An Automatic Modulation Classifier using a Frequency Discriminator for Intelligent Software Defined Radio

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1 C IEEJ Transactions on Electronics, Information and Systems Vol.133 No.5 pp DOI: /ieejeiss a) An Automatic Modulation Classifier using a Frequency Discriminator for Intelligent Software Defined Radio Kazuyuki Morioka a), Non-member, David Asano, Non-member In this paper, we proposed a simple frequency discriminator based method to automatically identify PSK and M-ary FSK modulation schemes. This method is simpler than other methods based on statistical moments, neural networks and wavelet transforms. Also, this method can demodulate M-ary FSK signals using the classifier s output, resulting in a simple receiver structure. Results from a digital implementation are presented to show the validity of the proposed method. Keywords: automatic modulation classification, frequency discriminator, intelligent software defined radio 1. CPU FPGA (SDR:Software Defined Radio) a) Correspondence to: Kazuyuki Morioka. Kazuyuki. Morioka@gmail.com Department of Mathematics and System Development, The Interdisciplinary Graduate School of Science and Technology, Shinshu University , Wakasato, Nagano , Japan Department of Computer Science & Engineering, Shinshu University , Wakasato, Nagano , Japan 3 (1). (2) (3) (4) (5) (6) (7) Dobre (1) FSK c 2013 The Institute of Electrical Engineers of Japan. 910

2 Beidas (8) FSK FSK (9) (10) (11) 2 FSK Fig. 1 PSK FSK AWGN T idt T idt N idt T idt = N idt T sym T sym 1 ( PSK M FSK ) r(t), 0 t T idt r(t) = s(t) + n(t), (1) n(t) s(t) s(t) = A cos[2π f c t + φ(t)], (2) A, f c, φ(t) Fig. 1. Intelligent Software Defined Radio M PSK φ(t) φ(t) = a k u(t kt sym )π, (3) k a k { 2 (m 1), M m = 1, 2,...,M}, a k k u(t) 1 0 t T sym u(t) = (4) 0 t < 0, t > T sym. M FSK φ(t) φ(t) = b k g(t kt sym )Δ f, (5) k b k {±m, m = 1, 2,..., M 2 }, Δ f b k k g(t) t/t sym 0 t T sym g(t) =. (6) 0 t < 0, t > T sym P c = 1 N mod P c (m). (7) N mod m=1 P c (m) m N mod P c P e = 1 P c. (8) (11) FSK Fig. 2 φ(t) (3), (5) φ(t) φ(t) PSK 911 IEEJ Trans. EIS, Vol.133, No.5, 2013

3 Fig. 2. An Automatic Modulation Classifier using a Frequency Discriminator Fig. 4. Average value of integrator output, V idt Fig. 3. (a) 2-PSK (b) M-FSK Output of Frequency Discriminator Fig. 3 (a) φ(t) PSK FSK Fig. 3 (b) Fig. 3 (b) FSK FSK T idt V idt = Tidt 0 V dis (t) dt. (9) V dis (t) PSK FSK V idt FSK V idt PSK V idt Fig. 4 N idt 10 T idt (1,000,000 ) PSK FSK threshold0 FSK Fig. 4 threshold1threshold2 40 db N idt (Fig. 4). SNR SNR V idt threshold0 2-PSK V idt threshold0threshold1 2-FSK V idt threshold1threshold2 4-FSK V idt threshold2 8-FSK Fig. 5 2-PSK, 2-FSK, 4-FSK, 8-FSK 32 (11) FSK Fig. 4 T idt 8-FSK 8-FSK 8-FSK IEEJ Trans. EIS, Vol.133, No.5, 2013

