Joint signal detection and synchronization for OFDM based cognitive radio networks and its implementation

Abstract

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.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

References

  1. 1.

    Axell, E., Leus, G., Larsson, E. G., & Poor, H. V. (2012). Spectrum sensing for cognitive radio: State-of-the-art and recent advances. IEEE Signal Processing Magazine, 29(3), 101–116.

    Article  Google Scholar 

  2. 2.

    Ghosh, C., Roy, S., & Cavalcanti, D. (2011). Coexistence challenges for heterogeneous cognitive wireless networks in TV white spaces. IEEE Wireless Communications, 18(4), 22–31.

    Article  Google Scholar 

  3. 3.

    Zhang, Q., Jia, J., & Zhang, J. (2009). Cooperative relay to improve diversity in cognitive radio networks. IEEE Communications Magazine, 47(2), 111–117.

    Article  Google Scholar 

  4. 4.

    Zhao, N., & Sun, H. (2011). Robust power control for cognitive radio in spectrum underlay networks. KSII Transactions on Internet & Information Systems, 5(7), 1214–1229.

    Article  Google Scholar 

  5. 5.

    Cadambe, V. R., & Jafar, S. A. (2008). Interference alignment and degrees of freedom of the \(K\)-user interference channel. IEEE Transactions on Information Theory, 54(8), 3425–3441.

    MathSciNet  Article  MATH  Google Scholar 

  6. 6.

    Zhao, N., Yu, F. R., Sun, H., Nallanathan, A., & Yin, H. (2013). A novel interference alignment scheme based on sequential antenna switching in wireless networks. IEEE Transactions on Wireless Communications, 12(10), 5008–5021.

    Article  Google Scholar 

  7. 7.

    Zhao, N., Yu, F. R., Sun, H., Yin, H., Nallanathan, A., & Wang, G. (2015). Interference alignment with delayed channel state information and dynamic AR-model channel prediction in wireless networks. Wireless Networks, 21(4), 1227–1242.

    Article  Google Scholar 

  8. 8.

    Zhao, N., Yu, F. R., Sun, H., & Li, M. (2016). Adaptive power allocation schemes for spectrum sharing in interference-alignment-based cognitive radio networks. IEEE Transactions on Vehicular Technology, 65(5), 3700–3714.

    Article  Google Scholar 

  9. 9.

    El Ayach, O., Peters, S. W., & Heath, R. W. (2013). The practical challenges of interference alignment. IEEE Wireless Communications, 20(1), 35–42.

    Article  Google Scholar 

  10. 10.

    Zhao, N., Yu, F. R., & Leung, V. C. M. (2015). Opportunistic communications in interference alignment networks with wireless power transfer. IEEE Wireless Communications, 22(1), 88–95.

    Article  Google Scholar 

  11. 11.

    Li, X., Zhao, N., Sun, Y., & Yu, F. R. (2016). Interference alignment based on antenna selection with imperfect channel state information in cognitive radio networks. IEEE Transactions on Vehicular Technology, 65(7), 5497–5511.

    Article  Google Scholar 

  12. 12.

    Gao, F., Zhang, R., Liang, Y. C., & Wang, X. (2010). Design of learning-based MIMO cognitive radio systems. IEEE Transactions on Vehicular Technology, 59(4), 1707–1720.

    Article  Google Scholar 

  13. 13.

    Xie, H., Wang, B., Gao, F., & Jin, S. (2016). A full-space spectrum-sharing strategy for massive MIMO cognitive radio systems. IEEE Journal on Selected Areas in Communications, 34(10), 2537–2549.

    Article  Google Scholar 

  14. 14.

    van Nee, R., & Prasad, R. (2000). OFDM for wireless multimedia communications. Boston: Artech House.

    Google Scholar 

  15. 15.

    Sidhu, G. A. S., Gao, F., Wang, W., & Chen, W. (2013). Resource allocation in relay-aided OFDM cognitive radio networks. IEEE Transactions on Vehicular Technology, 62(8), 3700–3710.

    Article  Google Scholar 

  16. 16.

    Ali, A., & Hamouda, W. (2015). Spectrum monitoring using energy ratio algorithm for OFDM-based cognitive radio networks. IEEE Transactions on Wireless Communications, 14(4), 2257–2268.

    Article  Google Scholar 

  17. 17.

    Jntti, J., Chaudhari, S., & Koivunen, V. (2015). Detection and classification of OFDM waveforms using cepstral analysis. IEEE Transactions on Signal Processing, 63(16), 4284–4299.

    MathSciNet  Article  MATH  Google Scholar 

  18. 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.

  19. 19.

    Shi, Z., McLernon, D., Ghogho, M., & Wu, Z. (2014). Improved spectrum sensing for OFDM cognitive radio in the presence of timing offset. EURASIP Journal on Wireless Communications and Networking, 2014(1), 224. doi:10.1186/1687-1499-2014-224.

