Search results

Filters

  • Journals
  • Authors
  • Keywords
  • Date
  • Type

Search results

Number of results: 1
items per page: 25 50 75
Sort by:
Download PDF Download RIS Download Bibtex

Abstract

Due to the multifold growth in demands of multimedia services and mobile data, the request for increased channel capacity in mobile and wireless communication has been quickly increasing. Developing a wireless system with more spectral efficiency under varying channel condition is a key challenge to provide more bit rates with limited spectrum. Multiple Input Multiple Output (MIMO) system with Orthogonal Frequency Division Multiplexing (OFDM) gives higher gain by using the direct and the reflected signals, thus facilitating the transmission at high data rate. An integration of Spatial Modulation (SM) with OFDM (SM OFDM) is a newly evolved transmission technique and has been suggested as a replacement for MIMO -OFDM transmission. In practical scenarios, channel estimation is significant for detecting transmitted data coherently. This paper proposes pilot based, Minimum Mean Square Error (MMSE) channel estimation for the SM OFDM communication system. We have focused on analyzing Symbol Error Rate (SER) and Mean Square error (MSE) under Rayleigh channel employing International Telecommunication Union (ITU) specified Vehicular model of Pilot based MMSE channel estimator using windowed Discrete Fourier Transform (DFT) and MMSE weighting function. Simulation output shows that proposed estimator’s SER performance lies close to that of the MMSE optimal estimator in minimizing aliasing error and suppressing channel noise by using frequency domain data windowing and time domain weighting function. Usage of the Hanning window eliminates error floor and has a compact side lobe level compared to Hamming window and Rectangular window. Hanning window has a larger MSE at low Signal to Noise Ratio (SNR) values and decreases with high SNR values. It is concluded that data windowing technique can minimize the side lobe level and accordingly minimize channel estimation error when interpolation is done. MMSE weighting suppresses channel noise and improves estimation performance. Since Inverse Discrete Fourier Transform (IDFT)/DFT transforms can be implemented with fast algorithms Inverse Fast Fourier Transform( IFFT)/Fast Fourier Transform (FFT) computational complexity can be remarkably reduced.
Go to article

