Wireless communication systems are essential for modern telecommunications, including fifth-generation (5G) networks and future sixth-generation (6G) technologies. However, wireless signals are affected by noise, multipath propagation, and fading, which can reduce the reliability of data transmission. Channel estimation is an important process that enables the receiver to estimate the characteristics of the wireless channel and recover the transmitted information accurately. Traditional channel estimation techniques, such as Least Squares (LS), are widely used but may provide limited performance under challenging channel conditions. Deep learning has emerged as a promising approach for improving channel estimation by learning complex relationships between transmitted and received signals. This article investigates the application of deep learning to channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems. The proposed approach can be implemented and evaluated using MATLAB simulations under different signal-to-noise ratio (SNR) values and wireless channel conditions. Performance can be assessed using the Mean Squared Error (MSE) and Bit Error Rate (BER), with comparisons against conventional estimation methods. Integrating deep learning into channel estimation may improve the reliability and efficiency of wireless communication systems. Keywords: Artificial Intelligence, Deep Learning, OFDM, Channel Estimation, Wireless Communication, MATLAB, BER, MSE. 1. Introduction The rapid development of wireless communication technologies has increased the demand for high-speed data transmission and reliable connectivity. Modern wireless systems must provide efficient communication despite noise, interference, and variations in the propagation environment. Orthogonal Frequency Division Multiplexing is widely used in wireless communication because it divides a high-rate data stream into multiple lower-rate streams transmitted over orthogonal subcarriers. This structure helps reduce the effects of frequency-selective fading and supports efficient data transmission. However, the performance of OFDM systems depends significantly on accurate channel estimation. The receiver must estimate the channel characteristics to compensate for signal distortion and recover the transmitted data. Conventional methods, including Least Squares and Minimum Mean Square Error estimation, are commonly used for this purpose. Nevertheless, their performance depends on channel conditions and the assumptions used in the estimation process. Deep learning offers an alternative approach by allowing neural networks to learn relationships between channel characteristics and received signals from training data. This capability makes deep learning a promising tool for improving channel estimation in complex wireless environments. 2. Proposed Methodology The proposed system consists of an OFDM transmitter, a wireless communication channel, an OFDM receiver, and a deep learning-based channel estimation module. Initially, random binary data are generated and mapped to a digital modulation scheme, such as QPSK. The modulated symbols are then converted into OFDM signals using the Inverse Fast Fourier Transform (IFFT). A cyclic prefix is added before transmission through the simulated wireless channel. The channel introduces noise and may produce multipath fading. At the receiver, the cyclic prefix is removed, and the Fast Fourier Transform (FFT) is applied to recover the received subcarrier symbols. A conventional channel estimation method, such as Least Squares, can provide a baseline estimate. A deep neural network can then be trained using suitable channel data to estimate channel characteristics from the received signals and available pilot information. The proposed method can be implemented in MATLAB by generating training and testing datasets under different channel conditions. The trained network should be evaluated on data that were not used during training to assess its generalization capability. 3. Performance Evaluation The performance of the proposed method can be evaluated using Mean Squared Error and Bit Error Rate. Mean Squared Error measures the difference between the actual channel response and the estimated channel response. A lower MSE indicates more accurate channel estimation. Bit Error Rate measures the ratio of incorrectly detected bits to the total number of transmitted bits. A lower BER indicates more reliable data transmission. Simulation experiments can be performed at different SNR values using AWGN and Rayleigh fading channel models. The results of the deep learning-based method can then be compared with those of conventional channel estimation techniques. The effectiveness of the proposed approach must be confirmed through simulation results, including numerical comparisons and performance curves. 4. Conclusion Deep learning provides a promising approach to improving channel estimation in OFDM wireless communication systems. By learning complex channel characteristics from training data, neural networks may overcome some limitations of conventional estimation techniques. MATLAB simulations can be used to evaluate the proposed approach under different channel conditions and compare its performance with traditional methods. Future research may investigate convolutional neural networks and other deep learning architectures to improve channel estimation accuracy and communication reliability.