پیوندهای مفید
آمار
| تعداد نشریات | 7 |
| تعداد شماره‌ها | 399 |
| تعداد مقالات | 5,389 |
| تعداد مشاهده مقاله | 5,288,194 |
| تعداد دریافت فایل اصل مقاله | 4,882,936 |
Improved Channel Estimation for DVB-T2 Systems by Utilizing Side Information on OFDM Sparse Channel Estimation | ||
| AUT Journal of Electrical Engineering | ||
| مقاله 3، دوره 50، شماره 2، اسفند 2018، صفحه 129-134 اصل مقاله (902.33 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/eej.2018.13944.5199 | ||
| نویسندگان | ||
| S. Ghazi-Maghrebi* 1؛ S.H. Hashemi-Rafsanjani2 | ||
| 1Collage of Electrical Engineering, Yadegar-e Imam Khomeini (RAH) Shahr-e Rey Branch, Tehran, Iran | ||
| 2Digital Communication, ICT Research Center, Tehran, Iran | ||
| چکیده | ||
| The second generation of digital video broadcasting (DVB-T2) standard utilizes orthogonal frequency division multiplexing (OFDM) system to reduce and to compensate the channel effects by utilizing its estimation. Since wireless channels are inherently sparse, it is possible to utilize sparse representation (SR) methods to estimate the channel. In addition to sparsity feature of the channel, there is usually some additional information, known as side information. The side information, in general application, is not used in ordinary sparse channel estimation methods. However, utilizing the side information may help improve the channel estimation. In this paper, we utilize side information to estimate sparse channel of an OFDM system. Also, for more verification of the proposed method in this paper, we have shown the impact of side information in the estimation procedure for an applied system such as DVB-T2 system. Simulation results, in this research, show that utilizing side information not only increases the performance of the DVB-T2 system, but also releases a portion of resources of the system such as estimation-pilots. It is obvious that these resources can be used for increasing data rate. | ||
| کلیدواژه‌ها | ||
| DVB-T2 System؛ Sparse Channel Estimation؛ OFDM Modulation؛ Side Information | ||
| مراجع | ||
|
[1] E.S. EN, 302 304: Digital Video Broadcasting (DVB), Transmission system for handheld terminals (DVB-H), ETSI, (2004). [2] J.S. Proakis, M. Nov, 6, 2007” Digital Communications, in, McGraw-Hill Education.
[3] A. Goldsmith, Wireless communications, Cambridge university press, 2005.
[4] T. ETSI, 102 428: Digital Audio Broadcasting (DAB), DMB video service, (2009).
[5] O. Edfors, M. Sandell, J.-J. Van de Beek, S.K. Wilson, P.O. Borjesson, OFDM channel estimation by singular value decomposition, IEEE Transactions on communications, 46(7) (1998) 931-939.
[6] S. Coleri, M. Ergen, A. Puri, A. Bahai, Channel estimation techniques based on pilot arrangement in OFDM systems, IEEE Transactions on broadcasting, 48(3) (2002) 223-229.
[7] H. Minn, V.K. Bhargava, An investigation into time-domain approach for OFDM channel estimation, IEEE Transactions on broadcasting, 46(4) (2000) 240-248.
[8] J.-J. Van De Beek, O. Edfors, M. Sandell, S.K. Wilson, P.O. Borjesson, On channel estimation in OFDM systems, in: Vehicular Technology Conference, 1995 IEEE 45th, IEEE, 1995, pp. 815-819.
[9] Y. Li, L.J. Cimini, N.R. Sollenberger, Robust channel estimation for OFDM systems with rapid dispersive fading channels, IEEE Transactions on communications, 46(7) (1998) 902-915.
[10] A. Petropulu, R. Zhang, R. Lin, Blind OFDM channel estimation through simple linear precoding, IEEE Transactions on Wireless Communications, 3(2) (2004) 647-655.
[11] J.K. Tugnait, L. Tong, Single-user channel estimation and equalization, IEEE Signal Processing Magazine, 17(3) (2000) 17-28.
[12] P. Hoeher, S. Kaiser, P. Robertson, Two-dimensional pilot-symbol-aided channel estimation by Wiener filtering, in: Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on, IEEE, 1997, pp. 1845-1848.
