Open AccessLoader and tester swarming drones for cellular phone network loading and field test: non-stochastic particle swarm optimization
Author(s) -
Amir Mirzaeinia,
Mostafa Hassanalian,
Mohammad Shekaramiz,
Mehdi Mirzaeinia
Publication year - 2019
Publication title -
journal of autonomous intelligence
Language(s) - English
Resource type - Journals
ISSN - 2630-5046
DOI - 10.32629/jai.v2i2.46
Subject(s) - drone , swarming (honey bee) , particle swarm optimization , swarm behaviour , cellular network , computer science , network performance , simulation , engineering , computer network , artificial intelligence , algorithm , genetics , botany , biology
Cellular network operators have problems to test their network without affecting their user experience. Testing network performance in a loaded situation is a challenge for the network operator because network performance differs when it has more load on the radio access part. Therefore, in this paper, deploying swarming drones is proposed to load the cellular network and scan/test the network performance more realistically. Besides, manual swarming drone navigation is not efficient enough to detect problematic regions. Hence, particle swarm optimization is proposed to be deployed on swarming drone to find the regions where there are performance issues. Swarming drone communications helps to deploy the PSO method on them. Loading and testing swarm separation helps to have almost non-stochastic received signal level as objective function. Moreover, there are some situations that more than one network parameter should be used to find a problematic region in the cellular network. It is also proposed to apply multi-objective PSO to find more multi-parameter network optimization at the same time.