Review: Effective Solutions for Challenges in Cognitive Radio Networks

Review: Effective Solutions for Challenges in Cognitive Radio Networks

Madhushi P. Ranasinghe (Charles Sturt University, Australia) and Malka N. Halgamuge (The University of Melbourne, Australia)
DOI: 10.4018/978-1-5225-7458-3.ch005
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Cognitive radio technology (CRNs) will be the fundamental driving force behind the next generation (5G) mobile communication systems as it provides the optimal solution for the problem of spectrum scarcity via dynamic spectrum usage. The CRNs, however, pose several key challenges such as network management, spectrum allocation, and access, energy efficiency, interference, cost, spectrum sensing, security, and quality of service (QoS). In this chapter, the authors undertake a comprehensive analysis of 30 peer-reviewed scientific publications collated from 2017 to 2018 April that examine cognitive radio networks to identify practical solutions proposed to overcome critical challenges in this field. Nine distinct challenges were considered: network management, spectrum allocation, and access, energy efficiency, interference, cost, spectrum sensing, security, and QoS. The analysis demonstrates that the majority of research work related to CRN focuses on approaches to improve network management and, specifically, optimization of networks.
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The rapid growth in mobile communications, wireless technologies, big data, cloud computing and Internet of Things (IoT) has significantly raised the demand for both licensed and unlicensed spectrum bands. Therefore, increasing utilization of the limited radio frequency spectrum has become one of the biggest concerns in the communication world. The most prominent contribution towards fulfilling this objective is the development of Cognitive Radio Network (CRN). As its name implies, CRN is an intelligent radio network that can automatically detect available channels in a spectrum and then dynamically change its reception and transmission parameters in order to allow more concurrent wireless communications in a given spectrum band (Sharma V., 2015). The main idea is to allow secondary users (SUs) who do not have access to the licensed spectrum, access to the licensed spectrum which is allocated for primary users (Pus). But it is important that this access does not cause any interference to the PUs’ communication. Hence, it is apparent that minimizing interference with PUs is one of the key challenges in CRNs. Likewise, there are a number of challenging aspects that affect the performance of a CRN such as, accurate spectrum sensing, spectrum sharing and allocation, energy efficiency, scalability, mobility, cost, security, heterogeneity, scheduling, network management and quality of service (QoS). In order to get the maximum benefit out of this novel technology, it is important to realize the research gaps in this end and analyze the existing work to obtain an overall understanding of the research trends associated with CRNs and compare solutions provided in order to determine the most ideal solution to minimize the foregoing challenges.

Recent research focuses on these pressing challenges of CRNs and has introduced a wide range of mechanisms to reduce their impact and thus, improve the performance of CRNs. Some researchers have based their studies on specific applications. For example, Bräysy et al. (2017) discuss issues in CRN management in the context of military applications, Eze et al. (2017) focus on vehicular ad-hoc networks while Salameh et al. (2018) base their study on delay-sensitive IoT applications. Others discuss about the general concept of cognitive radio. Some other authors have paid attention to CRNs under various conditions such as security attacks (Salameh, Almajali, Ayyash, & Elgala, 2018), mobile networks (Bennaceur, Souihi, Idoudi, Saidane, & Mellouk, 2017) and multi-user networks (Wang, Ekin, & Serpedin, 2018). Very few researchers have performed survey or review articles on the concepts and issues of CRNs. For instance, Chen et al. (2018) have presented a comprehensive overview of the research contributions towards spectrum sharing process in CRNs that covers spectrum sensing, utilization, allocation and handoffs and related issues. Also Sumi and Ganesh (2017) analyzed techniques to enhance energy efficiency and network performance in their work.

An important concept is centralized spectrum sensing where a centralized server will estimate the spectrum access opportunity of a SU (Kim, Ko, Cha, & Kim, 2017). Other research has determined the effect of imperfect spectrum monitoring on the performance of a CRN (Thakur, Kumar, Pandit, Singh, & Satashia, 2017). Couturier et al. (2015) claim that cluster-based cooperative spectrum sensing can be applied to improve the performance of sensing process as clustering the SU network allows SUs to report sensing results to a common receiver with a spatial diversity. Furthermore, the study also defines what QoS is in the context of cognitive radio. QoS management in CRNs is similar to network management where appropriate network configuration such as routing and bandwidth allocation is allowed so that traffic with distinct QoS requirements can be controlled (Couturier, Krygier, Bentstuen, & Nir, 2015).

However, the fact that “to which extent these challenges are addressed” by the research and “what are the research trends” associated with CRNs seems to be under-researched. Further, the majority of authors so far have only discussed techniques to mitigate very few challenges of CRNs in their work, but no one discusses all challenging aspects in a single study.

Key Terms in this Chapter

Interweave Spectrum Access: A method that allows SU access to the licensed spectrum only when PU is not utilizing the channel.

Cooperative Spectrum Sensing: The process used by secondary user (SU) to confirm the state of channel (idle or busy) through the cooperation with other SUs or a fusion center.

Hybrid Spectrum Access: The method that allows SU to switch between overlay, underlay and interweave spectrum access methods based on the state of PUs.

Underlay Spectrum Access: A method of enabling SU and primary user PU concurrent access to the spectrum based on the degree of interference that PU can tolerate during the SU’s spectrum access period.

Game-Based Spectrum Allocation: A method of allocating spectrum among PUs and SUs based on the game theory which includes players, strategies, and pay-offs.

Overlay Spectrum Access: A method of enabling SU and PU concurrent access to the spectrum by offsetting the effect of interference caused by SU using a part of SU’s power for relaying PU’s information.

Energy Detection: A method of spectrum sensing used by SU to identify the state of channel by itself. The knowledge of signal and noise power is used to identify the presence or absence of a PU.

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