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

Pythonic mmWave Toolbox for TI's IWR Radar Sensors

TI mmWave radar ROS driver (with sensor fusion and hybrid)

Python program to read and plot the data in real time from the AWR1642 and IWR1642 mmWave radar boards (Texas Instruments).

Python program to read and plot the data in real time from the AWR1843 mmWave radar board (MMWAVE SDK 3)

🎯 ML-based positioning method from mmWave transmissions - with high accuracy and energy efficiency

[mmWave based fmcw radar design files] based on AWR1843 chip operating at 76-GHz to 81-GHz.

High-fidelity implementation of the IEEE 802.11ad/ay standards in network simulator ns-3

object tracking based on millimeter wave radar

Basic Gesture Recognition Using mmWave Sensor - TI AWR1642

A simple example with how hybrid beamforming is employed at the transmit end of a massive MIMO communications system.

Optimization algorithms for hybrid precoding in mmWave MIMO systems: Version 1.1.0

High resolution point clouds from mmWave radar

Integrated Sensing and Communication Physical layer (PHY) model of IEEE 802.11ay/bf.

Python program to read and plot the position of the reflected points from the IWR1443 and the AWR1443. It works both with Windows and Raspberry Pi.

The project represents the main code for the proposed cross-layer Dynamic sub-array scheduling for 5G applications, in collaboration with Mathworks inc.

ESPHome LD2450 mmWave custom external component

Matlab Simulation for T. K. Vu, M. Bennis, S. Samarakoon, M. Debbah and M. Latva-aho, "Joint In-Band Backhauling and Interference Mitigation in 5G Heterogeneous Networks," European Wireless 2016; 22th European Wireless Conference, Oulu, Finland, 2016, pp. 1-6. URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7499273&isnumber=7499250

This is a Matlab code package is related to the article : Path Selection and Rate Allocation for URLLC in Self-backhauled mmWave 5G Networks

RF Genesis: Zero-Shot Generalization of mmWave Sensing through Simulation-Based Data Synthesis and Generative Diffusion Models (SenSys'23)