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ROHIT KUMAR BONDUGULA's Projects

5g-planning-using-a-ga icon 5g-planning-using-a-ga

An implementation of a genetic algorithm to solve the planning problem present in 5G cellular networks

5gc_apis icon 5gc_apis

RESTful APIs of main Network Functions in the 3GPP 5G Core Network

awesome-public-datasets icon awesome-public-datasets

A topic-centric list of high-quality open datasets in public domains. Propose NEW data ☛☛☛PR☛☛☛

convolutional-networks-for-stock-predicting icon convolutional-networks-for-stock-predicting

This is a Machine Learning project using Convolutional Neural Network to predict the stock price. It is done after a project article by Ashwin Siripurapu from Stanford University but in a different way.

deeplearn icon deeplearn

Implementation of research papers on Deep Learning+ NLP+ CV in Python using Keras, Tensorflow and Scikit Learn.

deeplearning-coordinatedbeamforming icon deeplearning-coordinatedbeamforming

Simulation code for "Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems" by Ahmed Alkhateeb, Sam Alex, Paul Varkey, Ying Li, Qi Qu, and Djordje Tujkovic, in IEEE Access, vol. 6, pp. 37328-37348, 2018.

fast-wavenet icon fast-wavenet

Speedy Wavenet generation using dynamic programming :zap:

har-cnn-keras-stm32 icon har-cnn-keras-stm32

Human Activity Recognition Using Convolutional Neural Network in Keras for SensorTile (STMicroelectronics)

new_radio_5g_utils icon new_radio_5g_utils

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

ns3-mmwave icon ns3-mmwave

ns-3 module for simulating mmWave-based cellular systems. See https://ieeexplore.ieee.org/document/8344116/ (open access) as a reference.

ntr_project_aro_into_rmc_5g icon ntr_project_aro_into_rmc_5g

Simulation of Round Robin and MaxSNR network ressource allocation algorithms to demonstrate the power of opportunistic allocation

rssd icon rssd

Rohde & Schwarz SCPI Driver (in Python)

stock-prediction-comparison icon stock-prediction-comparison

Test performance and reliability of machine learning models from ARIMA, Trees and Deep Learning for Stock Prediction

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