Orthogonal Frequency Division Multiplexing (OFDM) is a key technique that effectively controls inter-symbol interference (ISI) that arises because of the delays in the multi path propagation in wireless communication. Its high spectral efficiency and mitigation against multipath fading make it widely adopted by many wireless systems. In this paper, we explore techniques to improve the performance of OFDM for wireless communication, focusing on channel estimation, signal detection and bit error rate (BER) reduction. To address these limitations of OFDM, we incorporate Schur decomposition for efficient channel equalization and improved numerical stability. Channel estimation is essential in wireless communication systems to accurately recover the transmitted signal at the receiver. It plays a critical role in enhancing system performance by improving signal detection, reducing bit error rates (BER), and enabling advanced techniques like equalization and adaptive modulation. While conventional channel estimation techniques like (Least Squares) LS is simpler, it is prone to errors in noisy channels, making it less effective practically. Although (Minimum Mean Square Error) MMSE estimation performs better in noisy conditions, it still struggles in dynamic and noisy environments. To address these limitations, we propose Convolutional Neural Network (CNN)-based noise suppression, which potentially outperformed LS and MMSE by learning non-linear channel distortions and adapting to dynamic noise conditions. This intelligent estimation technique improves BER performance, enhances signal detection, and enables real-time adaptive communication, offering a promising solution for next-generation wireless systems.