SDN-enabled Cognitive Radio Network Architecture

Cicioglu M., Cicioglu S., Calhan A.

IET COMMUNICATIONS, cilt.14, sa.18, ss.3153-3160, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 14 Sayı: 18
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1049/iet-com.2019.1346
  • Dergi Adı: IET COMMUNICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.3153-3160
  • Anahtar Kelimeler: cognitive radio, software radio, radio spectrum management, telecommunication network management, telecommunication control, wireless channels, error statistics, telecommunication power management, bit error rate parameter, channel bonding technique, network architecture software-defined cognitive radio networks, cognitive radio environments, RIVERBED MODELER simulation software, base stations, SDN controller, spectrum management, network resource management, software-defined networking approach, SDN-enabled cognitive radio networks, cognitive radio wireless network, SOFTWARE-DEFINED NETWORKING, WIRELESS NETWORKS, VIRTUALIZATION, MANAGEMENT, PROTOCOL, FUTURE
  • Bursa Uludağ Üniversitesi Adresli: Hayır

Özet

In this study, a new network architecture based on the software-defined networking (SDN) approach is proposed for cognitive radio networks (CRNs). The proposed network architecture [software-defined cognitive radio (SDCR)] assumes the responsibilities of network resource management for CRNs and provides a dynamic spectrum management mechanism with an SDN controller. In this way, the dependency of network users on base stations is reduced in dynamic cognitive radio environments, and network performance is improved by delegating some of the management responsibilities to the controller. The performance analysis of the SDCR is carried out through the RIVERBED MODELER simulation software. End-to-end delays and packet loss rates for the primary network are investigated by selecting different offered loads for secondary users. In addition, for the equal and different packet sizes, primary network and SDCR throughput are examined and network performance is improved by using channel bonding technique. The results indicate that the SDCR outperforms the traditional CRN architecture, in terms of the throughput, and the proposed architecture can provide effective performance. Bit error rate parameter is investigated in the study and the energy consumption parameter of the SDCR is also compared with the cognitive radio wireless network.