- Title
- Performance comparison of channel sensing and geolocation database-based resource allocation techniques for cognitive radio networks
- Creator
- Gamage, Samoda; Khan, Jamil Y.; Ngo, Duy T.
- Relation
- IEEE Conference on Vehicular Technology (VTC) . Proceedings of Vehicular Technology Conference (VTC2019-Spring), Vehicular Technology Conference (VTC2019-Spring) (Kuala Lumpur, Malaysia 28 April - 01 May, 2019)
- Publisher Link
- http://dx.doi.org/10.1109/VTCSpring.2019.8746300
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2019
- Description
- Cognitive radio (CR) technology allows efficient management of the limited usable radio spectrum for communications. Traditionally, channel sensing has been used by the CR devices to acquire information about the primary users (PU) and to borrow channels for secondary users (SU). However, inherent limitations could make channel sensing often unreliable and energy inefficient when deployed in large numbers. Geolocation database (GDB) has been proposed as an alternative to the channel sensing technique for cognitive radio networks (CRN). A GDB can act as a repository of channel information in a CRN. Such an approach could improve the utilization of primary and secondary channels, and QoS of the networks. This paper compares the performance of channel sensing-based and GDB-based secondary channel allocation techniques in a CRN. This work has developed mathematical models for the GDB-based CRN to estimate SU and PU throughputs, and energy efficiency, which are used as the performance metrices. Numerical calculations show that the GDB-based resource allocation technique can deliver higher SU and PU throughput levels while offering improved energy efficiency compared to channel sensing-based techniques in a CRN.
- Subject
- sensors; throughput; resource management; interference; databases; quality of service; mathematical model; SDG 7; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1474452
- Identifier
- uon:49284
- Identifier
- ISBN:9781728112176
- Language
- eng
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