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Wireless Networks Division Tools

Simulation Tools for Wireless Communications

Device-to-Device and Public Safety Communication Models for ns-3

The psc-ns3 repository contains a suite of new simulation models for wireless communication including, but not limited to, public safety communications. It provides extensions to the LTE model to support the evaluation of D2D communications based on 3GPP’s Proximity Services (ProSe) architecture, Mission Critical Push-to-Talk (MCPTT), HTTP application, and video application models. On-going work includes extensions to 5G New Radio (NR) to support the evaluation of V2X and ProSe services over NR Sidelink.

NetSimulyzer – a 3D Visualizer for ns-3

The NetSimulyzer is a new visualization tool supporting ns-3, an open-source packet-level network simulator widely used by researchers around the world and inside WND for several projects, including Public Safety communication, 5G, and Machine Learning.  NetSimulyzer includes 3D visualization of the topology, node mobility, and a flexible statistics framework. One of the principal design goals was to keep the visualizer technology agnostic, thus allowing it to be used for various applications beyond those currently targeted by the NIST’s research areas.

QPP Sim

QPP Sim is a discrete event simulator aimed at helping and accelerating the study of Quality of Service (QoS), Priority, and Pre-emption (QPP) mechanisms and policies in LTE. The complexities of the LTE architecture have been abstracted in order to facilitate the rapid prototyping of schedulers, priority policies, and monitoring mechanisms, and enable comparative analyses of different approaches, solutions, and proposals.

Quasi-Deterministic Framework

The NIST Quasi-deterministic framework is a suite of tools designed to evaluate end-to-end performance of millimeter Wave (mmWave) systems (from channel to application layer). The software is made of five software packages: 
-    The NIST Q-D Realization software: generate mmWave channel realizations using the Q-D methodology
-    The NIST IEE 802.11ay PHY: the link-level simulator for mmWave
-    The Codebook Generator: Realistic Phased antenna array model
-    ns-3 802.11ad/ay with Q-D support: System-level simulation for mmWave WiFi Protocols
-    The NIST Q-D Interpreter:  3D visualization of mmWave scenarios

Machine Learning in Modeling and Simulation

Machine Learning solutions and approaches are gaining in popularity to solve complex networking problems for which closed-form equations and models are non-existent. For that, large datasets are typically needed for training and operation.  However, often times datasets are not available  (e.g., the technologies are too new to have field data collected, or the data is very specific to a situation or location, etc.). To overcome this limitation, we present a data-centric integration of Machine Learning tools and network simulators, where network simulators are used to generate synthetic datasets for training Machine Learning solutions, and Machine Learning tools are seamlessly integrated into network simulators to facilitate the evaluation and comparison of said solutions.

Development and testing tools for radar detection in the CBRS band

The Citizens Broadband Radio Service (CBRS) in the U.S. permits commercial broadband access to the radio frequency spectrum between 3550 MHz and 3700 MHz on a shared basis with incumbents in the band. Among the incumbents is the U.S. military which operates radar systems in this band, including shipborne radar off the U.S. coasts. NIST has developed  the following software tools that can be used for testing and development of radar detection algorithms in the CBRS band. 

Simulated Radar Waveform generator

A software tool for generation of simulated radar waveform datasets. The datasets can be used to develop and test detection algorithms for the 3.5 GHz CBRS or similar bands where the primary users of the band are federal incumbent radar systems. The software tool generates radar waveforms and randomizes the radar waveform parameters, which include pulse modulation, pulse duration, pulse repetition rate, chirp width and pulses per burst. A reference RF dataset was generated using this software and is available here: https://doi.org/10.18434/M32116.

Baseline Deep Learning Detectors for Radar Detection in the 3.5 GHz CBRS Band

A comprehensive framework for designing deep learning (DL) detectors and evaluating their detection performance using both simulated and experimental test data. The proposed tools and techniques are developed in the context of dynamic spectrum use for the 3.5 GHz CBRS band, but they can be utilized and expanded for standardization of machine learned spectrum awareness technologies and methods.

Tools for ESC sensor development and evaluation.

These tools are discussed in the paper “3.5 GHz ESC Sensor Test Apparatus Using Field-Measured Waveforms”. The proposed apparatus utilizes waveforms captured in the field to reproduce what the sensor would experience in the field in a controlled laboratory environment, and with repeatability unattainable in live field testing. Two software tools are provided for the implementation of this approach and for automated testing of large numbers of test waveforms:

  • RF Sensor Test Harness
    A LabVIEW-based software for automating the process of playing the waveforms to a sensor under test.
  • ESC Waveform Generator
    A MATLAB-based software used in combination with RF Sensor Test Harness for automating the generation of the test waveforms.

Reference Data sets

NextG Channel Model Alliance

The NextG Channel Model Alliance (formerly operated as the 5G Millimeter-Wave (mmWave) Channel Model Alliance) is a NIST-sponsored international research consortium working to advance breakthrough measurement, calibration, and channel modeling approaches and technologies used for mmWave and submillimeter-wave frequencies. The Alliance is open to the public and serves to facilitate improved data and knowledge sharing amongst leading communications engineers interested in producing more accurate and predictive channel models and measurements required to support the commercialization of next-generation wireless networks (5G and beyond).
 

Contacts

Division Chief

Created May 14, 2021, Updated August 12, 2021