| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | August 13, 2020 |
| Latest Amendment Date: | June 3, 2024 |
| Award Number: | 2016379 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Deepankar Medhi
dmedhi@nsf.gov (703)292-2935 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | October 1, 2020 |
| End Date: | September 30, 2024 (Estimated) |
| Total Intended Award Amount: | $962,657.00 |
| Total Awarded Amount to Date: | $1,037,657.00 |
| Funds Obligated to Date: |
FY 2023 = $75,000.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
4333 BROOKLYN AVE NE SEATTLE WA US 98195-1016 (206)543-4043 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
4333 Brooklyn Ave. NE Seattle WA US 98195-2500 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
Information Technology Researc, CCRI-CISE Cmnty Rsrch Infrstrc |
| Primary Program Source: |
01002021DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
Simulation of emerging next-generation wireless networks continues to be an invaluable component of the design, evaluation and innovation cycle as a complement to other modes such as testbeds of performance testing. This project will focus on upgrading ns-3, the most widely used open source network simulator, to meet the challenges of efficient yet accurate simulation based performance evaluation of 5G and beyond networks. It will do this via model building for the evolutions in Wi-Fi (notably Wi-Fi 6) and cellular (notably 5G NR) technologies and incorporating new simulation techniques targeting dense and heterogeneous network use cases.
Network simulation faces fundamental challenges due to inherent increases in complexity, notably at the physical layer due to increasing bandwidth, MIMO (multiple-input, multiple-output) and multi-user operation as well as cross-layer (physical and multiple access) operation, necessary to deal with network scale and heterogeneity. The primary objective is to develop simulation methods that achieve the desired balance between maintaining simulation run-time efficiency while preserving accuracy of measured network parameters (loss, throughput, latency) - in the face of increasing complexity. The research plan will explore a variety of techniques including efficient link-to-system mappings, pruning of network state representations that do not impact simulation accuracy, and parallelization approaches based on optimistic simulation.
In addition, the project plans to improve ns-3 usability and further adoption through increased community outreach and creation of new educational material to lower barriers to entry for a new generation of ns-3 users. The recreated ns-3 Consortium hosted by the University of Washington will foster creation of training material for both novice and advanced users, to be archived and distributed online as well as in-person at leading networking/simulation conferences.
New content (educational materials and tutorials, research reports and publications) will be disseminated primarily via the ns-3 website https://www.nsnam.org/ and the two institutional lab home pages (the University of Washington - https://depts.washington.edu/funlab and Georgia Tech - http://blough.ece.gatech.edu/research/) along with other popular channels such as YouTube/Viemo for video. Additionally, code under development will be preserved in archives such as Gitlab and announced via the ns-3 Users and Developers mailing lists.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The major goal of this project was to extend the popular open-source ns-3 network simulator for performance evaluation of next generation (5G and beyond) wireless networks that are inherently more complex: characterized by increasing node density and heterogeneity (due to integration of multiple radio access technologies).
The project sought to advance the state-of-the-art in simulator performance (measured by run-times) for these challenging scenarios based on
1. (Strategy Element 1) Improving Parallelization and other efficiencies in network protocol stack representations in ns-3
2: (Strategy Element 2) Improved Link Abstractions via efficient Link-2-System mapping
The above was complemented by renewed attention to Outreach/Community Building:Amplified existing and created new sustainment oriented activities, based on the organization of annual Workshop on ns-3 + other conferences comprising of more vigorous training, enhanced tutorials etc. for new users [1], and greater online dissemination of key material (sample code, test/validation reports, videos).
Finally, a 1-yr Supplement to the parent award (2324365, performance duration extended to 09/24) was used for the goal of bridging ns-3 simulator with PAWR testbed (in our case, with POWDER @ U. Utah). Such bridging was achieved by using available over-the-air (OTA) outdoor channel measurements conducted using POWDER infrastructure [2] and creating a workflow to convert this to channel models using Generative Adversarial Networks. This represents initial validation for a novel approach to ameliorating the challenge of insufficient dataset availability relative to the needs for appropriate training of AI/ML algorithms [3]
[1] https://www.nsnam.org/research/wns3/wns3-2021/tutorials/
https://www.nsnam.org/research/wns3/wns3-2022/tutorials/
[2] Oscar Bejarano, Kirk Webb, and Rahman Doost-Mohammady. “Data and analysis script for channel measurement campaign at POWDER-RENEW using Iris SDRs” (2020).
[3] S. Roy, L. Cao and L. Balamurugan, ``Generative Channel Modeling for POWDER Datasets usingGANs" UW Technical Rpt (2024).
Last Modified: 12/16/2024
Modified by: Sumit Roy
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