cognitive radio, distributed networks, spectrum sensing and sharing, next generation networks
With next generation networks driving the confluence of multi-media, broadband, and broadcast services, Cognitive Radio (CR) networks are positioned as a preferred paradigm to address spectrum capacity challenges. CRs address these issues through dynamic spectrum access. However, the main challenges faced by the CR pertain to achieving spectrum efficiency. As a result, spectrum efficiency improvement models based on spectrum sensing and sharing models have attracted a lot of research attention in recent years, including CR learning models, network densification architectures, and massive Multiple Input Multiple Output (MIMO), and beamforming techniques. This paper provides a survey of recent CR spectrum efficiency improvement models and techniques, developed to support ultra-reliable low latency communications that are resilient to surges in traffic and competition for spectrum. These models and techniques, broadly speaking, enable a wide range of functionality ranging from enhanced mobile broadband to large scale Internet of Things (IoT) type communications. In addition and given the strong correlation between the typical size of a spectrum block and the achievable data rate, the models studied in this paper are applicable in ultra-high frequency band. This study therefore provides a good review of CRs and direction for future investigations into newly identified 5G research areas, applicable in industry and in academia.
Tsinghua University Press
Kagiso Rapetswa, Ling Cheng. Convergence of mobile broadband and broadcast services: A cognitive radio sensing and sharing perspective. Intelligent and Converged Networks 2020, 1(1): 99-114.