ABSTRACT
This paper documents an initial effort in detecting frequency-hopped (FH) signals in a multiple access (MA) environment from a machine learning perspective. Although the off-line training might require very intensive computing power, the extracted information does has a concise representation, which then enables to detect a signal using only simple and low-power operations.Frequency-hopping is an attractive alternative multiple access technique for direct sequence based code division multiple access (CDMA) schemes. Other than the communication channel statistic, the capacity of an FHMA system is determined by two major related design concerns: waveform design and receiver structure. Given the FH waveform, one still has difficulty in designing an FHMA ML receiver due to the facts that our knowledge about the channel statistics is often incomplete and even if it is complete the associated conditional probability density function (pdf) does not render a closed-form expression.Regarding the FHMA/MFSK waveform as a time-frequency pattern, we convert the multiuser detection problem into a pattern classification problem and then resolve to the Support Vector Machine (SVM) approach for solving the resulting multiple-class classification problem. By using an appropriate kernel function,t he SVM essentially transforms the received signal space into a higher dimension feature space. We propose a SVM-based FHMA/MFSK receiver by applying the Sequential Minimization Optimization (SMO) and Directed Acyclic Graph (DAG) algorithms to find the optimal separating hyperplanes in the feature space. Simulation results indicate that our design does yield robust and satisfactory performance.
- S. Chen, A. K. Samingan, L. Hanzo, "Support vector machine multiuser receiver for DS-CDMA signals in multipath channels," IEEE Trans. Neural Networks, vol. 12, no. 3, pp. 604--611, May. 2001. Google ScholarDigital Library
- G. R. Cooper, R. W. Nettleton, "A spread spectrum technique for high capacity mobile communication," IEEE Trans. Veh. Technol., vol. VT-27, pp. 264--275, NOV. 1978.Google Scholar
- N. Cristianini, J. Shawe-Taylor An Introduction to Support Vector Machines, Cambridge University Press. Google ScholarDigital Library
- G. Einarsson, "A ddress assignment for a time-frequency-coded, spread-spectrum system," Bell Syst. Tech. J., vol. 59, no. 7, pp. 1241--1255, Sep. 1980.Google ScholarCross Ref
- F. Pérez-Cruz, P. Alarcón-Diana, A. Navia-Vázquez and A. Artés-Rodríguez, "SVC-Based Equalizer for Burst TDMA Transmissions," Signal Processing, 81(8), pp.1681--1693. August 2001.Google ScholarCross Ref
- C. P. Hung and Y. T. Su, Diversity combining considerations for incoherent frequency hopping multiple access systems, it IEEE J. Select. Areas Commun., vol. 13, pp. 333V344, Feb. 1995. Google ScholarDigital Library
- X. Gong, A. Kuh, "Support vector machine for multiuser detection in CDMA communications," 33rd Asilomar Conference on Signals, Systems and Computers, Monetary CA, vol. 1, pp.685--689, Nov. 1999.Google Scholar
- D. J. Goodman, P. S. Henry, and V. K. Prabhu, "Frequency-hopped multilevel FSK for mobile radio," Bell Syst. Tech. J., vol. 59, no. 7, pp. 1257--1275, Sep. 1980Google ScholarCross Ref
- G.S. Kimeldorf and G. Wahba, "Some results in tchebycheffian spline functions," J. Math. Anal. Applicat., vol. 33, no. 1, pp. 82V95, 1971.Google ScholarCross Ref
- J. C. Platt, N. Cristianini, J. Shawe-Taylor, "Large Margin DAGs for Multiclass Classification," in Proceedings of Neural Information Processing Systems, NIPS'99, pp. 547--553. MIT Press, 2000Google Scholar
- J. C. Platt, "Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines," in Microsoft Research, Technical Report MSR-TR-98-14, Apr. 1998Google Scholar
- B. Schölkopf, A. Smola Learning with Kernel, MIT Press, Cambridge, MA, 2002.Google Scholar
- G. Solomon, "Optimal frequency hopping sequences for multiple access," in Proc. 1973 Symp. Spread Spectrum Commun., San Diego, CA, Mar 13--16, 1973, pp. 33--35.Google Scholar
- Y. Su, Y. Shen, and C. Hsiao, "On the detection of a class of fast frequency-hopped multiple access signals," IEEE J. Select. Areas Commun., vol. 19, no. 11, pp. 2151--2164, Nov. 2001. Google ScholarDigital Library
- U. Timor, "Improved decoding scheme for frequency-hopped multilevel FSK system," Bell Syst. Tech. J., vol. 59, no. 10, pp. 1839--1855, Dec. 1980.Google ScholarCross Ref
- U. Timor, "Multistage decoding of frequency-hopped FSK system," Bell Syst. Tech. J., vol. 60, no. 4, pp. 471--483, Apr. 1981.Google ScholarCross Ref
- A. J. Viterbi, "A processing satellite transponder for multiple access by low rate mobile users," Proc. Digital Satellite Commun. Conf. (Montreal, P.Q., Canada), pp. 166--174, Oct. 1978.Google Scholar
Index Terms
- Detection of FHMA/MFSK signals based on SVM techniques
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