GPGPU Based Parallel Implementation of Spectral Correlation Density Function

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.

[1]  J. R. Simic,et al.  The strip spectral correlation algorithm for spectral correlation estimation of digitally modulated signals , 1999, 4th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services. TELSIKS'99 (Cat. No.99EX365).

[2]  Aldebaro Klautau,et al.  An Efficient FPGA IP Core for Automatic Modulation Classification , 2013, IEEE Embedded Systems Letters.

[3]  O. Dobre,et al.  On performance bounds for joint parameter estimation and modulation classification , 2007, 2007 IEEE Sarnoff Symposium.

[4]  Jeffrey H. Reed,et al.  A new approach to signal classification using spectral correlation and neural networks , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[5]  W. Gardner.  The spectral correlation theory of cyclostationary time-series , 1986 .

[6]  Qinru Qiu,et al.  Effective Utilization of CUDA Hyper-Q for Improved Power and Performance Efficiency , 2016, 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW).

[7]  Jesús Grajal,et al.  Real-time low-complexity automatic modulation classifier for pulsed radar signals , 2015, IEEE Transactions on Aerospace and Electronic Systems.

[8]  Symeon Chatzinotas,et al.  Modulation and Coding Classification for Adaptive Power Control in 5G Cognitive Communications , 2014, 2014 IEEE 15th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[9]  Xin Wang,et al.  A method of constellation blind detection for spectrum efficiency enhancement , 2016, 2016 18th International Conference on Advanced Communication Technology (ICACT).

[10]  Xiao Xian-ci.  Detection and chip rate estimation of MPSK signals based on cyclic spectral density , 2005 .

[11]  Hao Li,et al.  Performance modeling in CUDA streams — A means for high-throughput data processing , 2014, 2014 IEEE International Conference on Big Data (Big Data).

[12]  Do-Sik Yoo,et al.  ATSC digital television signal detection with spectral correlation density , 2014, Journal of Communications and Networks.

[13]  P.E. Pace,et al.  Efficient time-frequency and bi-frequency signal processing on a reconfigurable computer , 2008, 2008 42nd Asilomar Conference on Signals, Systems and Computers.

[14]  Feng Ge,et al.  A Parallel Computing Based Spectrum Sensing Approach for Signal Detection under Conditions of Low SNR and Rayleigh Multipath Fading , 2008, 2008 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[15]  R. Breinig,et al.  Autonomous dynamic spectrum access system behavior and performance , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[16]  Ali Akoglu,et al.  GPU Based Quarter Spectral Correlation Density Function , 2018, 2018 Conference on Design and Architectures for Signal and Image Processing (DASIP).

[17]  Octavia A. Dobre,et al.  On the likelihood-based approach to modulation classification , 2009, IEEE Transactions on Wireless Communications.

[18]  Ali Abdi,et al.  Survey of automatic modulation classification techniques: classical approaches and new trends , 2007, IET Commun..

[19]  Sao-Jie Chen,et al.  Parallelization of spectrum sensing algorithms using graphic processing units , 2012, CSQRWC 2012.

[20]  Jesús Grajal,et al.  Real time FPGA implementation of an automatic modulation classifier for electronic warfare applications , 2011, 2011 19th European Signal Processing Conference.

[21]  Ming Yu,et al.  DUAL-USE OF MODULATION RECOGNITION TECHNIQUES FOR DIGITAL COMMUNICATION SIGNALS , 2006, 2006 IEEE Long Island Systems, Applications and Technology Conference.

[22]  Hengzhu Liu,et al.  Parallelized cyclostationary feature detection on a software defined radio processor , 2010, 2010 International Symposium on Signals, Systems and Electronics.

[23]  Steven R. Schnur.  Identification and classification of OFDM based signals using preamble correlation and cyclostationary feature extraction , 2009 .

[24]  J. L. Guntzel,et al.  VLSI architectures for Digital Modulation Classification using Support Vector Machines , 2013, 2013 IEEE 4th Latin American Symposium on Circuits and Systems (LASCAS).

[25]  Wu Le-nan,et al.  Identification of Digital Modulation Signals Based on Cyclic Spectral Density and Statistical Parameters , 2009 .

[26]  Li Min,et al.  Improved method of spectral correlation density and its applications in fault diagnosis , 2013 .