[13] P. Hoeher, S. Kaiser, P. Robertson, Pilot-symbol-aided channel estimation in time and frequency, Multi-carrier spread-spectrum, (1997) 169-178.
[14] M. Sandell, Design and analysis of estimators for multicarrier modulation and ultrasonic imaging, Luleå tekniska universitet, 1996.
[15] M. Necker, F. Sanzi, J. Speidel, An adaptive wiener-filter for improved channel estimation in mobile OFDM-systems, in: Proc. IEEE Intern. Symp. on Signal Proc. and Inf. Tech.(ISSPIT), 2001, pp. 213-216.
[16] X. Hou, S. Li, D. Liu, C. Yin, G. Yue, On two-dimensional adaptive channel estimation in OFDM systems, in: Vehicular Technology Conference, 2004. VTC2004-Fall. 2004 IEEE 60th, IEEE, 2004, pp. 498-502
[17] S. Haykin, B. Widrow, Least-mean-square adaptive filters, John Wiley & Sons, 2003.
[18] S. Haykin, B. Widrow, Least-mean-square adaptive filters, John Wiley & Sons, 2003.
[19] S. Haykin, B. Widrow, Least-mean-square adaptive filters, John Wiley & Sons, 2003.
[20] P. Pakrooh, A. Amini, F. Marvasti, OFDM pilot allocation for sparse channel estimation, EURASIP Journal on Advances in Signal Processing, 2012(1) (2012) 59.
[21] C. Qi, L. Wu, A study of deterministic pilot allocation for sparse channel estimation in OFDM systems, IEEE communications letters, 16(5) (2012) 742-744.
[22] F. Wan, W.-P. Zhu, M. Swamy, Semi-blind most significant tap detection for sparse channel estimation of OFDM systems, IEEE Transactions on Circuits and Systems I: Regular Papers, 57(3) (2010) 703-713.
[23] M. Elad, From Exact to Approximate Solutions, in: Sparse and Redundant Representations, Springer, 2010, pp. 79-109. [24] E.J. Candes, J.K. Romberg, T. Tao, Stable signal recovery from incomplete and inaccurate measurements, Communications on pure and applied mathematics, 59(8) (2006) 1207-1223. [25] S.G. Mallat, Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on signal processing, 41(12) (1993) 3397-3415
[26] J.A. Tropp, Greed is good: Algorithmic results for sparse approximation, IEEE Transactions on Information theory, 50(10) (2004) 2231-2242.
[27] G.H. Mohimani, M. Babaie-Zadeh, C. Jutten, Fast sparse representation based on smoothed ℓ0 norm, in: International Conference on Independent Component Analysis and Signal Separation, Springer, 2007, pp. 389-396.
[28] M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer New York, 2011.
[29] Y. Nan, L. Zhang, X. Sun, An efficient downlink channel estimation approach for TDD massive MIMO systems, in: Vehicular Technology Conference (VTC Spring), 2016 IEEE 83rd, IEEE, 2016, pp. 1-5.
[30] I.F. Gorodnitsky, B.D. Rao, Sparse signal reconstruction from limited data using FOCUSS: A re-weighted minimum norm algorithm, IEEE Transactions on signal processing, 45(3) (1997) 600-616.
[31] S.S. Chen, D.L. Donoho, M.A. Saunders, Atomic decomposition by basis pursuit, SIAM review, 43(1) (2001) 129-159.
[32] J.O. Berger, Statistical Decision Theory and Bayesian Analysis, Springer, 1985.
[33] M. Unser, P.D. Tafti, An Introduction to Sparse Stochastic Processes, Cambridge University Press, 2014.
[34] A. Papoulis, Probability & Statistics, Prentice Hall, 1990.
[35] R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B (Methodological), (1996) 267-288.
[36] M. Babaie-Zadeh, B. Mehrdad, G.B. Giannakis, Weighted sparse signal decomposition, in: Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on, IEEE, 2012, pp. 3425-3428. | ||
|
آمار تعداد مشاهده مقاله: 1,130 تعداد دریافت فایل اصل مقاله: 780 |
||