There are 8 repositories under mmwave topic.
ns-3 module for simulating mmWave-based cellular systems. See https://ieeexplore.ieee.org/document/8344116/ (open access) as a reference.
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
Basic Gesture Recognition Using mmWave Sensor - TI AWR1642
object tracking based on millimeter wave radar
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
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.
Integrated Sensing and Communication Physical layer (PHY) model of IEEE 802.11ay/bf.
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
Gait Based User Recognition from mmWave Radar Data
A sensor fusion approach to the recognition of microgestures.
Using sub-6 GHz channels to predict mmWave beams and link blockage.
ESPHome integration for mmWave Sensors from Seeed Studio
Cardiovascular Activity Monitoring Using mmWaves
M. Polese, F. Restuccia, and T. Melodia, "DeepBeam: Deep Waveform Learning for Coordination-Free Beam Management in mmWave Networks", Proc. of ACM Intl. Symp. on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (ACM MobiHoc), July 2021.
IAB feature for the ns-3 mmWave module
Artifacts for Sigcomm'21 paper "Two beams are better than one: Enabling reliable and high throughput mmWave links."
Code for M. Polese, J. Jornet, T. Melodia, M. Zorzi, “Toward End-to-End, Full-Stack 6G Terahertz Networks”, https://arxiv.org/abs/2005.07989, 2020.
RF Genesis: Zero-Shot Generalization of mmWave Sensing through Simulation-Based Data Synthesis and Generative Diffusion Models (SenSys'23)
Submitted paperwork