SPARSE CHANNEL ESTIMATION IN MMWAVE MIMO-OFDM USING BI-LSTM WITH CHANNEL SPARSITY REGULARIZATION

The primary objective of millimeter wave (mmWave) Multiple-Input Multiple-Output (MIMO) systems is to effectively estimate the Channel State Information (CSI). Recent research has increasingly utilized the nuclear norm theory to recover a lower-rank structure of channel matrices. Certain sub-optimal solutions to the rank minimization issue arise when addressing nuclear norm-based convex formulations that reduce channel estimation accuracy. To mitigate this, this research developed the Channel Sparsity Regularization – Bidirectional Long Short-Term Memory (CSR-Bi-LSTM), effectively estimates a channel in mmWave MIMO-OFDM. This research incorporates sparsity as a constraint and ensures the method focuses on essential paths, enhancing accuracy and reducing noise. The Bi-LSTM network captures the relationships among OFDM symbols and exploits the spatial correlations between multiple antennas in MIMO. Moreover, it learns the non-linear relationship among pilot signals and channels, which helps to enhance accuracy of channel estimation. The developed algorithm obtained accuracy of 0.0993 for 35 learning rate, 0.990 for 45 learning rate, 0.986 for 55 learning rate, 0.982 for 65 learning rate and 0.976 for 75 learning rate when compared to other conventional techniques.