Discover @farismismar Open Source projects

@farismismar

farismismar

Stars
296
Global Rank 90,006 (Top 4 %)
Followers 194
Registered about 8 years ago
Most used languages

Python
90.9 %

MATLAB
9.1 %

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.

Q-Learning-Power-Control

Code for the following publication: F. B. Mismar, J. Choi, and B. L. Evans, "A Framework for Automated Cellular Network Tuning with Reinforcement Learning", IEEE Transactions on Communications, vol. 67, no. 10, Oct. 2019, pp. 7152-7167, DOI 10.1109/TCOMM.2019.2926715.

Stock-Prediction

Implementation of stock technical indicators and deep LSTM for closing price 30-day lookahead predictions (for learning purposes only).

Deep-Q-Learning-SON-Perf-Improvement

Code for the following publication: F. B. Mismar, J. Choi, and B. L. Evans, "A Framework for Automated Cellular Network Tuning with Reinforcement Learning", IEEE Transactions on Communications, vol. 67, no. 10, Oct. 2019, pp. 7152-7167, DOI 10.1109/TCOMM.2019.2926715.

DL-CoMP-Machine-Learning

Code for my publication: F. B. Mismar and B. L. Evans, "Deep Learning in Downlink Coordinated Multipoint in New Radio Heterogeneous Networks", IEEE Wireless Communication Letters, vol. 8, no. 4, Aug. 2019, pp. 1040-1043, DOI 10.1109/LWC.2019.2904686.

Handover-Improvement-ML

Code for my publication: Partially Blind Handovers for mmWave New Radio Aided by Sub-6 GHz LTE Signaling. Paper accepted to IEEE International Conference of Communications Workshops, 2018.

Bandswitch-DeepMIMO

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

eesc7v86-fall22

ml-and-ai-in-arabic

هنا سأقوم بوضع السلايدات وبعض البرامج حسب سلسلة المحاضرات

Laylat-al-Qadr

World-Cup-2018-Prediction

This project is for fun. Without proper data to solve the problem, ML does not do much.

EE380L

Code repo for EE 380L Data Mining Spring 2017