More Web Proxy on the site http://driver.im/

research-article

Public Access

GPGPU Based Parallel Implementation of Spectral Correlation Density Function

Authors:

Scott Marshall,

Garrett Vanhoy,

Bo RyuAuthors Info & Claims

Journal of Signal Processing Systems, Volume 92, Issue 1

Pages 71 - 93

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

Published: 01 January 2020 Publication History

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.

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 AR, and Klautau A An efficient fpga ip core for automatic modulation classification IEEE Embedded Systems Letters 2013 5 3 42-45

[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 MA, Lopez-Vallejo M, and Yeste-ojeda OA Real-time low-complexity automatic modulation classifier for pulsed radar signals IEEE Transactions on Aerospace and Electronic Systems 2015 51 1 108-126

[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, and Wang X-J Improved method of spectral correlation density and its applications in fault diagnosis Journal of University of Science and Technology Beijing 2013 35 5 674-681

[13]

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

[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 On the Likelihood-based Approach to Modulation Classification IEEE Transactions on Wireless Communications 2009 8 12 5884-5892

[18]

Dobre OA, Abdi A, Barness Y, and Su W A survey of automatic modulation classification techniques : Classical approaches and new trends IET Communications 2007 1 2 137-156

[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, and Xiao X-C 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 2005 27 5 803-806

[21]

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

[22]

Gardner WA The Spectral Correlation Theory of Cyclostationary Time-series Signal Process 1986 11 1 13-36

[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.

Cited By

Li CLi XLou BJin CBoland DLeong P(2023)Fixed-point FPGA Implementation of the FFT Accumulation Method for Real-time Cyclostationary AnalysisACM Transactions on Reconfigurable Technology and Systems10.1145/356742916:3(1-28)Online publication date: 22-Jun-2023
https://dl.acm.org/doi/10.1145/3567429
Li XMaskell DLi CLeong PBoland D(2022)A Scalable Systolic Accelerator for Estimation of the Spectral Correlation Density Function and Its FPGA ImplementationACM Transactions on Reconfigurable Technology and Systems10.1145/354618116:1(1-24)Online publication date: 22-Dec-2022
https://dl.acm.org/doi/10.1145/3546181

Index Terms

GPGPU Based Parallel Implementation of Spectral Correlation Density Function
1. Computing methodologies
  1. Parallel computing methodologies
    1. Parallel programming languages
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language types
        Parallel programming languages

Index terms have been assigned to the content through auto-classification.

Recommendations

From GPGPU to Many-Core: Nvidia Fermi and Intel Many Integrated Core Architecture

Comparing the architectures and performance levels of an Nvidia Fermi accelerator with an Intel MIC Architecture coprocessor demonstrates the benefit of the coprocessor for bringing highly parallel applications into, or even beyond, GPGPU performance ...
On the Efficacy of a Fused CPU+GPU Processor (or APU) for Parallel Computing
SAAHPC '11: Proceedings of the 2011 Symposium on Application Accelerators in High-Performance Computing

The graphics processing unit (GPU) has made significant strides as an accelerator in parallel computing. However, because the GPU has resided out on PCIe as a discrete device, the performance of GPU applications can be bottlenecked by data transfers ...
A unified optimizing compiler framework for different GPGPU architectures

This article presents a novel optimizing compiler for general purpose computation on graphics processing units (GPGPU). It addresses two major challenges of developing high performance GPGPU programs: effective utilization of GPU memory hierarchy and ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Journal of Signal Processing Systems

Journal of Signal Processing Systems Volume 92, Issue 1

Jan 2020

129 pages

ISSN:1939-8018

EISSN:1939-8115

Issue’s Table of Contents

© Springer Science+Business Media, LLC, part of Springer Nature 2019.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 January 2020

Accepted: 08 March 2019

Revision received: 15 February 2019

Received: 23 November 2018

Author Tags

Qualifiers

Research-article

Funding Sources

Office of Naval Research

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 19 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Li CLi XLou BJin CBoland DLeong P(2023)Fixed-point FPGA Implementation of the FFT Accumulation Method for Real-time Cyclostationary AnalysisACM Transactions on Reconfigurable Technology and Systems10.1145/356742916:3(1-28)Online publication date: 22-Jun-2023
https://dl.acm.org/doi/10.1145/3567429
Li XMaskell DLi CLeong PBoland D(2022)A Scalable Systolic Accelerator for Estimation of the Spectral Correlation Density Function and Its FPGA ImplementationACM Transactions on Reconfigurable Technology and Systems10.1145/354618116:1(1-24)Online publication date: 22-Dec-2022
https://dl.acm.org/doi/10.1145/3546181

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents