American Journal of Circuits, Systems and Signal Processing
Articles Information
American Journal of Circuits, Systems and Signal Processing, Vol.1, No.2, Jun. 2015, Pub. Date: May 14, 2015
MIMO Channel Correlation and System Capacity Analysis
Pages: 20-27 Views: 4630 Downloads: 1730
Authors
[01] Isiaka A. Alimi, Department of Electrical and Electronics Engineering, School of Engineering and Engineering Technology, Federal University of Technology, Akure, Nigeria.
[02] Kayode F. Akingbade, Department of Electrical and Electronics Engineering, School of Engineering and Engineering Technology, Federal University of Technology, Akure, Nigeria.
[03] Jide J. Popoola, Department of Electrical and Electronics Engineering, School of Engineering and Engineering Technology, Federal University of Technology, Akure, Nigeria.
[04] Michael O. Kolawole, Department of Electrical and Electronics Engineering, School of Engineering and Engineering Technology, Federal University of Technology, Akure, Nigeria.
Abstract
Over the last two decades, wireless communication services and applications worldwide have witnessed exponential growth with corresponding progression in subscribers’ population but without resultant increase in bandwidth required for their effective functionality. Solution to this challenge has led to the adoption of multiple-input–multiple-output (MIMO) systems prevalent in the emerging wireless communication technologies because of their high capacity. This paper investigates the capacity distribution of MIMO systems with arbitrary correlation among the antennas in frequency-flat Rayleigh environments. In addition, the capacity of spatially correlated MIMO channels is evaluated with and without channel state information (CSI) at the transmitter. Through simulation, this study observed that channel capacity increases as signal correlation decreases, while bit error rate increases with increase in signal correlation. Consequently, as the channel becomes progressively correlated in space, the probability of multiplexing gain for the MIMO communication system reduces considerably.
Keywords
Channel State Information (CSI), Singular Value Decomposition (SVD), Rayleigh-Fading Channels, Correlation, Diversity, MIMO
References
[01] D. Gesbert, H. Bölcskei, D. A. Gore, and A. J. Paulraj, “Outdoor MIMO Wireless Channels: Models and Performance Prediction”, IEEE Transactions on Communications, vol. 50, no. 12, pp. 1926-1934, 2002.
[02] P. Kyritsi, D. C. Cox, R. A. Valenzuela, and P. W. Wolniansky, “Correlation Analysis Based on MIMO Channel Measurements in an Indoor Environment”, IEEE Journal on Selected Areas in Communications, vol. 21, no. 5, pp. 713- 720, 2003.
[03] S. K. Jha, M.K. Jain, “Performance Analysis of MIMO Systems using OSTBCs”, International Journal of Engineering Trends and Technology.vol. 4,issue7, pp. 2860-2869, 2013
[04] P. Satyanarayana, M. A. Babu, H. Khan, M. Mallikarjun, “Performance Analysis of MIMO Systems using Orthogonal Space Time Coding over Rayleigh Fading Channel”, International Journal of Modern Engineering Research, vol.2, issue.2, pp-283-287, 2012.
[05] A. Sridhar, B. L. Prakash, M. A. Yaseen, P. Rayi, “Performance and Evaluation of Space Time Block Codes in MIMO WideBandchannel Capacity”, International Journal of Science and Advanced Technology, vol. 1 no. 6, pp. 253-258, 2011.
[06] E. Telatar, “Capacity of Multi-antenna Gaussian Channels,” European Transactions on Telecommunications, vol. 10, no. 6, pp. 585–595, 1999.
[07] D.-S. Shiu, G. J. Foschini, M. J. Gans, and J. M. Kahn, “Fading Correlation and its Effect on the Capacity of Multi-element Antenna Systems,” IEEE Trans. Commun., vol. 48, pp. 502–513, 2000.
[08] G. Tsoulos, MIMO System Technology for Wireless Communications, CRC Press, 2006.
[09] M. Vu, and A. Paulraj, “Optimal Linear Precoders for MIMO Wireless Correlated Channels With Nonzero Mean in Space–Time Coded Systems”, IEEE Transactions on Signal Processing, vol. 54, no. 6, pp. 2318-2332, 2006.
[10] P. Xia, S. Zhou, and G. B. Giannakis, “Adaptive MIMO-OFDM Based on Partial Channel State Information”, IEEE Transactions on Signal Processing, vol. 52, no. 1, pp. 202- 213, 2004.
[11] J. H. Kotecha and A. M. Sayeed, “Transmit Signal Design for Optimal Estimation of Correlated MIMO Channels, IEEE Transactions on Signal Processing”, vol. 52, no. 2, pp. 546-557, 2004.
[12] X. Xiaowei, W. Dexiao, G. Shuhong, "The study of MIMO channel correlation coefficient in the rainfall environment for satellite communications," 2013 IEEE 5th International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, pp.378-382, 2013.
[13] H. Xuemin, W. Cheng-Xiang, J. Thompson, B. Allen, W.Q. Malik, G. Xiaohu, "On Space–Frequency Correlation of UWB MIMO Channels," IEEE Transactions on Vehicular Technology, vol.59, no.9, pp.4201-4213, 2010.
[14] M. Matthaiou, M.R. McKay, P.J. Smith, J.A. Nossek, "On the condition number distribution of complex wishart matrices", IEEE Transactions on Communications, vol.58, no.6, pp.1705-1717, 2010 H. D. Tuan, H. H. Kha, H. H. Nguyen and V. J. Luong, “Optimized Training Sequences for Spatially Correlated MIMO-OFDM”, IEEE Transactions on Wireless Communications, vol. 9, no. 9, pp. 2768-2778, 2010.
[15] D. Gesbert, M. Shafi, D. S. Shiu, P. Smith, and A. Naguib, “From theory to practice: An overview of MIMO space-time coded wireless systems,” IEEE Journal on Selected Areas in Communications, vol. 21, no. 3, pp. 281–302, 2003.
[16] C. X. Wang, X. Hong, H. Wu, and Wen Xu, “Spatial-Temporal Correlation Properties of the 3GPP Spatial Channel Model and the Kronecker MIMO Channel Model”, EURASIP Journal on Wireless Communications and Networking, vol.2007, issue 1, pp. 59-59, 2007.
[17] I. A. Alimi, J. J. Popoola, K. F. Akingbade, and M. O. Kolawole, “Performance Analysis of Bit-Error-Rate and Channel Capacity of MIMO Communication Systems over Multipath Fading Channels”, International Journal of Informatics and Communication Technology. vol.2, no.2, pp. 57-63, 2013.
[18] I. Ali, “Bit-Error-Rate (BER) Simulation Using MATLAB, International Journal of Engineering Research and Applications”, vol. 3, issue 1, pp.706-711, 2013.
[19] J. P. Kermoal, L. Schumacher, K. I. Pedersen, P. E. Mogensen, and F. Frederiksen, “A Stochastic MIMO Radio Channel Model With Experimental Validation”, IEEE Journal on Selected Areas in Communications, vol. 20, no. 6, pp.1211-1226, 2002.
[20] H.J. Song, A. Bekaryan, J.H. Schaffner, A. Hussain, P.S. Kildal, "Effects of Mutual Coupling on LTE MIMO Capacity for Monopole Array: Comparing Reverberation Chamber Tests and Drive Tests," Antennas and Wireless Propagation Letters, IEEE , vol.14, no., pp.454,457, 2015
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