A Coded MIMO-OFDM System’s Performance Comparison of the Spatial Channel Model and the Onering Channel Model Based on Interpolation Techniques
Main Article Content
Abstract
In this paper, we consider to estimate the channel coefficient in the wideband and frequency selective multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system. The simulation is based on two channel models, one has been proposed by the 3rd Generation Partnership Project (3GPP) standard - the Spatial Channel Model (SCM) and the other is the Onering channel model, under the LTE Advanced standard for 4G in the suburban macro-cell environment. The obtained results show the symbol error rate (SER) value when using different interpolations (Linear, Sinc or Wiener) with the same input parameters. The Space Frequency Block Coding (SFBC) and minimum mean-squared error (MMSE) equalizer are also used for the simulation of the MIMO 2x2 systems. The SER results in the SCM channel model are lower than that obtained by the Onering channel model when employing the different interpolation methods.
Keywords
MIMO-OFDM, Onering channel model, SCM channel model, SFBC, Wiener interpolation, Sinc interpolation
Article Details
References
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[2] Thuong N., Van Duc N., Phuong Dang, Luong PhamVan, Thu Nga N., & Patzold, M. (2012), A performance study of LTE MIMO-OFDM systems using the extended one-ring MIMO channel model. In The 2012 International Conference on Advanced Technologies for Communications (ATC 012) (pp. 263–268).
[3] 3GPP, Technical Specification Group Radio Access Network Spatial channel model for Multiple Input Multiple Output (MIMO) simulation, pp. 25–996, Release 10, Mar. 2011.
[4] Nguyen, T. Nga., & Nguyen, V. D. (2016), Research article, A performance comparison of the SCM and the Onering channel modeling method for MIMO-OFDM systems, (October), 3123–3138.
[5] Jiang Y, Varanasi MK, Li J, Performance Analysis of ZF and MMSE Equalizers for MIMO System: An In-Depth Study of the High SNR Regime, IEEE Transactions on Information Theory 2011, 2008–2026.
[6] Alan V. Oppenheim and Ronald W. Schafer, Discrete Time signal processing, chapter 7, pp. 473–475, Prentice Hall, 1999.
[7] S. Hayking, Adaptive Filter Theory, Prentice Hall, 1986, USA.
[8] Hajizadeh, F. R., Mohamedpor, S. K., & Tarihi, T. M. R. (2010), Channel Estimation in OFDM System Based on the Linear Interpolation, FFT and Decision Feedback, 484–488, 18th Telecommunications Forum TELFOR 2010.
[9] Zhang, X., & Yuan, Z. (n.d.), The Application of Interpolation Algorithms in OFDM Channel Estimation, ijssst, Vol-17, No-38, paper11, pp. 1–5.
[10] Nasreddine, M., Bechir, N., Hakimiand, W., & Ammar, M. (2014), Channel Estimation for Downlink LTE System Based on LAGRANGE Polynomial Interpolation, ICWMC 2014: The Tenth International Conference on Wireless and Mobile Communications, 65–69.
[11] Schanze, T. (1995), Sinc interpolation of discrete periodic signals, IEEE Transactions on Signal Processing, 43(6), 1502–1503.
[12] Li du and Louis Scharf, (1990), Wiener Filters for Interpolation and Extrapolation, Conference Record Twenty-Fourth Asilomar Conference on Signals, Systems and Computers.