ghizall4

ACES

Automatic Configuration of Energy Harvesting Sensor Nodes with Reinforcement Learning

covarianceEstimation-massiveMIMO

This is the code that generates the figures in the paper titled "Covariance Matrix Estimation for Massive MIMO" authored by Karthik Upadhya and Sergiy A. Vorobyov and published in IEEE Signal Processing Letters, vol. 25, no. 4, pp. 546-550, April 2018.

Deep-Q-Learning-SON-Perf-Improvement

Code for my publication: Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement. Paper accepted to 52nd Asilomar Conference on Signals, Systems, and Computers, 2018.

Deep-Reinforcement-Learning-for-5G-Networks

Code for my publication: Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination. Paper accepted for publication to IEEE Transactions on Communications.

deeprm

Resource Management with Deep Reinforcement Learning (HotNets '16)

DL-CoMP-Machine-Learning

Code for my publication: Deep Learning in Downlink Coordinated Multipoint in New Radio Heterogeneous Networks. Paper is accepted to IEEE Wireless Communications Letters, 2019.

Edge-Computing-Codes

Implementation of Edge Computing papers.

EnergySharing-EHSN-RL

This contains code for work described in "Energy Sharing for Multiple Sensor Nodes With Finite Buffers", which is published in IEEE Transactions on Communications ( Volume: 63 , Issue: 5 , May 2015 )

HybridPrecodingMassiveMIMO

This project contains MATLAB codes for the paper: L. Liang, W. Xu, and X. Dong, "Low-complexity hybrid precoding in massive mulituser MIMO systems," IEEE Wireless Communications Letters, vol. 3, no. 6, pp. 653-656, Dec. 2014.

ResourceAllocationV2Xgraph

This project contains MATLAB codes for the paper: L. Liang, S. Xie, G. Y. Li, Z. Ding, and X. Yu, “Graph-based resource sharing in vehicular communication,” IEEE Transactions on Wireless Communications, vol. 17, no. 7, pp. 4579–4592, Jul. 2018.

ghizall4/Deep-Q-Learning-SON-Perf-Improvement

Code for my publication: Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement. Paper accepted to 52nd Asilomar Conference on Signals, Systems, and Computers, 2018.

ghizall4/ACES

Automatic Configuration of Energy Harvesting Sensor Nodes with Reinforcement Learning

ghizall4/Deep-Reinforcement-Learning-for-5G-Networks

Code for my publication: Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination. Paper accepted for publication to IEEE Transactions on Communications.

ghizall4/DL-CoMP-Machine-Learning

Code for my publication: Deep Learning in Downlink Coordinated Multipoint in New Radio Heterogeneous Networks. Paper is accepted to IEEE Wireless Communications Letters, 2019.

ghizall4/Edge-Computing-Codes

Implementation of Edge Computing papers.

ghizall4/STCNet

Source code of IEEE JSAC

ghizall4/Advanced-Machine-Learning-Specialization

Advanced Machine Learning Specialization on Coursera

ghizall4/Bandswitch-DeepMIMO

Code for my publication: Deep Learning Predictive Band Switching in Wireless Networks. Paper to appear in IEEE Transactions in Wireless Communications.

ghizall4/Coursera-Machine-Learning-Foundations-A-Case-Study-Approach

Course material and assignment repository for the "Machine Learning Foundations: A Case Study Approach" on Coursera.

ghizall4/COVID-19

Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE

ghizall4/Deep-Learning-vs.-Conventional-Classifying-in-Predicting-Indoor-Location-Using-WiFi-Fingerprinting

ghizall4/Deep-Q-Learning-based-Downlink-Power-Allocation

A deep reinforcement learning based approach is used to allocate downlink power for multi-cell wireless system.

ghizall4/Deeplearning.ai

Materials from deeplearning.ai course on Coursera

ghizall4/DROO

Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks

ghizall4/Energy-Harvesting-DDPG-

Codes for "Deep Deterministic Policy Gradient (DDPG) based Energy Harvesting Wireless Communications"

ghizall4/Hands-On-Reinforcement-Learning-with-Python

Hands-On Reinforcement Learning with Python, published by Packt

ghizall4/IRS-relaying

Simulation code for “ Intelligent Reflecting Surface vs. Decode-and-Forward: How Large Surfaces Are Needed to Beat Relaying?,” by Emil Björnson, Özgecan Özdogan, Erik G. Larsson, IEEE Wireless Communications Letters, To appear.

ghizall4/IRSTestbedUofG

Code repository for the University of Glasgow's intelligent reflecting surface hardware testbed

ghizall4/LassoADMM

Source code for our paper "A Distributed ADMM Approach for Collaborative Regression Learning in Edge Computing"

ghizall4/M.S.-Thesis

M.S. Thesis: "Channel Allocation and Power Control for Device-to-Device Communications Underlaying Cellular Cellular Networks Incorporated With Deep Learning Assistance"

ghizall4/mec_drl

Deep reinforcement learning for mobile edge computing

ghizall4/OpenRIS

OpenRIS is an open-source reconfigurable intelligent surface (RIS) project

ghizall4/Paper-with-Code-of-Wireless-communication-Based-on-DL

无线与深度学习结合的论文代码整理/Paper-with-Code-of-Wireless-communication-Based-on-DL

ghizall4/Reinforcement-Learning-Specialization

[Coursera] Reinforcement Learning Specialization by "University of Alberta" & "Alberta Machine Intelligence Institute"

ghizall4/RIS_Codes_Collection

A collection contains the codes for IRS(RIS) papers.

ghizall4/RL-for-binary-computation-offloading-in-wireless-powered-MEC-networks

It's a implementation about the paper Liang Huang, Suzhi Bi, and Ying-jun Angela Zhang, "Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks", on https://ieeexplore.ieee.org/document/8771176

ghizall4/Robust-Design-of-AC-Computing-enabled-Receiver-Architecture-for-SWIPT-Networks

ghizall4/slicesim

5G Network Slicing Simulation

ghizall4/STDenseNet

Source code of Citywide Cellular Traffic Prediction Based on Densely Connected Convolutional Neural Networks

ghizall4/Supervised-Deep-Learning-for-Radio-Resource-Allocation

A supervised deep learning based resource allocation scheme for multi-cell wireless system.