Abstract: The fast development of wireless communication technologies has placed a great pressure on the requirements of high data rates, low latency, and reliable transmission, especially in innovative systems, like 5G and future 6G networks. Orthogonal Frequency Division Multiplexing (OFDM) has proven to be one of the most important modulation methods since it can effectively absorb multipath fading and effectively use the spectrum. Nonetheless, the functioning of the OFDM systems is very sensitive to the proper channel estimation and signal detection that becomes difficult in the dynamic wireless environment due to noise, interference and fading. Conventional methods like Least Squares (LS) and Minimum Mean Square Error (MMSE) are based on the priori mathematical models and statistical conditions, which may be ineffective in reflecting the actual variations of channels. In order to address these drawbacks, this paper suggests a smart signal processing solution based on deep learning, namely Long Short-Term Memory (LSTM) networks, to estimate the channel jointly and detect symbols in the OFDM system. The model proposed is capable of learning the complex channel behaviors directly through the data without having prior channel knowledge. MATLAB simulations are used to implement and assess the system under different conditions of the channel. The findings indicate that it has great enhancements in Bit Error Rate (BER) performance, robustness, and adaptability over traditional techniques. This research paper emphasizes how deep learning methods can be applied in wireless communication systems to develop intelligent, adaptive, and efficient receivers in the future high-speed network.

Keywords: OFDM, Deep Learning, LSTM, Channel Estimation, Signal Detection,5G ,6G ,BER Wireless Communication, Neural Networks, Intelligent Signal Processing, MATLAB Simulation.

Downloads: | DOI: 10.17148/IJIREEICE.2026.14441