Joint signal detection and synchronization for OFDM based cognitive radio networks and its implementation
- 146 Downloads
In the cooperative cognitive radio networks (CRN), often secondary user (SU) relays the information of primary user (PU) as a rewarding relay to improve diversity gain of PU without being a legitimate user. So the SU needs to detect the signal, blindly estimate the parameters introduced in channel and reconstruct the signal before relaying it to the primary receiver. In this paper, a joint scheme for signal detection and non-data-aided (blind) parameter estimation of orthogonal frequency division multiplexing (OFDM) based CRN has been discussed. Based upon binary hypothesis testing problem, the SU formulates a minimum cost signal detection scheme for the presence of OFDM based PU signal in CRN. The probability of detection, probability of false alarm and receiver operating characteristics have been presented to illustrate the performance of signal detection scheme in the CRN. Further, the effective throughput analysis of the secondary system has been demonstrated in the context when the primary system is detected as idle. Blind synchronous parameters of OFDM signal such as carrier frequency offset and symbol timing offset has been presented over the wireless fading channel in the CRN. Existing theoretical studies on blind parameter estimation algorithms for signals have been carried out but most of them have not been implemented in order to validate their feasibility. Here, a software-defined radio testbed has been implemented using national instruments hardware in a multipath indoor environment and experimental results have been provided using real measurement system. The preliminary measurement and simulation results demonstrate that the proposed blind estimator is capable of estimating the concerned parameters and constellation symbols over an indoor propagation environment.
KeywordsCognitive radio network Detection and estimation Orthogonal frequency division multiplexing Blind parameter estimation Software-defined radio
- 14.van Nee, R., & Prasad, R. (2000). OFDM for wireless multimedia communications. Boston: Artech House.Google Scholar
- 18.Dikmese, S., Ilyas, Z., Sofotasios, P., Renfors, M., & Valkama, M. (2016). Novel frequency domain cyclic prefix autocorrelation based compressive spectrum sensing for cognitive radio. In 2016 IEEE 83rd vehicular technology conference (pp. 1–6). VTC Spring.Google Scholar
- 20.Lei, Z., & Chin, F. (2008). OFDM signal sensing for cognitive radios. In 2008 IEEE 19th international symposium on personal, indoor and mobile radio communications (pp. 1–5).Google Scholar
- 30.Younis, S., Al-Dweik, A., Hazmi, A., Tsimenidis, C. C., & Sharif, B. S. (2010). Symbol timing offset estimation scheme for OFDM systems based on power difference measurements. In 21st Annual IEEE international symposium on personal, indoor and mobile radio communications (pp. 927–932).Google Scholar
- 32.Pan, Y. C., Phoong, S. M., & Lin, Y. P. (2014). An improved ESPRIT-based blind CFO estimation algorithm in OFDM systems. In 2014 48th Asilomar conference on signals, systems and computers (pp. 258–262).Google Scholar
- 33.Liu, J. G., Wang, X., & Chouinard, J. Y. (2012). Iterative blind OFDM parameter estimation and synchronization for cognitive radio systems. In 2012 IEEE 75th vehicular technology conference (pp. 1–5). VTC Spring.Google Scholar
- 36.Kumar, M., & Majhi, S. (2015). Blind synchronization of OFDM system and CRLB derivation of CFO over fading channels. In 2015 10th International conference on information, communications and signal processing (ICICS) (pp. 1–6).Google Scholar
- 38.Majhi, S., Gupta, R., Xiang, W., & Glisic, S. (2017). Hierarchical hypothesis and feature based blind modulation classification for linearly modulated signals. IEEE Transactions on Vehicular Technology, 99, 1–1.Google Scholar
- 39.Majhi, S., Gupta, R., & Xiang, W. (2017). Novel blind modulation classification of circular and linearly modulated signals using cyclic cumulant. In 28th Annual IEEE international symposium on personal, indoor and mobile radio communications (pp. 1–6).Google Scholar
- 40.Van Trees, H. L. (2002). Detection, estimation, and modulation theory. Part IV: Optimum array processing. New York: Wiley. http://opac.inria.fr/record=b1105852.
- 42.Kay, S. (1998). Fundamentlas of statistical signal processing, volume 2: Detection theory. Englewood: Prentice-Hall.Google Scholar