Channel Estimation in Multi-Antenna Cellular Systems: Taguchi Optimization Techniques

Multiple-input multiple-output (MIMO) systems improve wireless communication by increasing data rates and supporting multiple users, making them essential for 5G and future networks. Accurate channel estimation is necessary to enhance system performance. This study examines three key estimation methods: Least Squares (LS), Minimum Mean Square Error (MMSE), and a Taguchi-optimized MMSE approach. The Bit Error Rate (BER) and Mean Square Error (MSE) performance are analyzed across Rayleigh, Rician, and Nakagami fading channels under different SNRs to understand the impact of noise variance on system reliability. The results show that the Taguchi-optimized MMSE method performs better than LS and standard MMSE by reducing MSE and improving noise resistance. At 10 dB SNR, the Taguchi-based method achieves the lowest MSE across all fading models, showing its ability to adapt to different channel conditions. This study highlights the importance of noise variance control, efficient estimation techniques, and multiantenna systems in improving MIMO performance for next-generation wireless networks.