QuaDRiGa, short for QUAsi Deterministic RadIo channel GenerAtor, is used for generating realistic radio channel impulse responses for system-level simulations of mobile radio networks.

BRAT Lab files for generating CSI datasets with QuaDRiGa channel model

Modern wireless cellular networks use massive multiple-input multiple-output (MIMO) technology. This technology involves operations with an antenna array at a base station that simultaneously serves multiple mobile devices which also use multiple antennas on their side. For this, various precoding and detection techniques are used, allowing each user to receive the signal intended for him from the base station. There is an important class of linear precoding called Regularized Zero-Forcing (RZF). In this work, we propose Adaptive RZF (ARZF) with a special kind of regularization matrix with different coefficients for each layer of multi-antenna users. These regularization coefficients are defined by explicit formulas based on SVD decompositions of user channel matrices. We study the optimization problem, which is solved by the proposed algorithm, with the connection to other possible problem statements. We also compare the proposed algorithm with state-of-the-art linear precoding algorithms on simulations with the Quadriga channel model. The proposed approach provides a significant increase in quality with the same computation time as in the reference methods.

Modern 5G wireless cellular networks use massive multiple-input multiple-output (MIMO) technology. This concept entails using an antenna array at a base station to concurrently service many mobile devices that have several antennas on their side. In this field, a significant role is played by the precoding (beamforming) problem. During downlink, an important part of precoding is the power allocation problem that distributes power between transmitted symbols. In this paper, we consider the power allocation problem for a class of precodings that asymptotically work as regularized zero-forcing. Under some realistic assumptions, we simplify the sum spectral efficiency functional and obtain tractable expressions for it. We prove that equal power allocation provides optimum for the simplified functional with total power constraint (TPC). Also, low-complexity algorithms that improve equal power allocation in the case of per-antenna power constraints (PAPC) are proposed. On simulations using Quadriga, the proposed algorithms show a significant gain in sum spectral efficiency while using a similar computing time as the reference solutions.

Code repository for working on Quadriga simulations

✨ Ruby wrapper for the QuadrigaCX API

my notebook abount the channel generation using the QuaDriGa_v2.6.1 tool

C++/MEX/Python Utility library for radio channel modelling and simulations

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This repository provides MATLAB code to generate large-scale, standardized 3D spatio-temporal-frequency channel state information (CSI) datasets based on the 3GPP/QuaDRiGa channel model. The datasets are suitable for machine learning, deep learning, and wireless communication research, supporting a wide range of MISO-OFDM configurations.

Vorlage für die Entwicklung von QUADRIGA OERs mit Jupyter Book

A project in a Quadriga sub-urban environment, with parametrized values for the number of interferences and moving objects that communicate through an OFDM (with QAM modulation) with a Base Station.

Golang wrapper for the QuadrigaCX API

nodejs interface for QuadrigaCX bitcoin exchange

Quadriga based MIMO channel generator

We provide a detailed description of a dataset generated using a QuaDRiGa-based system-level simulation platform, supporting AI-driven mobility management research for high-speed railway communications.

QUADRIGA OER: Fallstudie 1 - Text: Quantitative Analyse der Medienwellen der Spanischen Grippe (1918/19)

A C# / Dot Net wrapper for the QuadrigaCX bitcoin exchange API

A data generation pipeline and synthetic dataset for 5G channel modeling, utilizing QuaDRiGa and 3GPP TR 38.901.

Python Wrapper for QuadrigaCX API

This project provides an efficient way to export chats from WhatsApp in a convenient and readable form. This makes it easier to check out your chat history.

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Sunsetted client project. Bitcoin Exchange.

Command line tool to fetch BTC, ETH, and LTC price

Quadriga API exporter to Bitcoin.tax csv format

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Python Client for QuadrigaCX

QUADRIGA OER: Fallstudie 1 - Text: Quantitative Analyse der Medienwellen der Spanischen Grippe (1918/19)

QuaDRiGa

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