Bibliography

[1] A. Mohammadi, F.M. Ghannouchi, “Single RF front-end MIMO transceivers,” in RF transceiver design for MIMO wireless communications, Springer, Berlin, Heidelberg, pp. 265-288, 2012.
[2] R. Mesleh, H. Haas, C.W. Ahn, S. Yun, “Spatial modulation-a new low complexity spectral efficiency enhancing technique,” in 2006 First International Conference on Communications and Networking in China IEEE, pp. 1-5, Oct 25, 2006.
[3] M. Wen, B. Zheng, K.J. Kim, M. Di Renzo, T.A. Tsiftsis, K.C. Chen, N. Al-Dhahir, “A survey on spatial modulation in emerging wireless systems: Research progresses and applications,” IEEE Journal on Selected Areas in Communications, 37(9): 1949-72, Jul 17, 2019.
[4] H. Doğan, E. Panayırcı, H.V. Poor, “Low-complexity joint data detection and channel equalisation for highly mobile orthogonal frequency division multiplexing systems,” IET communications, 4(8): 1000-11, May 21, 2010.
[5] H. Haas, S. Sinanovic, C.W. Ahn, S. Yun, “Spatial modulation,” IEEE Trans. Veh. Technol, 57(4): 2228-41, Jul 2008.
[6] M. Biguesh, A.B. Gershman, “Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals,” IEEE transactions on signal processing, 54(3):884-93, Feb 21, 2006.
[7] M. Yalcin, A. Akan, H. Doğan, “Low-complexity channel estimation for OFDM systems in high-mobility fading channels,” Turkish Journal of Electrical Engineering & Computer Sciences, 25;20(4): 583-92, Apr. 2012.
[8] J.G. Andrews, A. Ghosh and R. Muhamed, “Fundamentals of WiMAX: understanding broadband wireless networking,” Pearson Education; Feb 27, 2007.
[9] E. Dahlman, S. Parkvall, J. Skold, “4G: LTE/LTE- advanced for mobile broadband,” Academic Press, Oct 7, 2013.
[10] S. Coleri, M. Ergen, A. Puri, A. Bahai, “Channel estimation techniques based on pilot arrangement in OFDM systems,” IEEE Transactions on broadcasting, 7;48(3): 223-9, Nov 2002 .
[11] Y. Wu, Y. Zhao, D. Li, “Channel estimation for pilot-aided OFDM systems in single frequency network,” Wireless Personal Communications, 1;62(1): 227-45, Jan 2012.
[12] H. Doğan, “On detection in MIMO-OFDM systems over highly mobile wireless channels,” Wireless personal communications, 86(2): 683-704, Jan 2016.
[13] Y. Acar, H. Doğan, E. Panayirci, “Pilot symbol aided channel estimation for spatial modulation-OFDM systems and its performance analysis with different types of interpolations,” Wireless Personal Communications, 94(3): 1387-404, Jun 2017.
[14] M. Speth, S. Fechtel, G. Fock, H. Meyr, “Broadband transmission using OFDM: System performance and receiver complexity,” in 1998 International Zurich Seminar on Broadband Communications. Accessing, Transmission, Networking. Proceedings (Cat. No. 98TH8277), IEEE, pp. 99-104, Feb 1998.
[15] F. Ling, C.L. Nikias, J.G. Proakis, C.M. Rader, “Advanced digital signal processing,” Macmillan, 1992.
[16] B. Yang, Z. Cao, K.B. Letaief, „Analysis of low-complexity windowed DFT-based MMSE channel estimator for OFDM systems,” IEEE Transactions on Communications, 49(11): 1977-87, Nov 2001.
[17] Y. Li, “Pilot-symbol-aided channel estimation for OFDM in wireless systems,” IEEE transactions on vehicular technology, 49(4):1207-15, Jul 2000.
[18] P. Hoeher, S. Kaiser, P. Robertson, “Two-dimensional pilot symbol-aided channel estimation by Wiener filtering,” in 1997 IEEE international conference on acoustics, speech, and signal processing, IEEE, Vol. 3, pp. 1845-1848, Apr 1997.
[19] Y. L. Li, L.J. Cimini, N. R. Sollenberger, “Robust channel estimation for OFDM systems with rapid dispersive fading channels,” IEEE Transactions on communications, 46(7): 902-15, Jul 1998.
[20] O. Edfors, M. Sandell, J.J. Van De Beek, S.K. Wilson, P.O. Börjesson, “Analysis of DFT-based channel estimators for OFDM,” Wireless Personal Communications, 1;12(1): 55-70, Jan 2000.
[21] V.K. Jones, G.C. Raleigh, “Channel estimation for wireless OFDM systems,” in IEEE GLOBECOM 1998 (Cat. NO. 98CH36250), IEEE, Vol. 2, pp. 980-985, Nov 8, 1998.
[22] Y. Zhao, A. Huang, “A novel channel estimation method for OFDM mobile communication systems based on pilot signals and transform-domain processing,” in 1997 IEEE 47th Vehicular Technology Conference. Technology in Motion, IEEE, Vol. 3, pp. 2089-2093, May 1997.
[23] B. Yang, K.B. Letaief, R.S. Cheng, Z. Cao, “Windowed DFT based pilot-symbol-aided channel estimation for OFDM systems in multipath fading channels,” in VTC2000-Spring, 2000 IEEE 51st Vehicular Technology, 2020 Conference Proceedings (Cat. No. 00CH37026), IEEE, Vol. 2, pp. 1480-1484, May 15 ,2000.
[24] J.J. Van De Beek, O. Edfors, M. Sandell, S.K. Wilson, P.O. Borjesson “On channel estimation in OFDM systems,” in 1995 IEEE 45th Vehicular Technology Conference. Countdown to the Wireless Twenty-First Century, IEEE, Vol. 2, pp. 815-819, Jul 25 ,1995.
[25] ITU-R M.1225(1997) International Telecommunication Union, “Guidelines for evaluation of radio transmission technologies for IMT-2000,” 1997.
[26] M. Patzold, “Mobile fading channels,” Hoboken: Wiley, 2003
Go to article

Authors and Affiliations

Anetha Mary Soman
1
R. Nakkeeran
1
Mathew John Shinu
2

  1. Department of Electronics Engineering, School of Engineering and Technology, Pondicherry Central University, Pondicherry, India
  2. Department of Computer Science, St.ThomasCollege of Engineering & Technology, Kannur, Kerala, India

This page uses 'cookies'. Learn more