mmwave

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)

Pythonic mmWave Toolbox for TI's IWR Radar Sensors

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

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

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

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

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

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

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

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

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.

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

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

Basic Gesture Recognition Using mmWave Sensor - TI AWR1642

IAB feature for the ns-3 mmWave module

A Matlab reading program for TI AWR1642

A sensor fusion approach to the recognition of microgestures.

Using sub-6 GHz channels to predict mmWave beams and link blockage.

object tracking based on millimeter wave radar

Gait Based User Recognition from mmWave Radar Data

Camera based beam prediction

Submitted paperwork

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.

A repo containing the mmWave SDK for TI's IWR16x (IWR1642) radar offerings

Learning Environment-aware and hardware-compatible beam-forming codebooks

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.

Simulation files for Topics in Wireless Communications.

MATLAB application for ns-3 IEEE 802.11ad/ay codebook/beambook generation