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Rendering to their abilities to provide ubiquitous connectivity, flexibly andcost effectively, unmanned aerial vehicles (UAVs) have been getting more andmore research attention. To take the UAVs' performance to the next level,however, they need to be merged with some other technologies likenon-orthogonal multiple access (NOMA) and millimeter wave (mmWave), which bothpromise high spectral efficiency (SE). As managing UAVs efficiently may not bepossible using model-based techniques, another key innovative technology thatUAVs will inevitably need to leverage is artificial intelligence (AI).Designing an AI-based technique that adaptively allocates radio resources andplaces UAVs in 3D space to meet certain communication objectives, however, is atough row to hoe. In this paper, we propose a neuroevolution of augmentingtopologies NEAT framework, referred to as ProSky, to manage NOMA-mmWave-UAVnetworks. ProSky exhibits a remarkable performance improvement over amodel-based method. Moreover, ProSky learns 5.3 times faster than andoutperforms, in both SE and energy efficiency EE while being reasonably fair, adeep reinforcement learning DRL based scheme. The ProSky source code isaccessible to use here: https://github.com/Fouzibenfaid/ProSky

ProSky: NEAT Meets NOMA-mmWave in the Sky of 6G