Impact of Polarization Diversity on Channel Estimation Accuracy in 5G Networks Using MIMO-OFDM Technology

Abstract

Fifth-generation (5G) wireless networks rely heavily on accurate channel estimation to achieve their ambitious performance targets of ultra-high data rates and ultra-low latency. This paper presents a comprehensive analysis of polarization diversity impact on channel estimation accuracy in 5G networks using Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) technology. The novel contribution of this work lies in the systematic evaluation of dual polarization effects on channel estimation performance compared to conventional single polarization approaches, providing quantitative insights that have been lacking in existing literature.

Our methodology employs rigorous mathematical modeling and extensive MATLAB simulations to compare Least Squares (LS) and Minimum Mean Square Error (MMSE) channel estimation algorithms under both single and dual polarization scenarios. The simulation framework is based on realistic 5G New Radio (NR) specifications, including 3.5 GHz carrier frequency, 100 MHz bandwidth, and Rayleigh fading channel conditions.

Key findings demonstrate that dual polarization with MMSE achieves up to 8 dB improvement in Signal-to-Noise Ratio (SNR) performance compared to single polarization systems, with Mean Square Error (MSE) reduction by more than one order of magnitude under low SNR conditions. The practical implications for 5G network design include enhanced spectral efficiency, improved link reliability, and better coverage in challenging propagation environments. These results provide valuable guidelines for engineers implementing advanced antenna systems in next-generation wireless networks.