Investigating the estimation of primary occupancy patterns under imperfect spectrum sensing
Al-Tahmeesschi, Ahmed; López-Benítez, Miguel; Lehtomäki, Janne; Umebayashi, Kenta (2017-05-04)
A. Al-Tahmeesschi, M. Lopez-Benitez, J. Lehtomaki and K. Umebayashi, "Investigating the Estimation of Primary Occupancy Patterns under Imperfect Spectrum Sensing," 2017 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), San Francisco, CA, 2017, pp. 1-6. doi: 10.1109/WCNCW.2017.7919112
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Dynamic Spectrum Access (DSA)/Cognitive Radio (CR) systems access the channel in an opportunistic, noninterfering manner with the primary network. As a result, CR performance depends on the primary channel occupancy pattern. The occupancy pattern of primary network is affected by multiple factors including time, location and frequency band. This work focuses on the time domain of spectrum sharing. The objective of this work is to study how the primary user activity pattern in the time domain (i.e., statistical distribution of the durations of idle/busy periods) affects the ability of the CR system to obtain accurate statistical information based on spectrum sensing observations. In this research, we model the primary activity pattern as a Continuous-Time Semi-Markov Chain (CTSMC). Different distributions to imitate occupancy patterns of primary network are tested by means of simulation, first when having a perfect spectrum sensing, then in the presence of imperfect spectrum sensing. It is shown that every occupancy pattern (i.e., distribution) actually leads to different levels of accuracy in the estimated statistics. A new algorithm to palliate the degrading effects of spectrum sensing errors is proposed and evaluated. The new and considered algorithms can improve the prediction of primary network statistics, however with different levels of effectiveness depending on the primary activity pattern.
- Avoin saatavuus