Abstract
Cognitive radio (CR) and software-defined radio (SDR) system technologies provide added flexibility and offer improved spectrum efficiency. Cognitive radio is an obstreperous radio communication and networking technology. It is presently undergoing drastic development due to its potential to resolve many of the problems affecting existing systems. Spectrum sensing is the crucial in empowering technology of CR to sense the existence of primary users or licensed user signals and exploit the spectrum holes. Hence, this paper addresses the design of Energy detection spectrum sensing technique for 16-QAM and 64-QAM transceivers employing SDR testbed. In addition, this paper also addresses the practical signal detection and the impact of various filtering windowing methods on detected signals. Furthermore, the impact of different simulation channels such as dynamic channel model and frequency selective faded channel models with Rayleigh and Rician fadings, are presented by testing the spectrum sensing technique on differential phase shift keying transceiver. For practical proof of the concepts, experimental tests are performed using SDR testbed that employs universal software radio peripheral N210 as hardware platform and GNU Radio as an open source free software platform. Therefore, I have reduced the cost of implementation drastically.
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References
Mitola, J. (1992). Software radios-survey, critical evaluation and future directions. In IEEE national telesystems conference (pp. 13/15–13/23, 19–20).
Available, http://www.itu.int/net/newsroom/wrc/2012/features/software.aspx. Accessed 20 July 2017.
Lee, W., & Cho, D.-H. (2013). Channel selection and spectrum availability check scheme for cognitive radio systems considering user mobility. IEEE Communications Letters, 17(3), 463–466.
Lee, J., Andrews, J. G., & Hong, D. (2013). Spectrum-sharing transmission capacity with interference cancellation. IEEE Transactions on Communications, 61(1), 76–86.
Zhao, Q., & Swami, A. (2007). A decision-theoretic framework for opportunistic spectrum access. IEEE Wireless Communications Magazine - Special Issue on Cognitive Wireless Networks., 14, 14–20.
Li, Q., Li, G., Lee, W., Lee, M. I., Mazzarese, D., Clerckx, B., et al. (2010). MIMO techniques in WiMAX and LTE: A feature overview. IEEE Communications Magazine, 48(5), 86–92.
Ettus Research, USRP N210. (2012). https://www.ettus.com/product/details/UN210-KIT. Accessed February 08, 2014.
Radio, G. N. U. (2007). The GNU software radio. Available from World Wide Web, https://gnuradio.org. Accessed 20 July 2017.
O’ shea, T. J., Clancy, T. C., & Ebeid, H. J. (2007). Practical signal detection and classification in gnu radio. In SDR forum technical conference (SDR).
Li, B., Sun, M., Li, X., Nallanathan, A., & Zhao, C. (2015). Energy detection based spectrum sensing for cognitive radios over time–frequency doubly selective fading channels. IEEE Transactions on Signal Processing, 63(2), 402–417.
Reddy, B. S. K., & Lakshmi, B. (2014). BER analysis of energy detection spectrum sensing in cognitive radio using GNU radio. World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, 8(11), 1692–1698.
Arslan, H. (2007). Cognitive radio, software defined radio, and adaptive wireless systems. London: Springer.
Hajimolahoseini, H., Amirfattahi, R., Soltanian-Zadeh, H., & Gazor, S. (2015). Instantaneous fundamental frequency estimation of non-stationary periodic signals using non-linear recursive filters. IET Signal Processing, 9(2), 143–153.
Yang, J., Kwon, H. M., Mukherjee, A., & Pham, K. D. (2016). Spreading-sequence design for partially connected multirelay networks under multipath fading. IEEE Transactions on Vehicular Technology, 65(3), 1420–1433.
Chang, L., Li, G. Y., & Li, J. (2016). Closed-form SNR estimator for MPSK signals in Nakagami fading channels. IEEE Transactions on Vehicular Technology, 65(9), 6878–6887.
Mukumoto, K., & Wada, T. (2014). Realization of root raised cosine roll-off filters using a recursive FIR filter structure. IEEE Transactions on Communications, 62(7), 2456–2464.
Li, B., Hou, J., Li, X., Nan, Y., Nallanathan, A., & Zhao, C. (2016). Deep sensing for space–time doubly selective channels: When a primary user is mobile and the channel is flat Rayleigh fading. IEEE Transactions on Signal Processing, 64(13), 3362–3375.
Reddy, B. S. K., & Lakshmi, B. (2015). Improvement in the performance of WiMAX with channel equalizers and space time block coding techniques using simulink. Wireless Personal Communications Journal, 84(4), 2815–2833.
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Boddu, R.D. Experimental Validation of Spectrum Sensing on Various Transceivers Using Software Defined Radio. Wireless Pers Commun 109, 1615–1630 (2019). https://doi.org/10.1007/s11277-019-06641-2
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DOI: https://doi.org/10.1007/s11277-019-06641-2