The Future of mm-wave Wireless Communication Systems for Unmanned Aircraft Vehicles in the Era of Artificial Intelligence and Quantum Computing

As wireless networks are getting increasingly complex, there is a strong need to develop novel approaches and capabilities for high throughput communication and processing of wireless data. This need is more pressing as unmanned aircraft vehicles (UAVs) are applied in new areas and innovations such as UAV (Unmanned Aerial Vehicle) swarms are developed. Millimeter-wave helps to meet the throughput demand as it provides the higher bandwidth that is needed for 5G wireless communication and beyond. But these higher frequencies also come with their own challenges, such as low signal penetration depth. The main challenge in modern wireless networks is to be able to accurately predict the dynamically changing radio environment. With the introduction of narrow beams in the mm-wave frequency, tracking of the beams is a great challenge. Artificial Intelligence (AI) and Quantum computing (QC) could be employed to resolve this problem of beam tracking. In this talk, the challenges in mm-wave wireless networks for vehicle-to-vehicle communication will be highlighted and the ideas involving recent developments in phased-array antenna, beam-steering, beam-alignment, and tracking will be discussed. We shall then critically evaluate the need for Artificial Intelligence (AI) and Quantum computing (QC) within the network architecture to provide the required capability for tasks such as beam-control, data-feed processing, resource, and interference management. The next-generation wireless networks need to be self-predictive and proactive to handle futuristic applications like holographic communication, haptic feedback, or any latency-dependent application. Especially in the context of rapidly changing environment where the UAVs are moving fast, AI and/or QC would play a crucial role to achieve a dynamically adaptive network. Next-generation wireless networks offer more capacity for the conveyance of relevant information from onboard instruments such as cameras or thermal sensors. Thus, there shall be a need for the efficient processing of potentially copious amounts of raw data for relevant information. For example, to deploy a drone swarm in a search and rescue operation, the use of QC or AI could improve the processing of received visual information with respect to processing speed. The application of AI and QC for mm Wave-UAV communications is a promising research direction to break through the traditional communication paradigm and integrate communication, computing, and storage resources.