Integrating cognitive radio with unmanned aerial vehicles - Santana, Guilherme Marcel Dias (2021)
An Unmanned Aerial Vehicle (UAV) is an aircraft that operates without a human on-board and can be flown autonomously or controlled remotely. Due to its unmanned operation, UAVs need technologies so they can not only fly autonomously, but also communicate with base stations, flight controllers, computers, devices or even other UAVs. Traditionally, UAVs operate within unlicensed spectrum bands, competing against the increasing number of mobile devices and other wireless networks. This use could lead to interference that affect UAVs communication and problems with overcrowded spectrum. Cognitive Radio (CR) presents itself as a promising technology to solve these problems. CR provides a smart wireless communication which, instead of using a transmission frequency defined in the hardware, uses software defined radio. This allows CR to adapt its transmission frequency in a smart way, using free transmission channels and/or choosing them accordingly with the applications requirements. The combination of UAVs and CR can be used in missions where the conventional UAVs face limitations due to communication problems. Moreover, CR is considered a key enabler for adequately deploying communication paradigms that require high connectivity, such as Smart Cities, 5G, Internet of Things (IoT) and, thus, Internet of Flying Things (IoFT). Though both CR and UAVs are well-established fields of research, the combination of these two elements is little explored in literature. Therefore, this work identifies gaps and opportunities, as well as challenges on the field. Furthermore, this work contributes to the progress regarding the integration of CR and UAVs. To do so, this work presents the definition of CR technologies, as well as their integration on a real mission of data collection. This works results differ to the others on the literature in terms of, for example, highlighting the limitation in real scenario of traditionally deployed Machine Learning algorithms using simulated data on the field.