Convergence of mobile broadband and broadcast services: A cognitive radio sensing and sharing perspective
Kagiso Rapetswa, Ling Cheng
Abstract
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.