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The increased usage of wireless communication has created a wireless frequency shortage problem. Cognitive Radio (CR) has attracted public attention, as one of the solutions that can resolve this issue. In this paper, the authors built an actual CR system testbed using the SDR (Software Defined Radio) platform, USRP (Universal Software Radio Peripheral) board, the SDR development toolkit, GNU Radio, and Raspberry Pi3, which is a single board computer. They configured Secondary User (SU)s with Raspberry Pi3 for straightforward and portable test environment. The authors' testbed performs spectrum sensing based on energy detection and determines whether the channel is occupied or not. Experimental results not only show performance but also provide their testbed that works well in multi-hop environments.

Cognitive radio (CR) is a spectrum sharing technology that facilitates a hierarchal coexistence between licensed and license-exempt users over licensed bands. One of the biggest challenges in cognitive radio network (CRN) is efficient spectrum management. Recently, a trend has shifted towards the use of machine learning techniques such as reinforcement learning for learning problem in CRN. This paper provides an insight into the working principles of reinforcement learning based CRN and summarizes the recent survey papers done on the topic of learning based CRN. This paper also presents a 5G technology i.e. network slicing, based intelligent CRN architecture for efficient spectrum management. Some challenges in the existing solutions and future research directions are also introduced.

With the advent of ubiquitous environments, the smart phone has come into wide use and the demand for various content increases. Thus, in order to efficiently utilize limited resources cognitive radio technology is regarded as a possible solution. Besides spectrum sensing or access schemes, the provision of VoIP traffic service for secondary users with limited spectrum resources is a very important issue. In this paper, a resource allocation and control system for VoIP QoS provision in cognitive radio networks is proposed. Firstly, as the system model, the time structure of the network is addressed and, according to the structure, a bandwidth broker is proposed. In addition, based on available bandwidth estimated by the bandwidth broker, a connection admission control for secondary users is developed. It is demonstrated that the provision of VoIP QoS is greatly affected by channel utilization, the number of channels, and the length of timeslot.

We propose a quality-aware media access control (MAC) protocol for real-time voice delivery in cognitive radio- (CR-) enabled wireless sensor networks (WSNs). The temporal structure of the system model is addressed by using periodic timeslots in order to make more efficient use of the spectrum. In our proposed temporal structure, a bandwidth broker in such centralized CR networks (CCRNs) is selected as a central counterpart to synchronize with secondary users (SUs) and assign spectral resources to them. We develop an analytical model for SUs for the Call Admission Control (CAC) of voice traffic using the quality of service (QoS) requirements of delay bound and delay bound violation probability. Ours is an approach that provides reliable data transmission and reduces packet delay and packet delay jitter. In addition to the aforementioned packet-level performance metrics, we calculated a call-level performance metric, that is, the number of acceptable SUs, satisfying QoS requirements.