Group-Mobility-Aware Spectrum Management for Future Digital Battlefields

Zheng, Haitao; Shi, Jinqiao; Cao, Lili

doi:10.1109/milcom.2006.302052

Cited by 3 publications

(2 citation statements)

References 16 publications

(11 reference statements)

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“…Using the definitions given in [23], we identify that there are two major zones in urban battlefield, theater of operation (TOO) and theater of war (TOW). In our work, friendly force soldiers are distributed in the TOO according to the structured group mobility model (SGMM) which is used for modelling task oriented node positioning [24,25]. Drone shooters are distributed uniformly in the TOW.…”

Section: System Model and Assumptionsmentioning

confidence: 99%

“…They operate and move in groups to accomplish a given task [28]. The SGMM is used to model node positioning which helps researchers to analyze military networks in a more realistic way [24,25]. In our work, we consider a snapshot of a network where a number of soldier groups are distributed in the TOO, which is a cell with radius r T OO .…”

Section: Modeling Soldier Distributionmentioning

confidence: 99%

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Measurement based threat aware drone base station deployment

Akarsu¹,

Girici²

2020

Turk J Elec Eng & Comp Sci

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Unmanned aerial vehicles are gaining importance with many civilian and military applications. Especially the surveillance, search/rescue, and military operations may have to be carried out in extremely constrained environments. In such scenarios, drone base stations (DBSs) have to provide communication services to the people at the ground. The ground users may have no access to the global positioning system (GPS); therefore, their locations have to be estimated using alternative techniques. Besides there may be threats in the environment, such as shooters. In this work, we address the problem of optimal DBS deployment under the aforementioned constraints. We propose a novel DBS deployment algorithm that uses estimated positions of ground users and threats. The proposed algorithm is based on receiver signal strength-based maximum likelihood estimate of user locations and K-means clustering supported heuristic that takes into account the positions of threats. Numerical results show that proposed algorithm performs close to the computation intensive near-optimal algorithm and strikes a good trade-off between the number of unserved users and the probability of DBSs not being hit.

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Section: System Model and Assumptionsmentioning

confidence: 99%