we focus on few-shot SEI (FS-SEI) for aircraft identification via automatic dependent surveillance-broadcast (ADS-B) signals, and a novel FS-SEI method is proposed, based on deep metric ensemble learning (DMEL).
A Real-World Radio Frequency Signal Dataset Based on LTE System and Variable Channels
However, most of these datasets are collected from 2. 4G WiFi devices and through similar channel environments.
Nonconvex ${L_ {1/2}} $-Regularized Nonlocal Self-similarity Denoiser for Compressive Sensing based CT Reconstruction
In this paper, we develop a ${{L_ {{1/2}}}} $-regularized nonlocal self-similarity (NSS) denoiser for CT reconstruction problem, which integrates low-rank approximation with group sparse coding (GSC) framework.
A Novel Channel Identification Architecture for mmWave Systems Based on Eigen Features
Hence, the ideal communication environment for mmWave is line of sight (LOS) channel.
Sum-Rate Maximization in Distributed Intelligent Reflecting Surfaces-Aided mmWave Communications
The solution to transmit beamforming at the BS and the phase shifts at the IRS are derived by using the successive convex approximation (SCA)-based algorithm, and a greedy algorithm is proposed to design the IRS switch vector.
Uplink Achievable Rate Maximization for Reconfigurable Intelligent Surface Aided Millimeter Wave Systems with Resolution-Adaptive ADCs
In this letter, we investigate the uplink of a reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) multi-user system.
Reconfigurable Intelligent Surfaces Aided mmWave NOMA: Joint Power Allocation,Phase Shifts, and Hybrid Beamforming Optimization
Then, we solve the power allocation problem under fixed phase shifts of the RIS and hybrid beamforming.
Secure Beamforming for Multiple Intelligent Reflecting Surfaces Aided mmWave Systems
In this system, the secrecy rate is maximized by controlling the on-off status of each IRS as well as optimizing the phase shift matrix of the IRSs.
Nonconvex Nonsmooth Low-Rank Minimization for Generalized Image Compressed Sensing via Group Sparse Representation
The popular L_2-norm and M-estimator are employed for standard image CS and robust CS problem to fit the data respectively.
From Group Sparse Coding to Rank Minimization: A Novel Denoising Model for Low-level Image Restoration
Recently, low-rank matrix recovery theory has been emerging as a significant progress for various image processing problems.
Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions
The new demands for high-reliability and ultra-high capacity wireless communication have led to extensive research into 5G communications.
ADMM-IDNN: Iteratively Double-reweighted Nuclear Norm Algorithm for Group-prior based Nonconvex Compressed Sensing via ADMM
Group-prior based regularization method has led to great successes in various image processing tasks, which can usually be considered as a low-rank matrix minimization problem.
Image and Video Processing
