An Investigation into Cognitive Radio System Performance

The objective of this thesis is to explore cognitive radio performance through an in-depth literature review and an implementation of a software-defined radio prototyping system. Specifically, this thesis investigates the spectrum-sensing aspect of cognitive radio by comparing two spectrum-sensing methods. It was found in the literature review that a system utilizing matched filter detection would provide higher probability of detection in low signal-to-noise ratio environments when compared to a system utilizing energy detection. These spectrum sensing methods were thus implemented and compared in the cognitive radio systems presented in this thesis. Additionally, experiments were conducted to determine the most efficient intervals for the spectrum sensing and cycle interval periods. Therefore, system performance was measured on the basis of probability of successful primary user signal detection and maximum throughput capabilities, quantified by bit error rate. It was found that a cognitive radio system based on matched filter detection was more robust, given that the transmitted signal of interest was previously known. However, compared to a system based on energy detection, the implementation of the matched filter required more complex algorithms and computational power. These results are consistent with the findings in the literature review

Recent Advances in Embedded Computing, Intelligence and Applications

The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems