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Automatic Configuration of Energy Harvesting Sensor Nodes with Reinforcement Learning

advanced-machine-learning-specialization

Advanced Machine Learning Specialization on Coursera

alternating-minimization-algorithms-for-hybrid-precoding-in-millimeter-wave-mimo-systems

Simulation codes for "Alternating minimization algorithms for hybrid precoding in millimeter wave MIMO systems," by Xianghao Yu, Juei-Chin Shen, Jun Zhang, and Khaled B. Letaief, IEEE J. Sel. Topics Signal Process., to appear, 2016.

awesome-deep-learning

A curated list of awesome Deep Learning tutorials, projects and communities.

bandswitch-deepmimo

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

classicpowerallocation

This is a classic power allocation in a sparse femtocell network based on dual lagrange problem.

cognitive-backscatter

Figure 4 in paper ``Cognitive Backscatter Network: A Spectrum Sharing Paradigm for Passive IoT''

convex-optimization

A convex optimization course https://see.stanford.edu/Course/EE364A completed homework assignments

convex-optimization-1

projects of convex optimization course,written in MATLAB

coursera

Introduction to Data Science in Python

coursera-machine-learning-foundations-a-case-study-approach

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

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.

covid-19

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

d2dsimulator

D2D communications system simulator on MATLAB.

ddlo

Distributed Deep Learning-based Offloading for Mobile Edge Computing Networks

deep-learning-coursera

Deep Learning Specialization by Andrew Ng on Coursera.

deep-learning-coursera-complete

Deep Learning Specialization by Andrew Ng on Coursera - My Completed Coursework Repo - All 5 Courses

deep-learning-power-allocation-in-massive-mimo

This is the code package related to the follow scientific article: Luca Sanguinetti, Alessio Zappone, Merouane Debbah 'Deep-Learning-Power-Allocation-in-Massive-MIMO' presented at the Asilomar Conference on Signals, Systems, and Computers, 2018. http://www.asilomarsscconf.org

deep-learning-vs.-conventional-classifying-in-predicting-indoor-location-using-wifi-fingerprinting

deep-q-learning-based-downlink-power-allocation

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

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.

deeplearning.ai

Materials from deeplearning.ai course on Coursera

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.

droo

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

edge-computing-codes

Implementation of Edge Computing papers.

energy-harvesting-ddpg-

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

energy_consumption_in_mu_mimo_with_mobility

This code computes the energy consumption in the downlink of a single-cell multi-user MIMO system in which the base station (BS) makes use of N antennas to communicate with K single-antenna user equipments (UEs). The UEs move around in the cell according to a random walk mobility model.

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 )