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GPGPU Based Parallel Implementation of Spectral Correlation Density Function

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Abstract

In this study, the parallelization of a critical statistical feature of communication signals called the spectral correlation density (SCD) is investigated. The SCD is used for synchronization in OFDM-based systems such as LTE and Wi-Fi, but is also proposed for use in next-generation wireless systems where accurate signal classification is needed even under poor channel conditions. By leveraging cyclostationary theory and classification results, a method for reducing the computational complexity of estimating the SCD for classification purposes by 75% or more using the Quarter SCD (QSCD) is proposed. We parallelize the SCD and QSCD implementations by targeting general purpose graphics processing unit (GPU) through architecture specific optimization strategies. We present experimental evaluations on identifying the parallelization configuration for maximizing the efficiency of the program architecture in utilizing the threading power of the GPU architecture. We show that algorithmic and architecture specific optimization strategies result with improving the throughput of the state of the art GPU based SCD implementation from 120 signals/second to 3300 signals/second.

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References

  1. Dobre, O.A., & Hameed, F. (2007). On Performance Bounds for Joint Parameter Estimation and Modulation Classification. 2007 IEEE Sarnoff Symposium (pp. 1–5). [Online]. Available: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4567394.

  2. Su, W., Monmouth, F., Kosinski, J.A., Yu, M. (2006). Dual-use of modulation recognition techniques for digital communication signals. IEEE Long Island Systems. Applications and Technology (LISAT) Conference, (pp. 1–6).

  3. Zhang, J., Wang, X., Yang, X. (2016). A method of constellation blind detection for spectrum efficiency enhancement. 2016 18th International Conference on Advanced Communication Technology (ICACT) (pp. 148–152). Pyeongchang.

  4. Seidel, S., & Breinig, R. (2005). Autonomous dynamic spectrum access system behavior and performance. First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN 2005) (pp. 180–183). Baltimore.

  5. Tsakmalis, A., Chatzinotas, S., Ottersten, B. (2014). Modulation and coding classification for adaptive power control in 5g cognitive communications. 2014 IEEE 15th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) (pp. 234–238). Toronto.

  6. Cardoso, C., Castro, A.R., Klautau, A. (2013). An efficient fpga ip core for automatic modulation classification. IEEE Embedded Systems Letters, 5(3), 42–45.

    Article  Google Scholar 

  7. Grajal, J., Yeste-Ojeda, O., Sanchez, M.A., Garrido, M., Lopez-Vallejo, M. (2011). Real time fpga implementation of an automatic modulation classifier for electronic warfare applications. Signal Processing Conference, 2011 19th European (pp. 1514–1518). Barcelona.

  8. Iglesias, V., Grajal, J., Royer, P., Sanchez, M.A., Lopez-Vallejo, M., Yeste-ojeda, O.A. (2015). Real-time low-complexity automatic modulation classifier for pulsed radar signals. IEEE Transactions on Aerospace and Electronic Systems, 51(1), 108–126.

    Article  Google Scholar 

  9. Sorato, E., Netto, R., Michel, P., Güntzel, J.L., Castro, A.R., Klautau, A. (2013). Vlsi architectures for digital modulation classification using support vector machines. 2013 IEEE Fourth Latin American Symposium on Circuits and Systems (LASCAS) (pp. 1–4). Cusco.

  10. Lee, C.-H., Chang, C.-J., Chen, S.-J. (2012). Parallelization of spectrum sensing algorithms using graphic processing units. Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSQRWC 2012) (pp. 35–39). New Taipei City.

  11. Bidyanta, N., Vanhoy, G., Hirzallah, M., Akoglu, A., Ryu, B., Bose, T. (2015). Gpu and fpga based architecture design for real-time signal classification. Proceedings of the 2015 Wireless Innovation Forum Conference on Wireless Communications Technologies and Software Defined Radio (WInnComm’15) (pp. 70–79). San Diego.

  12. Lu, Q.-Q., Li, M., Wang, X.-J. (2013). Improved method of spectral correlation density and its applications in fault diagnosis. Journal of University of Science and Technology Beijing, 35(5), 674–681.

    Google Scholar 

  13. Yoo, D.-S., Lim, J., Kang, M.-H. (2014). Atsc digital television signal detection with spectral correlation density. Journal of Communications and Networks, 16(6), 600–612.

