Efficient Hybrid Channel Estimation for Massive MIMO in IoT Applications

This work introduces a hybrid channel estimation method designed for Massive MIMO (M-MIMO) systems in IoT applications. The proposed approach focuses on minimizing complexity while ensuring competitive performance. The hybrid method combines Least Squares (LS) and Minimum Mean Square Error (MMSE). It employs switching-based approaches with adaptive weight functions that adjust to varying channel conditions. Through comprehensive simulations involving 40 IoT devices, we analyze the impact of the Rician K-factor on the Relative Channel Estimation Error (RCEE) and evaluate the Average Variance of Channel Estimation Error (VarCEE) against the number of base station antennas under fixed K-factor. Our results demonstrate that the proposed hybrid method maintains performance comparable to MMSE while achieving lower computational complexity. This work highlights the potential of hybrid estimation techniques in enhancing the efficiency of MMIMO systems for IoT deployments.