Machine learning based channel estimation for wireless communication networks

The advancement of wireless communication systems has brought about an urgent need for more accurate and robust channel estimation techniques, especially in Orthogonal Frequency Division Multiplexing (OFDM)-based systems. Traditional estimation methods, such as Least Square (LS) and Minimum Mean Square Error (MMSE), although computationally efficient, often suffer from performance degradation under dynamic channel conditions, noise, and multipath fading. To address these limitations, this research explores the application of deep learning (DL) models for enhanced channel estimation in OFDM systems. Specifically, this study implements and compares several models: LS, MMSE, Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Convolutional Neural Network (CNN), and a hybrid CNN-LSTM network. The simulation setup utilizes MATLAB and incorporates QPSK and 16-QAM modulation schemes under various Signal-to-Noise Ratio (SNR) levels. The DL models are trained using large datasets comprising pilot symbols and corresponding channel responses. Performance is evaluated using Mean Square Error (MSE) and Bit Error Rate (BER) metrics. The results demonstrate that the proposed deep learning models, particularly the hybrid CNN-LSTM architecture, significantly outperform traditional estimators by providing superior channel tracking and noise resilience. The CNN effectively extracts spatial features while LSTM captures temporal dependencies, resulting in improved accuracy across low to high SNR regions. This work confirms that integrating DL models into OFDM systems can substantially enhance estimation accuracy and robustness, making them viable solutions for next-generation wireless communication technologies.