Abstract
The swift growth in radio communication technology has drawn to scarcity in wireless spectrum. Literature points out that licensed spectrums allotted by regulatory agencies are underutilized in the allocated band of spectrum. Cognitive radio networks appear to be a promising solution to address the bandwidth scarcity and demands of wireless spectrum. Cognitive radios are confronted to maximally utilize the spectrum through sharing of spectrum with the licensed primary users (PU). The efficiency of cognitive radios mainly depends on the efficiency of the spectrum sensing plane, in which better spectrum utilizations are exploited. A hybrid approach of merging energy detection (ED)-based channel sensing and cyclic prefix autocorrelation detection (CPAD) techniques has cascaded in the way to boost the probability of detection, which is proposed in this chapter. Energy detection techniques implicate no prior knowledge of PU signals, less computational complexity, and low energy consumption but hold uncertainties at low SNRs (at −20 dB to 10 dB). The next mentioned CPAD technique is more robust at less decibels, but it requires a large number of samples yielding complexity and increase in sensing time. Consequently, ED and CPAD techniques, with an influence on the benefits of each technique, have been designed as cascading and implemented using the Universal Software Radio Peripheral (USRP) tool. On comparison, the probability of the detection of ED and CPAD at SNR ranging from −20 dB to 5 dB is around 0.3–0.9 and 0.6–0.9, respectively. The cascaded design has the probability of detection in the bound 0.7–1 for the same specified SNR. Spectrum sensing based on cascading a couple of detectors outperforms in detection probability compared to a single detector.
Keywords
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- AWGN:
-
Additive white Gaussian noise
- CD:
-
Covariance-based detection
- CPAD:
-
Cyclic prefix autocorrelation detection
- CSD:
-
Cyclo stationary feature detection
- ED:
-
Energy detection
- EME:
-
Energy with minimum eigenvalue ratio
- MD:
-
Matched filter detection
- MME:
-
Maximum to minimum eigenvalue ratio
- PU:
-
Primary users
- RF:
-
Radio frequency
- SCD:
-
Spectral correlation density
- SDR:
-
Software-defined radio
- USRP:
-
Universal software radio peripheral
References
Stevenson CR, Chouinard G, Lei Z, Hu W, Shellhammer SJ, Caldwell W (2009) IEEE 802.22: the first cognitive radio wireless regional area network standard. IEEE Commun Mag 47(1):130–138
Cabric D, Tkachenko A, Brodersen RW (2006) Spectrum sensing measurements of pilot, energy, and collaborative detection. In: IEEE military Commun. On proceedings, IEEE Commun, October, pp 1–7
Wild B, Ramchandran K (2006) Detecting primary receivers for cognitive radio applications. In: 1st IEEE international symposium on proceedings. IEEE Commun, New Frontiers Dynamic Spectrum Access Network (DySPAN), pp 124–130
Dandawate BAV, Giannakis GB (1994) Statistical tests for presence of cyclostationarity. IEEE Trans Signal Process 42(9):2355–2369
Mitola J III, Maguire GQ Jr (1999) Cognitive radio: making software radios more personal. Personal Commun IEEE 6(4):13–18
Zeng Y, Liang Y (2009) Spectrum-sensing algorithms for cognitive radio based on statistical co-variances. IEEE Trans Veh Technol 58(4):1804–1815
Zeng Y, Liang Y (2009) Eigenvalue-based spectrum sensing algorithms for cognitive radio. IEEE Trans Commun 57(6):1784–1793
Jamali M, Downey J, Wilikins N, Rehm CR, Tipping J (2009) Development of a FPGA-based high speed FFT processor for wideband direction of arrival applications. In: IEEE Radar conference, pp 1–2
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Iswarya, N., Jayashree, L.S. (2020). Spectrum Sensing Based on Cascaded Approach for Cognitive Radios. In: Kumar, L., Jayashree, L., Manimegalai, R. (eds) Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications. AISGSC 2019 2019. Springer, Cham. https://doi.org/10.1007/978-3-030-24051-6_45
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DOI: https://doi.org/10.1007/978-3-030-24051-6_45
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