Simulation of Digital Communication (physical layer) in Python.

Tensorflow Implementation and result of Auto-encoder Based Communication System From Research Paper : "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/

Source code for paper "MIMO Channel Estimation using Score-Based Generative Models", published in IEEE Transactions on Wireless Communications.

This repository contains Physical layer utilities based on 3GPP specs for NR 5G

This repository will contains my learnings and quiz answers for Bits and Bytes of Networking course.

Implementation of the PCIe physical layer

5G Tookit provides a rich set of 3GPP standards compliant modules and libraries. These modules can be used for reseach and development on physical channels and procedures in uplink and downlink communication.

Covert data exfiltration and detection using 802.11 beacon stuffing

FPGA implementation of the eNodeB and gNB physical layers

Este trabalho foca na simulação do funcionamento das camadas de enlace e física do modelo OSI.

北京邮电大学 2023-2024 春季学期《计算机网络》课程作业集合

Implementation of the performance evaluation of "On the Impact of Control Signaling in RIS-Empowered Wireless Communications"

Python module for 5G NR sync signals and decoding.

Simulations, Paper Submission Drafts and Patent Applications for Research on Hierarchical Modulations

Train and evaluate autoencoders able to transmit data over a wireless channel, using TensorFlow and Keras frameworks.

Physical and Data Link Layer funtionalities of the TCP/IP Model. Manchester Encoding, Cyclic Redundancy Check (CRC), Frame by Frame communication and plotting graphs by PyLab.

"This repository focuses on implementing data link layer error detection codes. Providarious error detection techniques used in data communices methods for vations at the data link layer."

Python 5G toolbox provide 3GPP 5G NR physical layer high-phy and low-phy libraries. It has passed 60K+ testcases which were generated from Matlab 5G toolbox.

Official implementation of the neural decoder based on mutual information maximization