shalini38 Goto Github PK

artificial_noise_securetransmission

Artificial Noise Assisted Secure Massive MIMO Transmission With Statistical CSI

cognitive-radio-networks

This project designs and develops a technique to analyze and correlate spectrum allocation of secondary users using optimization problem of linear cooperative spectrum sensing techniques.

cognitive-radio-system-upload

color-identification-in-image

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-using-cognitive-radio

Bachelor Thesis

cr-comp

This is a repository for the code developed to produce the results in the paper titled "Cognitive radio network with coordinated multipoint joint transmission" (http://onlinelibrary.wiley.com/doi/10.1002/dac.3310/abstract)

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.

hybrid-beamforming

This example introduces the basic concept of hybrid beamforming and shows how to split the precoding and combining weights using orthogonal matching pursuit algorithm. It shows that hybrid beamforming can closely match the performance offered by optimal digital weights. This program simulates a 64 x 16 MIMO hybrid beamforming system, with a 64-element square array with 4 RF chains on the transmitter side and a 16-element square array with 4 RF chains on the receiver side. Each antenna element can be connected to one or more TR modules.

nn_gwtc

Simulations for the paper "Deep Learning for the Gaussian Wiretap Channel" with Tensorflow 2

probability-of-false-alarm-vs-probability-of-detection-in-cognitive-radio-network

Probability of False Alarm vs Probability of Detection of Primary User in CRN using Different Fading Channel like Rayleigh, Rician, Nakagami channel.

vanruby.github.io

Our community website