Pinned Repositories
AdaptiveModulationML
Adaptive Modulation using k-NN classification for OFDM system
ANN-based-Spectrum-Sensing-for-Cognitive-Radio-Networks
Cooperative-energy-spectrum-sensing
Centralized cooperative spectrum sensing with soft fusion of energy measurements in highly mobile environments. Neyman-Pearson (NP) detection criterion. Three methods: LRT (assuming the instantaneous SNRs at the cognitive radios are known by the fusion center, Generalized LRT (GLRT), Online Expectation-Maximization (EM) based algorithm that jointly estimates the SNRs and detects the presence of primary signals.
Cooperative-spectrum-sensing
Cooperative-Spectrum-Sensing-Using-Cognitive-Radio
Bachelor Thesis
Deep-ADMM-Net
deep-learning-channel-estimation
Simulation code for “Channel Estimation in Massive MIMO under Hardware Non-Linearities: Bayesian Methods versus Deep Learning,” by Özlem Tugfe Demir, Emil Björnson, IEEE Open Journal of the Communications Society, To appear.
DeepLearningFlappyBird
Flappy Bird hack using Deep Reinforcement Learning (Deep Q-learning).
ITU-AI-ML-in-5G-USATU
Network trafic classification and network trafic forecasting
Python_CsiNet
Python code for "Deep Learning for Massive MIMO CSI Feedback"
rainWY's Repositories
rainWY/ITU-AI-ML-in-5G-USATU
Network trafic classification and network trafic forecasting
rainWY/Python_CsiNet
Python code for "Deep Learning for Massive MIMO CSI Feedback"
rainWY/ANN-based-Spectrum-Sensing-for-Cognitive-Radio-Networks
rainWY/Cooperative-energy-spectrum-sensing
Centralized cooperative spectrum sensing with soft fusion of energy measurements in highly mobile environments. Neyman-Pearson (NP) detection criterion. Three methods: LRT (assuming the instantaneous SNRs at the cognitive radios are known by the fusion center, Generalized LRT (GLRT), Online Expectation-Maximization (EM) based algorithm that jointly estimates the SNRs and detects the presence of primary signals.
rainWY/Cooperative-spectrum-sensing
rainWY/Cooperative-Spectrum-Sensing-Using-Cognitive-Radio
Bachelor Thesis
rainWY/deep-learning-channel-estimation
Simulation code for “Channel Estimation in Massive MIMO under Hardware Non-Linearities: Bayesian Methods versus Deep Learning,” by Özlem Tugfe Demir, Emil Björnson, IEEE Open Journal of the Communications Society, To appear.
rainWY/DeepLearningFlappyBird
Flappy Bird hack using Deep Reinforcement Learning (Deep Q-learning).
rainWY/Denoising-HOM-for-DMI
This is the Matlab code for the paper "Denoising Higher-Order Moments for Blind Digital Modulation Identification in Multiple-Antenna Systems" published in the IEEE Wireless Communications Letters.
rainWY/DNN_Optimization-in-Cooperative-Spectrum-Sensing-s
rainWY/Double-Deep-Q-Learning-for-Resource-Allocation
Reproduce results of the research article "Deep Reinforcement Learning Based Resource Allocation for V2V Communications"
rainWY/FS-SEI
A few-shot learning method for specific emitter identification or radio frequency fingerprintinig
rainWY/Gaussian-Mixture-Model
This is a part of MATLAB implementation of the paper "Machine Learning Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks" in which Gaussian Mixture Model clustering is employed.
rainWY/hierarchical-clustering-for-Cooperative-Spectrum-Sensing
rainWY/https-github.com-iyytdeed-Automatic-Modulation-Classification
rainWY/ITU-Challenge-ML5G-PHY
Examples of implementations with the Raymobtime database available for ITU AI/ML 5G Challenge
rainWY/Keras-CNNVisualization
understanding of CNN with various visualization techniques
rainWY/Keras-GAN
Keras implementations of Generative Adversarial Networks.
rainWY/L-BFGS-B-C
L-BFGS-B, converted from Fortran to C, with Matlab wrapper
rainWY/LSTM-based-Spectrum-Sensing-for-Cognitive-Radio-Networks
rainWY/MARS-IEEE_IoT-HAR
rainWY/MCNet
MCNet: An Efficient CNN Architecture for Robust Automatic Modulation Classification
rainWY/MMNet
rainWY/PyTorch-Tutorial
Build your neural network easy and fast
rainWY/RIS-fading
Simulation code for “Rayleigh Fading Modeling and Channel Hardening for Reconfigurable Intelligent Surfaces, IEEE Wireless Communications Letters, to appear.
rainWY/RIS-myths
Simulation code for “Reconfigurable Intelligent Surfaces: Three Myths and Two Critical Questions,” by Emil Björnson, Özgecan Özdogan, Erik G. Larsson, IEEE Communications Magazine, vol. 58, no. 12, pp. 90-96, December 2020.
rainWY/Spectrum_sensing
rainWY/Spectrum_sensing_using_ML
rainWY/ss_noma
Simulations of spectrum sensing based on cyclostationary process analysis in the NOMA channel.
rainWY/Yolo_Siamese_CenterTripletLoss
Yolo net combined with siames net or center&triplet loss to recognize and classificate traffic signs