Studying data loss, nonlinearity, and modulation effects in drone swarm channels with artificial intelligence

Volodymyr Kharchenko (), Andrii Grekhov () and Vasyl Kondratiuk ()
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Volodymyr Kharchenko: National Aviation University
Andrii Grekhov: National Aviation University
Vasyl Kondratiuk: National Aviation University

Telecommunication Systems: Modelling, Analysis, Design and Management, 2024, vol. 87, issue 3, No 15, 743-758

Abstract: Abstract Drones can be used to create wireless communication networks in swarms using Artificial intelligence (AI). Their mobility and line-of-sight capability have made them key solutions for civil and military applications. AI is also developing rapidly nowadays and is being successfully applied due to the huge amount of data available. This has led to the integration of AI into networks and its application to solve problems associated with drone swarms. Since AI systems have to process huge amounts of information in real time, this leads to increased data packet loss and possible loss of communication with the control center. This article is devoted to the calculation of packet losses and the impact of traffic parameters on the data exchange in swarms. Original swarm models were created with the help of MATLAB and NetCracker packages. Dependences of data packet losses on the transaction size are calculated for different drone number in a swarm using NetCracker software. Data traffic with different parameters and statistical distribution laws was considered. The effect of different distances to drones on the base station workload has been simulated. Data transmission in a swarm was studied using MATLAB software depending on the signal-to-noise ratio, nonlinearity levels of base station amplifier, signal modulation types, base station antenna diameters, and signal phase offsets. The data obtained allows foresee the operation of drone communication channels in swarms.

Keywords: Drone; Communications; Swarms; Data traffic; Packet error rate (PER); Artificial intelligence (AI) (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11235-024-01210-w

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