Channel Estimation in Wireless Communication

Advanced channel estimation is crucial for improving the performance of 5G, 6G, and future communication systems. Techniques like Least Squares (LS) and Minimum Mean Square Error (MMSE) provide accurate estimations, validated through MATLAB simulations. OFDM enables high data rates and reliable transmission across various modulation schemes. Emerging methods, such as Extended Kalman Filters (EKF) for Direction of Arrival (DoA) and Time of Arrival (ToA) in mmWave systems, deep learning for reducing pilot overhead, and CNN-LSTM models for Non-Orthogonal Multiple Access (NOMA) enhance accuracy and efficiency. Autoencoders assist in noise reduction and feature extraction, while transformers improve performance by capturing both local and global dependencies. Sparse reconstruction techniques optimize mmWave precoding, and tensor-based methods tackle the challenges of high-dimensional MIMO systems. These advancements collectively improve spectral efficiency, reduce complexity, and ensure robust communication for next-generation networks.