    Article  Google Scholar 

  14. Schnur, S.R. (2009). Identification and classification of ofdm based signals using preamble correlation and cyclostationary feature extraction. DTIC Document, Tech. Rep.

  15. Simic, D., & Simic, J. (1999). The strip spectral correlation algorithm for spectral correlation estimation of digitally modulated signals. In 1999 4th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services (vol. 1, pp. 277–280).

  16. Marshall, S., Vanhoy, G., Akoglu, A., Bose, T., Ryu, B. (2018). Gpu based quarter spectral correlation density function. Conference on Design and Architectures for Signal and Image Processing (DASIP) (pp. 88–93). Porto.

  17. Hameed, F. (2009). On the Likelihood-based Approach to Modulation Classification. IEEE Transactions on Wireless Communications, 8(12), 5884–5892.

    Article  Google Scholar 

  18. Dobre, O.A., Abdi, A., Barness, Y., Su, W. (2007). A survey of automatic modulation classification techniques : Classical approaches and new trends. IET Communications, 1(2), 137–156.

    Article  Google Scholar 

  19. Fehske, A., Gaeddert, J., Reed, J.H. (2005). A New Approach to Signal Classification using Spectral Correlation and Neural Networks. 2005 1st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, DySPAN 2005 (pp. 144–150).

  20. Zhang, Z.-B., Li, L.-P., Xiao, X.-C. (2005). Detection and chip rate estimation of mpsk signals based on cyclic spectral density. Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 27(5), 803–806.

    Google Scholar 

  21. Zhu, L., Cheng, H. -W., Wu, L. -N. (2009). Identification of digital modulation signals based on cyclic spectral density and statistical parameters. Journal of Applied Sciences / Yingyong Kexue Xuebao, 27(2), 137–143.

    Google Scholar 

  22. Gardner, W.A. (1986). The Spectral Correlation Theory of Cyclostationary Time-series. Signal Process, 11 (1), 13–36. [Online]. Available: https://doi.org/10.1016/0165-1684(86)90092-7.

    Article  Google Scholar 

  23. Ge, F., & Bostian, C.W. (2008). A parallel computing based spectrum sensing approach for signal detection under conditions of low snr and rayleigh multipath fading. 2008. DySPAN 2008. 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks (pp. 1–10). Chicago.

  24. Upperman, G.J., Upperman, T.L.O., Fouts, D.J., Pace, P.E. (2008). Efficient time-frequency and bi-frequency signal processing on a reconfigurable computer. 2008 42nd Asilomar Conference on Signals, Systems and Computers (pp. 176–180). Pacific Grove.

  25. Wang, S., Zhang, B., Liu, H., Xie, L. (2010). Parallelized cyclostationary feature detection on a software defined radio processor. 2010 International Symposium on Signals, Systems and Electronics (pp. 1–4). Nanjing.

  26. Li, H., Yu, D., Kumar, A., Tu, Y.C. (2014). Performance modeling in cuda streams — a means for high-throughput data processing. 2014 IEEE International Conference on Big Data (pp. 301–310). Washington.

  27. Luley, R.S., & Qiu, Q. (2016). Effective utilization of cuda hyper-q for improved power and performance efficiency. 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) (pp. 1160–1169). Chicago.

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Acknowledgments

Research reported in this publication was supported in part by Office of the Naval Research under the contract N00014-15-C-5173. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Office of Naval Research.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Arizona, 1230 E. Speedway Blvd., Tucson, AZ, 85737, USA

    Scott Marshall, Ali Akoglu & Tamal Bose

  2. EpiSys Science, Inc, 13025 Danielson St, Suite 106, Poway, CA, 92064, USA

    Garrett Vanhoy & Bo Ryu

Authors
  1. Scott Marshall
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  2. Garrett Vanhoy
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  3. Ali Akoglu
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  4. Tamal Bose
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  5. Bo Ryu
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Correspondence to Scott Marshall.

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Marshall, S., Vanhoy, G., Akoglu, A. et al. GPGPU Based Parallel Implementation of Spectral Correlation Density Function. J Sign Process Syst 92, 71–93 (2020). https://doi.org/10.1007/s11265-019-01448-7

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  • DOI: https://doi.org/10.1007/s11265-019-01448-7

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