The rapid growth of wireless communication systems around the world has led to a significant demand for more radio spectrum resources. Radio spectrum regulators are facing several challenges due to spectrum scarcity caused not only by increased demand but also by inefficient management of this resource. Currently, the spectrum is statically allocated to licensed users even though some of them use this resource intermittently. This has resulted in underutilisation of the spectrum and therefore, spectrum scarcity for new users. Cognitive radio aims at solving this problem through dynamic spectrum access, which uses spectrum holes opportunistically. A cognitive radio can select the best channel available at a particular location and time. To accomplish that goal, the system requires some criteria, or channel quality estimation metrics, to rank the channels that it potentially could use. In this study, the authors present a survey on several channel quality estimation metrics such as bit error rate, signal‐to‐interference‐plus‐noise ratio, outage probability, channel quality estimation index, spectrum sensing accuracy of secondary user, and the idle state duration expectation.

Research in the field of autonomous Unmanned Aerial Vehicles (UAVs) has significantly advanced in recent years, mainly due to their relevance in a large variety of commercial, industrial, and military applications. However, UAV navigation in GPS-denied environments continues to be a challenging problem that has been tackled in recent research through sensor-based approaches. This paper presents a novel offline, portable, real-time in-door UAV localization technique that relies on macro-feature detection and matching. The proposed system leverages the support of machine learning, traditional computer vision techniques, and pre-existing knowledge of the environment. The main contribution of this work is the real-time creation of a macro-feature description vector from the UAV captured images which are simultaneously matched with an offline pre-existing vector from a Computer-Aided Design (CAD) model. This results in a quick UAV localization within the CAD model. The effectiveness and accuracy of the proposed system were evaluated through simulations and experimental prototype implementation. Final results reveal the algorithm’s low computational burden as well as its ease of deployment in GPS-denied environments.

Integration of AI and collaborative robotics: transformative applications and ethical considerations