Hybrid Model for Efficient Channel Estimationin 5G Multicarrier Modulation using OMP

Abstract

Channel estimation is fundamental to efficiently operating multi input multi output MIMO devices in 5G networks. This work presents a hybrid optimal channel estimation model to improve the accuracy and efficiency of channel estimation in 5G networks by combining the advantages of orthogonal frequency division multiplexing OFDM and filter bank multicarrier FBMC. Using sophisticated sparse signal recovery techniques such as orthogonal matching pursuit OMP, the proposed model correctly estimates the channel over multiple signal to noise ratios. The main performance metrics investigated in the model evaluation process are mean square error MSE, bit error rate BER, spectral efficiency, channel capacity, and throughput. Instead of traditional OFDM, 5G networks use the hybrid optimal model at high SNR levels to improve channel prediction accuracy and data transmission efficiency.