    Article  Google Scholar 

  20. 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).

  21. 21.

    Chaudhari, S., Koivunen, V., & Poor, H. V. (2009). Autocorrelation-based decentralized sequential detection of OFDM signals in cognitive radios. IEEE Transactions on Signal Processing, 57(7), 2690–2700.

    MathSciNet  Article  MATH  Google Scholar 

  22. 22.

    Lei, Z., & Chin, F. P. S. (2010). Sensing OFDM systems under frequency-selective fading channels. IEEE Transactions on Vehicular Technology, 59(4), 1960–1968.

    Article  Google Scholar 

  23. 23.

    Speth, M., Fechtel, S., Fock, G., & Meyr, H. (2001). Optimum receiver design for OFDM-based broadband transmission. II. A case study. IEEE Transactions on Communications, 49(4), 571–578.

    Article  Google Scholar 

  24. 24.

    Mostofi, Y., & Cox, D. (2006). Mathematical analysis of the impact of timing synchronization errors on the performance of an OFDM system. IEEE Transactions on Communications, 54(2), 226–230.

    Article  Google Scholar 

  25. 25.

    Filippi, A., & Serbetli, S. (2009). OFDM symbol synchronization using frequency domain pilots in time domain. IEEE Transactions on Wireless Communications, 8(6), 3240–3248.

    Article  Google Scholar 

  26. 26.

    Hsieh, H.-T., & Wu, W.-R. (2009). Maximum likelihood timing and carrier frequency offset estimation for OFDM systems with periodic preambles. IEEE Transactions on Vehicular Technology, 58(8), 4224–4237.

    Article  Google Scholar 

  27. 27.

    van de Beek, J.-J., Sandell, M., & Borjesson, P. (1997). ML estimation of time and frequency offset in OFDM systems. IEEE Transactions on Signal Processing, 45(7), 1800–1805.

    Article  MATH  Google Scholar 

  28. 28.

    Fusco, T., & Tanda, M. (2009). Blind synchronization for OFDM systems in multipath channels. IEEE Transactions on Wireless Communications, 8(3), 1340–1348.

    Article  Google Scholar 

  29. 29.

    Chen, B., & Wang, H. (2004). Blind estimation of OFDM carrier frequency offset via oversampling. IEEE Transactions on Signal Processing, 52(7), 2047–2057.

    MathSciNet  Article  MATH  Google Scholar 

  30. 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).

  31. 31.

    Jeon, H.-G., Kim, K.-S., & Serpedin, E. (2011). An efficient blind deterministic frequency offset estimator for OFDM systems. IEEE Transactions on Communications, 59(4), 1133–1141.

    Article  Google Scholar 

  32. 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).

  33. 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.

  34. 34.

    Shaat, M., & Bader, F. (2012). Asymptotically optimal resource allocation in OFDM-based cognitive networks with multiple relays. IEEE Transactions on Wireless Communications, 11(3), 892–897.

    Article  Google Scholar 

  35. 35.

    Majhi, S., & Ho, T. S. (2015). Blind symbol-rate estimation and test bed implementation of linearly modulated signals. IEEE Transactions on Vehicular Technology, 64(3), 954–963.

    Article  Google Scholar 

  36. 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).

  37. 37.

    Majhi, S., Kumar, M., & Xiang, W. (2017). Implementation and measurement of blind wireless receiver for single carrier systems. IEEE Transactions on Instrumentation and Measurement, 66(8), 1965–1975.

    Article  Google Scholar 

  38. 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. 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).

  40. 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.

  41. 41.

    Hyder, C. S., Al Islam, A. B. M. A., Xiao, L., & Torng, E. (2016). Interference aware reliable cooperative cognitive networks for real-time applications. IEEE Transactions on Cognitive Communications and Networking, 2(1), 53–67.

    Article  Google Scholar 

  42. 42.

    Kay, S. (1998). Fundamentlas of statistical signal processing, volume 2: Detection theory. Englewood: Prentice-Hall.

    Google Scholar 

  43. 43.

    Goldsmith, A., Jafar, S. A., Maric, I., & Srinivasa, S. (2009). Breaking spectrum gridlock with cognitive radios: An information theoretic perspective. Proceedings of the IEEE, 97(5), 894–914.

    Article  Google Scholar 

  44. 44.

    Chang, C.-S. (1994). Stability, queue length, and delay of deterministic and stochastic queueing networks. IEEE Transactions on Automatic Control, 39(5), 913–931.

    MathSciNet  Article  MATH  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Manish Kumar.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kumar, M., Majhi, S. Joint signal detection and synchronization for OFDM based cognitive radio networks and its implementation. Wireless Netw 25, 699–712 (2019). https://doi.org/10.1007/s11276-017-1586-y

Download citation

Keywords

  • Cognitive radio network
  • Detection and estimation
  • Orthogonal frequency division multiplexing
  • Blind parameter estimation
  • Software-defined radio