Article: Loader and tester swarming drones for cellular phone network loading and field test: non-stochastic particle swarm optimization.
Cellular networks have been growing rapidly over the last few decades. 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, Amir Mirzaeinia, Mostafa Hassanalian, Mohammad Shekaramiz, Mehdi Mirzaeinia conducted a study on the loader and tester swarming drones and the survey result published on the journal of autonomous intelligence.
They found that loader and tester swarming drone separation help to have a non-stochastic objective function to optimize. In this proposed method each tester drone works as particle agent. Swarming tester drones communicate to deploy the PSO in all drones. This helps to find the network problematic regions. Bayesian optimization can also be used to achieve more efficient optimization than PSO, which will be considered in our future work. Besides, there are cases in cell phone network optimization which need multi-objective optimization. In other words, multi-objective PSO/BO can be applied in swarming drone to find more complicated problematic regions.
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