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Distributed Consensus for Coherent Spectrum Sensing

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-23-C-0320
Agency Tracking Number: N23A-T017-0238
Amount: $139,942.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N23A-T017
Solicitation Number: 23.A
Timeline
Solicitation Year: 2023
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-07-17
Award End Date (Contract End Date): 2024-01-16
Small Business Information
10041 Wild Orchid Way
Elk Grove, CA 95757-4345
United States
DUNS: 084613536
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Amitav Mukherjee
 (408) 687-8288
 amukherjee@tiaminetworks.com
Business Contact
 Amitav Mukherjee
Phone: (408) 687-8288
Email: amukherjee@tiaminetworks.com
Research Institution
 University of Massachusetts Boston
 Shala Bonyun
 
100 Morrissey Boulevard
Boston, MA 02125-3393
United States

 (617) 287-5592
 Nonprofit College or University
Abstract

In this Phase I effort, Tiami, LLC, aims to develop and demonstrate a hardware proof of concept for a completely distributed spectrum sensing scheme that leverages consensus learning amongst radio frequency (RF) sensors. The algorithm is based on low-bandwidth message exchange between one-hop neighbors, spans multiple RF bands, is agnostic to the sensing modality, and is resilient to link disruptions. Wireless multi-hop networks are widely used by the Navy for tactical data links and exchange of track measurements or engage-on-remote data. Spectrum domain awareness is crucial for link reliability and anti-jamming measures, however, the distributed nature of multi-hop networks and the need for robustness rules out a centralized spectrum sensing approach. Distributed spectrum sensing is a natural solution, but maintaining coherency across non-colocated nodes with intermittent links is a major technical challenge. Our proposed multi-agent consensus learning scheme for distributed spectrum sensing works as follows. M sensors or agents attempt to collaboratively learn the spectrum occupancy state of N frequency bands. Each sensor exchanges its local sensing likelihood per band with its one-hop neighbors in an iterative process, and updates its local statistic based on a weighted combination of its neighbor observations. Weights are adjusted based on network topology and outlier detection. Convergence is achieved when connected nodes arrive at a consensus.

* Information listed above is at the time of submission. *

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