Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectrum sensing.

Last update: Dec 14, 2022

Overview

Deep-Learning-based-Spectrum-Sensing

Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectrum sensing.

The files in MATLAB are used to simulate signal and extract features. Use cs_feature.m to receive QPSK signals in AWGN channels and extract cyclostationary features of different signal, and save the .mat data for training.

The files in Python are use to train deep networks. Build a dataset "dataset" containing two typs of signals, use SignalDataloader.py to load the data and use train.py to train a deep linear network, and calculate the probability of detection and the probability of falsealarm.

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GitHub Repository

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Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectrum sensing.

Related tags

Overview

Deep-Learning-based-Spectrum-Sensing

Owner

AI Summer's complete catalog of articles

Pull sensitive data from users on windows including discord tokens and chrome data.

Points2Surf: Learning Implicit Surfaces from Point Clouds (ECCV 2020 Spotlight)

Home for cuQuantum Python & NVIDIA cuQuantum SDK C++ samples

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Res2Net for Instance segmentation and Object detection using MaskRCNN

code for "Feature Importance-aware Transferable Adversarial Attacks"

Volumetric Correspondence Networks for Optical Flow, NeurIPS 2019.

Official PyTorch implementation of the Fishr regularization for out-of-distribution generalization

Code for ICMI2020 and ICMI2021 papers: "Studying Person-Specific Pointing and Gaze Behavior for Multimodal Referencing of Outside Objects from a Moving Vehicle" and "ML-PersRef: A Machine Learning-based Personalized Multimodal Fusion Approach for Referencing Outside Objects From a Moving Vehicle"

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