ZF AND MMSE DETECTION STUDY IN MIMO SYSTEMS
Dmitriy A. Tyukhtyaev
Senior Lecturer, Department of Telecommunications Systems,
Volgograd State University
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Prosp. Universitetsky, 100, 400062 Volgograd, Russian Federation
Nadezhda N. Ermakova
Senior Lecturer, Department of Telecommunications Systems,
Volgograd State University
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Prosp. Universitetsky, 100, 400062 Volgograd, Russian Federation
Evgeniy S. Semenov
Candidate of Sciences (Engineering), Associate Professor,
Head of the Department of Telecommunications Systems,
Volgograd State University
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Prosp. Universitetsky, 100, 400062 Volgograd, Russian Federation
Abstract. The proliferation of obstacles in urban environments, such as buildings, wires, and trees, can cause signal re-reflection, resulting in difficulties in detecting the original one. Multipath propagation occurs when electromagnetic waves hit natural and man-made obstacles, leading to the reflection, scattering, and diffraction of waves. This can result in multiple waves arriving at the receiving antenna with different delays, attenuations, and phases. To address this problem, the widely used in modern wireless communication systems MIMO technology (Multiple Input Multiple Output) can be utilized. It uses multiple transmitting and receiving antennas to increase communication channel throughput and reduce errors that occur during data transmission. This paper examines a receiver with decorrelation (ZF receiver) and a receiver that minimizes the minimum mean square error (MMSE). Additionally, an algorithm for a MIMO transmission system model using ZF and MMSE receivers is presented. In the final section of the article, the results of Bit Error Rate (BER) modeling are presented for different numbers of transmitting and receiving antennas. The presented algorithm can significantly reduce errors that occur during data transmission in urban environments, where multipath propagation is common because of signal re-reflection. The use of MIMO technology with decorrelation and MMSE receivers can increase the efficiency of communication channels and make wireless communication more reliable in urban environments.
Key words: signal, circuit, communication channel, data analysis, MIMO, attenuation, distortion, interference, modeling, BER, ZF, MMSE.
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