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Wireless Networks Division

The Wireless Networks Division works with the networking industry to research, develop, promote, measure, and deploy emerging networking technologies and standards that revolutionize how networks are operated and used.





NIST CTL’s Wireless Networks Division works with industry to develop, deploy, and promote emerging technologies and standards that will dramatically improve the operation and use of wireless networks. Our team, based at NIST’s main campus in Gaithersburg, Md., specializes in communications networks and protocols as well as in the digital communications technologies that make those networks possible. In both of these areas, we perform both theoretical and empirical research to develop simulation models, experimental testbeds, and proof-of-concept prototypes that we use to evaluate new technologies and to refine existing standard specifications for wireless networks and systems. In addition, we define metrics and measurement methods to assess the performance of many types of wireless systems. As part of our program planning activities, we continue our industry and stakeholders outreach in order to identify additional measurements and metrology R&D gaps in support of the development of wireless communication systems and standards. In 2018 we published the Future Generation Wireless Research and Development Gaps Report.



Drone Networks

Our work impacts all of CTL’s core programs:  Public Safety Communications, Fundamental Metrology for CommunicationsTrusted Spectrum Testing, and 5G & Beyond. We also co-lead the  5G mmWave Channel Model Alliance, a nexus for global efforts to develop the future radio channels over which next-generation 5G wireless networks will operate at data rates up to a thousand times greater than what is possible today. Our diverse portfolio of work is possible thanks to an extensive collaboration network in the form of key partnerships within NIST and across industry, government, and academia. 


CTL’s Wireless Networks Division specializes in two areas of wireless technology research and analysis:
   • Communications networks and protocols, which involves data transport, routing, resource management and medium access control.   
   • Digital communications, which looks at the essential technologies enabling communications networks, including signal processing, modulation, error control coding and channel modeling. Across both of these competence areas, we bring our capabilities in performance measurements, model development, experimental testbeds and network prototyping to bear.


The goal of our performance-measurement work is to provide insights into key factors that affect the performance, reliability and resiliency of advanced communication networks. We define common performance metrics and modeling approaches and use commercially available and in-house customized network modeling and simulation tools as well as experimental testbeds to compare different network scenarios and deployments. Among the measurement we take:
   • Throughput, delay and loss associated with network protocols.
   • Delay, loss, collisions and retransmissions associated with medium access control.
   • Signal-to-noise ratio and block error rate associated with digital transceivers and links.
   • Detection reliability and delay associated with spectrum sensing and monitoring systems.
   • Fading, shadowing and noise associated with radio-frequency channels.
   • Distance and link margin needed for a given link budget.


Accurately characterizing the environments in which future wireless hardware and protocols will operate is a vital precursor to wireless network modeling and protocol development. Such modeling ultimately helps industry identify potential cost-savings and sets realistic expectations for network coverage, capacity, scalability and performance.

Our channel modeling work employs existing and custom models applying mathematical analysis and computer simulation to factors affecting radio-frequency propagation such as terrain, clutter, building morphologies, antenna height and center frequencies. We work with CTL’s RF Technology Division in developing and enhancing a millimeter-wave channel sounder capable of completely characterizing channels operating at the high frequencies expected to be used in 5G & Beyond systems. Our channel modeling experts then use these measurements as inputs for their models.


While much of our work involves mathematical modeling and computer simulation, our experimental testbeds help us validate our models, develop benchmarks and take physical measurements of wireless systems and their key components. We start with commercial broadband devices (e.g., LTE base station and user equipment), protocol analyzers and emulators, which provide a highly controlled, non-radiating environment to characterize how high-speed wireless devices transmitting over multiple channels in different environments might interact. But when our requirements push past the boundaries of what’s commercially available, we develop our own solutions, such as our real-time spectrum monitoring system using software-defined radios.






The Wireless Network Division participates in NIST’s Summer Undergraduate Research Fellowship (SURF) program (, which provides opportunities for undergraduate students to work side-by-side with NIST researchers. If you love science and engineering but have never been in a lab before, please apply; this program was designed for you. The application with detailed instructions is at The application will be open until February 9, 2020, or until NIST receives a total of 1000 applicants, so don’t delay! The projects that we have available this summer are as follows:

Project 673-1

Title:  A Database of Cellular Waveforms for Training Intelligent Signal Detectors

This project will create a library of digital waveforms that can be used to train and evaluate radio frequency (RF) signal detectors.  The student will use a vector signal analyzer to digitally sample and record RF signals generated by commercial mobile broadband equipment such as base stations and mobile devices.  The captured signals will be incorporated into a public database of RF waveforms, and the database will be used by machine learning algorithms to train signal detectors that use artificial intelligence.  Such detectors will support autonomous spectrum sharing systems that will enable future high-speed wireless networks.

Preferred major/skills:  Electrical engineering, laboratory experience with measurement instruments (e.g., spectrum/signal analyzer, oscilloscope)

Project 673-2

Title: GUI Development for Network Simulator

The Wireless Networks Division (WND) of the Communication Technologies Laboratory (CTL) is working to help network designers and researchers to visualize the output of their network simulations, such as network topology and other data like packet loss rates. We are specifically interested in evaluating next-generation wireless networks for public safety communications.  The student doing this project will help to develop new capabilities for a Qt-based Graphical User Interface (GUI) for the network simulator ns-3, including the development of 3D models, animations, and other visual enhancements.

Project 673-3

Title: Software-Defined Radio Testbed for Next-Generation Wireless Public Safety Communications

The Wireless Networks Division (WND) of the Communication Technologies Laboratory (CTL) is developing a testbed platform using Software Defined Radios (SDRs) to support the evaluation of next generation wireless networks and communication protocols for public safety. The objective of the project is to enhance and expand the testbed platform. The student will help to configure the testbed equipment and calibrate different SDR models, use the testbed to emulate wireless communication protocols, and enhance software interface features. 

Helpful skills: Knowledge of C/C++ programming language and MATLAB, experience in Signal Processing.

Project 673-4
Title: Opportunistic Dynamic Spectrum Access using Machine Learning


In opportunistic Dynamic Spectrum Access (DSA), a Secondary User (SU) accesses spectrum when it is not being used by the Primary User (PU).  However, once the SU starts transmitting in an idle period, it does not know when PU may start using the channel again, and the SU is required to access the spectrum so that the probability of interference to the PU remains below a given threshold.  Hence, the performance of a DSA algorithm depends on how accurately the SU can predict when the PU may reappear, so that the SU can complete its transmission and vacate the channel before the PU returns. At the same time, it is not desirable for the SU to vacate the channel too early because this will unnecessarily reduce the SU's throughput.  In this project, we will use suitable machine learning method(s) to train a neural network using historical spectrum idle/busy data to predict when the PU may reappear on the channel. The trained neural network then can be used to decide whether to grant or deny a SU's request to transmit.

Preferred Major: Electrical Engineering/Computer Science/closely related major

Courses Needed: Artificial Intelligence/Machine Learning and/or Wireless Networking

Computer Skills: Python, tensorflow/pytorch/keras


3.5 GHz Environmental Sensing Capability Detection
Thresholds and Deployment Modeling a Nationwide Public Safety Broadband Network
Radio Channel Sounders for Modeling Mobile Communications at 28 GHz, 60 GHz and 83 GHz



Public Safety Communications
Citizens Broadband Radio Service
Channel Modeling
Radio-resource Allocation and Beamforming Algorithms



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