4 threshold0psk FSK FSK T sym T sym V sym (i) = (i+1)tsym it sym V dis (t) dt, (10) i = 0, 1,..., N idt 1. Fig. 3 8-FSK level 3level FSK level 3level 4 1 level FSK level 1 2-FSK 40 db V sym Fig. 6 2-PSK, 2-FSK, 4-FSK, 8-FSK 33 FSK Fig. 6 8-FSK Fig. 3 level3level4 1 8-FSK 2-FSK, 4-FSK N idt 10 level3 level FSK 2-FSK, 4-FSK N idt = 10 8-FSK N idt = 10 4-FSK N idt = 20 8-FSK FSK FSK 4N idt 8 N idt 4-FSK 2N idt 4 N idt 8-FSK, 4-FSK 1 2 N idt N idt PSK, 2-FSK, 4-FSK, 8-FSK N idt Fig. 7 N idt = 10 Fig. 8 N idt = N idt Fig. 7 2-PSK 2-FSK 25 db Fig. 5. Identification algorithm (extension of Asano (11) ) Fig. 6. Proposed identification algorithm Fig. 7. Probability of Decision Error (N idt = 10) 913 IEEJ Trans. EIS, Vol.133, No.5, 2013

5 Fig. 8. Probability of Symbol Error (N idt = 10) Fig. 11. Probability of Decision Error (N idt = 20) Fig. 9. Probability of Decision Error (N idt = 15) Fig. 12. Probability of Symbol Error (N idt = 20) Fig. 10. Probability of Symbol Error (N idt = 15) Fig. 13. Average Probability of Identification P c 1 PSK FSK 4-FSK 8-FSK SNR 35 db FSK 8-FSK N idt = SNR 35 db 4-FSK 8-FSK Fig. 9 N idt = 15 Fig. 10 N idt = 15 Fig. 11 N idt = 20 Fig. 12 N idt = 20 SNR 35 db N idt = 20 SNR 35 db 10 6 Fig. 13 (7) P c (11) Fig IEEJ Trans. EIS, Vol.133, No.5, 2013

6 N idt (6) (9) (10) 50, 100, 200 N idt = M FSK PSK FSK PSK 1 O. Dobre, A. Abdi, Y. Bar-Ness, and W. Su: A Survey of Automatic Modulation Classification Techniques: Classical Approaches and New Trends, IET Communications, Vol.1, No.2, pp (2007) 2 J. Sills: Maximum-likelihood modulation classification for PSK/QAM, IEEE Military Communications Conference Proceedings, Vol.1 (1999) 3 W. Wei and J. Mendel: A new maximum-likelihood method for modulation classification, Conference Record of the Twenty-Ninth Asilomar Conference on Signals, Systems and Computers, Vol.2 (1995) 4 S. Soliman and S. Hsue: Signal classification using statistical moments, IEEE Trans. on Communications, Vol.40, No.5, pp (1992) 5 A. Nandi and E. Azzouz: Algorithms for automatic modulation recognition of communication signals, IEEE Trans. on Communications, Vol.46, No.4, pp (1998) 6 L. Hong and K.C. Ho: Identification of digital modulation types using the wavelettransform, IEEE Military Communications Conference Proceedings, Vol.1, pp (1999) 7 K. Ho, W. Prokopiw, and Y. Chan: Modulation identification of digital signals by the wavelet transform, IEE Proceedings - Radar, Sonar and Navigation, Vol.147, No.4, pp (2000) 8 B. Beidas and C. Weber: Asynchronous classification of MFSK signals using the higher order correlation domain, IEEE Trans. on Communications, Vol.46, No.4, pp (1998) 9 A.V. Rosti and V. Koivunen: Classification of MFSK modulated signals using the mean of complex envelope, Proc. EUSIPCO, Vol.1, pp (2000) 10 P. Prakasam and M. Madheswaran: M-ary Shift Keying Modulation Scheme Identification Algorithm Using Wavelet Transform and Higher Order Statistical Moment, Journal of Applied Science, Vol.8, No.1 (2008) 11 D. Asano and M. Ohara: Automatic Modulation Identification Using a Frequency Discriminator, IEICE Trans. on Communications,Vol.E91-B,No.2, pp (2008) IEEE, 1994 (Ph.D.) (STA ) IEEE Senior Member, 915 IEEJ Trans. EIS, Vol.133, No.5, 2013