THE EFFICIENCY RESEARCH OF MIMO SYS-TEMS WITH CORRELATED ELEMENTS OF A CHANNEL TRANSMISSION MATRIX

M. Odintsov, Yu. Shinakov,
Institute of Radio and Information Systems (IRIS), Vienna, Austria

DOI: 10.36724/2664-066X-2023-9-3-35-40

SYNCHROINFO JOURNAL. Volume 9, Number 3 (2023). P. 35-40.

Abstract

Currently, there is an active development of wireless data transmission systems. A solution widely used in improving existing and creating new wireless communication technologies, which allows increasing spectral efficiency and transmission speed, is the use of systems with Multiple-Input-Multiple-Output, MIMO. In this regard, research into the potential capabilities of MIMO systems under various operating conditions is an urgent task. In particular, the issue of the influence of the correlation of transmission matrix elements on the efficiency of the communication system is important. We will further consider cases of multipath propagation typical for urban conditions. We will also consider the signals transmitted by different antennas to be narrowband and independent of each other. For the two currently most commonly used MIMO radio channel models, the influence of the correlation between the elements of the transmission channel matrix on the efficiency of the MIMO system was studied using statistical tests. Dependences of the error probability per bit of transmitted information on the signal-to-noise ratio were obtained for channels with transmission matrices whose covariance matrices have varying degrees of difference from the diagonal (unit) matrix. When generating channel matrices with correlated elements, the unstructured (general correlation) model and the Kronecker model are used.

Keywords:  MIMO, Transmission Channel Matrix, Monte Carlo Method

References

[1] T. Gong, G. Yang, K. Mu, C. Zhao and Z. Yang, “An Improved Upper Bound on the Maximum Eigenvalue of Exponential Model Based Spatial Correlation Matrices in Massive MIMO Systems,” 2019 IEEE 19th International Conference on Communication Technology (ICCT), Xi’an, China, 2019, pp. 638-642, doi: 10.1109/ICCT46805.2019.8947034.

[2] G. Yang, H. Zhang, Z. Shi, S. Ma and H. Wang, “Asymptotic Outage Analysis of Spatially Correlated Rayleigh MIMO Channels,” IEEE Transactions on Broadcasting, vol. 67, no. 1, pp. 263-278, March 2021, doi: 10.1109/TBC.2020.3028346.

[3] A. de la Fuente, G. Interdonato and G. Araniti, “User Subgrouping and Power Control for Multicast Massive MIMO Over Spatially Correlated Channels,” IEEE Transactions on Broadcasting, vol. 68, no. 4, pp. 834-847, Dec. 2022, doi: 10.1109/TBC.2022.3190990.

[4] H. Akhlaghpasand, E. Björnson and S. M. Razavizadeh, “Jamming-Robust Uplink Transmission for Spatially Correlated Massive MIMO Systems,” IEEE Transactions on Communications, vol. 68, no. 6, pp. 3495-3504, June 2020, doi: 10.1109/TCOMM.2020.2978192.

[5] Nelson Costa, Simon Haykin, “Multiple-Input Multiple-Output Channel Models: Theory and Practice,” Hoboken: John Wiley & Sons, Inc., 2010. 230 p.

[6] P. Almers, E. Bonek, “Survey of Channel and Radio Propagation Models for WirelessMIMO Systems,” Hindawi Publishing Corporation, 2006.

[7] E. Telatar. Capacity of multiantenna Gaussian channels. Tech. Rep., AT&T Bell Labs., 1995.

[8] Alain Sibille, Claude Oestges, Alberto Zanella, “MIMO: from theory to implementation,” San Diego: Elsevier Inc., 2011. 360 p.

[9] Jerry R. Hampton, “Introduction to MIMO Communications,” N.Y.: Cambridge University press, 2014. 288 p.

[10] M.O. Odintsov, Yu.S. Shinakov, “Study of the efficiency of MIMO systems with correlated transmission channel matrix elements,” DSPA: Issues in the application of digital signal processing. 2016, pp. 115-120.

[11] Claude Oestges, Bruno Clerckx, “MIMO Wireless Communications: From Real-World Propagation to Space-Time Code Design,” N.Y.: Elsevier Ltd., 2007. 448 p.

[12] Christian Zimmermann, Matthias Paasch, Oomke Weikert and Udo Z¨olzer, “On Investigating Spatial Correlations of MIMO Indoor Channels,”. Department of Signal Processing and Communications Helmut Schmidt University – University of the Federal Armed Forces Hamburg, 2008.

[13] Y. A. S. Dama, R. A. Abd-Alhameed, S. M. R. Jones, D. Zhou, N. J. Mc Ewan, M. B. Child, and P. S. Excell, “An Envelope Correlation Formula for (N,N) MIMO Antenna Arrays Using Input Scattering Parameters, and Including Power Losses,” Hindawi Publishing Corporation International Journal of Antennas and Propagation. Vol. 2011, Article ID 421691, pp. 1-7.

[14] Constantinos Votis, George Tatsis, Panos Kostarakis, “Envelope Correlation Parameter Measurements in a MIMO Antenna Array Configuration,” Physics Department, University of Ioannina, Ioannina, Greece, 2010.

[15] Kainam Thomas Wong and Yue Ivan Wu, “Spatial-Polarizational Correlation-Coefficient Function Between Receiving-Antennas in Radiowave Communications – Geometrically Modeled,” Analytically Derived, Simple, Closed-Form, Explicit Formulas. IEEE Transactions on Communications. Vol. 57, No. 12, December 2009, pp. 3566-3570.

[16] ETSI TR 125 996 V17.0.0 (2022-05) – Universal Mobile Telecommunications System (UMTS); Spatial channel model for Multiple Input Multiple Output (MIMO) simulations (3GPP TR 25.996 version 17.0.0 Release 17).