🔬 Research Framework for Single and Multi-Players 🎰 Multi-Arms Bandits (MAB) Algorithms, implementing all the state-of-the-art algorithms for single-player (UCB, KL-UCB, Thompson...) and multi-player (MusicalChair, MEGA, rhoRand, MCTop/RandTopM etc).. Available on PyPI: https://pypi.org/project/SMPyBandits/ and documentation on

Radio Frequency Machine Learning with PyTorch

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/

Using signal processing based features to train and validate machine-learning algorithms to improve spectrum sensing and related problems in cognitive radios.

Spectrum sensing in cognitive radios leveraging machine learning models

In this repository, we deal with developing an energy detector and a detector based on cyclostationarity for an OFDM based cognitive radio system and implementing and evaluating the performance of these detectors.

Simulation code for "Achievable Rate Maximization for Underlay Spectrum Sharing MIMO System with Intelligent Reflecting Surface," by V. Kumar, M. F. Flanagan, R. Zhang, and L. -N. Tran, IEEE Wireless Communications Letters, 2022, doi: 10.1109/LWC.2022.3180988.

This is the Matlab code for the paper "Denoising Higher-Order Moments for Blind Digital Modulation Identification in Multiple-Antenna Systems" published in the IEEE Wireless Communications Letters.

A repository intended to gather and develop stuff related to Software-Defined Radio (SDR) HW & SW platforms, testbeds, Open Source and Standards working groups, Cognitive Radio (CR), SW Defined Wireless Networks (SDWN), Cognitive Spectrum Access (CSA), 3G, 4G, LTE, 5G, Space Communications, Alternatives to commercial implementations, etc

Simulation code for the paper "Genetic Algorithm Aided Transmit Power Control in Cognitive Radio Networks"

Cognitive Radio Framework in Python

Malware Detection for Cognitive Radio Networks

Code and resources for the paper: "Cognitive Radio Spectrum Sensing on the Edge: A Quantization-Aware Deep Learning Approach"

Simulation code for the paper "Energy-efficient outage-constrained power allocation based on statistical channel knowledge for dual-hop cognitive relay networks"

gnuRadio basic scripts running on windows

Advised by Prof. Jungmin So - spring '23

TinyML-aware spectrogram segmentation using pruning and knowledge distillation for RF spectrum sensing.

Using CNNs to correct distortion in modulation recognition