Framework to develop military operational understanding of cognitive radio

Tuukkanen, Topi; Anteroinen, Jukka

doi:10.1109/icmcis.2015.7158696

Cited by 7 publications

(8 citation statements)

References 35 publications

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“…A few more unique surveys have also examined individual components of an RFML ecosystem including dataset generation considerations using tools such as GNU Radio [18] and security and privacy challenges faced in cognitive wireless sensor networks [19], [20]. Additionally, though discussed in the context of CR, [21] and [22] also discuss operational considerations for using RFML in a military setting and solutions for combating practical imperfections encountered in CR system (i.e. noise uncertainty, channel/interference uncertainty, hardware imperfections, signal uncertainty, synchronization issues), with discussion relevant to Sections IV and VII.…”

Section: Prior Work and Contributionsmentioning

confidence: 99%

An RFML Ecosystem: Considerations for the Application of Deep Learning to Spectrum Situational Awareness

Wong

Clark

Flowers

et al. 2021

IEEE Open J. Commun. Soc.

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While deep learning (DL) technologies are now pervasive in state-of-the-art Computer Vision (CV) and Natural Language Processing (NLP) applications, only in recent years have these technologies started to sufficiently mature in applications related to wireless communications, a field loosely termed Radio Frequency Machine Learning (RFML). In particular, recent research has shown DL to be an enabling technology for Cognitive Radio (CR) applications as well as a useful tool for supplementing expertly defined algorithms for spectrum awareness applications such as signal detection, estimation, and classification. A major driver for the usage of RFML is that little, to no, a priori knowledge of the intended spectral environment is required, given that there is an abundance of representative raw Radio Frequency (RF) data to facilitate training and evaluation. However, in addition to this fundamental need for sufficient data, there are other key considerations, such as trust, security, and hardware requirements, that must be taken into account before deploying RFML systems in real-world wireless communication applications that largely go unaddressed in the current literature. This paper examines the prior works related to these major research considerations, with focus on the dependencies between them and factors unique to the RFML space.

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Section: Prior Work and Contributionsmentioning

confidence: 99%

An RFML Ecosystem: Considerations for the Application of Deep Learning to Spectrum Situational Awareness

Wong

Clark

Flowers

et al. 2021

IEEE Open J. Commun. Soc.

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“…Within the CR research community, reference [45] collected and rearranged proposed technical properties and possible technical characteristics of cognitive radios. One of these property groups, dynamic spectrum access, was tested in [19] through DOTMPLFI.…”

Section: B Military Capabilitymentioning

confidence: 99%

“…As [19] suggests, there seems to be an open research gap between the military art of war and modern radio engineering disciplines. Military expectations, needs, and formal requirements have not been elaborated in the publicly available academic documentation, whereas engineering efforts and potential new capabilities have not been crossreferenced nor analyzed to meet those military requirements.…”

Section: Introductionmentioning

confidence: 99%

Assessment of cognitive radio networks through military capability development viewpoint

Tuukkanen

Couturier

Buchin

et al. 2018

2018 International Conference on Military Communications and Information Systems (ICMCIS)

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In the absence of real-life implementations, this paper attempts to conceptually capture potential benefits and improvements that Cognitive Radio Networks (CRN) could introduce to a military environment. Functional business capability areas, as well as a system model, are used to frame analysis. Military CRNs improve reliability and availability of information flows. These may lead to improved information sharing and situation awareness and eventually to wider adoption of mission command and self-synchronization. However, fundamental capacity benefits of the CRNs remain a research topic. The requirement of computer-aided planning, preparation and simulation environment in support of deployed CRNs could also serve as a research and development platform for a Cognitive Radio System development activity as a collaborative international effort.

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“…Maintaining readiness involves the maintenance of existing capabilities and acquisition of new or replacing capabilities. Although we do not here need to go through all military capability areas of Doctrine, Organization, Training, Materiel, Leadership, Personnel, Facilities, Interoperability [21], a few observations can be made.…”

Section: A Peace Timementioning

confidence: 99%

Armed forces' views on Shared Spectrum Access

Tuukkanen

Yrjölä

Matinmikko

et al. 2017

2017 International Conference on Military Communications and Information Systems (ICMCIS)

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Recent transition from mobile telephony to daily consumption of mobile data presents the challenge of adequate spectrum availability. Besides regulators and operators, this challenge faces both public safety authorities and military as well. This paper investigates how selected spectrum sharing concepts (namely, European Licensed Shared Access (LSA) and US Citizens Broadband Service (CBRS)) support the military interests either as a Primary User (PU) or as a Secondary User (SU) and whether these concepts facilitate temporal adjustments of national defence from peace time mode of operations to hybrid warfare or to large scale homeland defence. In this paper we have shown that military spectrum needs vary from one scenario to another and that Shared Spectrum Access may support expected needs of spectrum in times of most dire stress. Both of the concepts reviewed have built in dynamism, which however does not support PU-SU role changes across tiers.

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Framework to develop military operational understanding of cognitive radio

Cited by 7 publications

References 35 publications

An RFML Ecosystem: Considerations for the Application of Deep Learning to Spectrum Situational Awareness

An RFML Ecosystem: Considerations for the Application of Deep Learning to Spectrum Situational Awareness

Assessment of cognitive radio networks through military capability development viewpoint

Armed forces' views on Shared Spectrum Access

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