DPSK Sistemler için LMS Algoritma ve ML Kriteri Temelli, Gözü Kapalı Kanal Kestiriminin ve Turbo Denkleştirmenin Birlikte Yapılması

Abstract

Sayısal haberleşme sistemlerinde, alıcıda gözü kapalı kanal kestirme ve turbo denkleştirme işlemlerinin birlikte yapılması, gözü kapalı turbo denkleştirme olarak adlandırılır. Bu çalışmada, farksal faz kaydırmalı anahtarlama (DPSK) modülasyonu kullanan sayısal haberleşme sistemleri için yeni bir gözü kapalı turbo denkleştirici geliştirilmiştir. DPSK tercih edilmesinin sebebi faz belirsizliğini çözmektir. Haberleşme kanalının katsayılarını kestirmek için LMS algoritma, toplanır beyaz Gauss gürültünün (AWGN) varyansını kestirmek için ise ML kriteri kullanılmıştır

Keywords

• yumuşak gözü kapalı kanal kestirimi, turbo denkleştirme, gözü kapalı LMS kestirimci

LMS Algorithm and ML Criterion Based, Combined Blind Channel Identification and Turbo Equalization for DPSK Systems

Abstract

In digital communication systems, combined blind channel identification and turbo equalization at the receiver is called blind turbo equalization. In this study, a new blind turbo equalizer is developed for digital communication systems which are using DPSK modulation. The reason for prefering DPSK is to remove phase ambiguity. Least-Mean Square (LMS) algorithm is used to estimate the channel coefficients and additive white Gaussian noise (AWGN) variance is estimated by using of maximum-likelihood (ML) criterion

Keywords

• soft blind channel identification, turbo equalization, blind LMS estimator

References

1. [1].Haykin, S., Moher, M., Introduction to analog & digital communications 2nd ed. John Wiley & Sons: New Jersey, 2007.
2. [2].Proakis, J.G., Salehi, M., Digital Communications, 5th ed. McGraw-Hill: New York, 2008.
3. [3].Douillard, C. et al., Iterative correction of intersymbol interference: Turbo equalization, European Transactions on Telecommunication, vol. 6, no. 5, pp. 507-511, 1995.
4. [4].Tuchler, M., Koetter, R., Singer, A.C., Turbo equalization: principles and new results, IEEE Transactions on Communication, vol. 50, no. 5, pp. 754-767, 2002.
5. [5].Tuchler, M., Koetter, R., Singer, A.C., Turbo equalization, IEEE Signal processing Magazine, vol. 21, no. 1, pp. 67-80, 2004.
6. [6].Chen, X. –M., Hoeher, P.A., Blind turbo equalization for wireless DPSK systems, in Proceedings of the 4th International. ITG Conference on Source and Channel Coding (SCC’02), pp. 371–378, 2002.
7. [7].Chen, X. –M., Hoeher, P.A., Trellis-based iterative adaptive blind sequence estimation for uncoded/coded systems with differential precoding, EURASIP Journal on Applied Signal Processing, vol. 2005, no. 6, pp. 828-843, 2005.
8. [8].Nissila, M., Pasupathy, S., Adaptive Baum-Welch algorithms for frequencyselective fading channels, in Proceedings of the IEEE International Conference on Communication (ICC’02), vol. 1, pp. 79-83, 2002.

9. [9].Kaleh, G.K., Vallet, R., Joint parameter estimation and symbol detection for linear or nonlinear unknown channels, IEEE Transactions on Communication, vol.42, no. 7, pp. 2406-2413, 1994.
10. [10].Proakis, J.G., Salehi, M., Bauch, G., Contemporary communication systems, 2nd ed. Thomson-Brooks/Cole, Canada, 2004.
11. [11]. Bahl, L., Cocke, J., Jelinek, F., Raviv, J., Optimum decoding of linear codes for minimizing symbol error rate, IEEE Transactions Information on Theory, vol. 20, no. 2, pp. 284-287, 1974.