Estimation of Direction of Signals using Wavenet in Wireless Communication Systems

The project "Estimation of Direction of Signals using Wavenet in Wireless Communication Systems" proposes a novel signal processing method, particularly under suboptimal antenna conditions. The new technique combines convolutional neural networks (CNNs) and wavelet transforms to improve signal direction estimation accuracy even in noisy conditions or when there are equipment defects. CNNs detect intricate signal patterns, while wavelet transforms enable precise time-frequency analysis. All this together forms a strong model that can accommodate real-world problems such as improper antenna placement, signal overlap, and hardware distortions—outperforming existing techniques in terms of accuracy and reliability. The data-driven approach provides excellent training and test outcomes, substantiating the strength of the model to perform well in adverse signal conditions. It estimates vital parameters such as signal delays, frequency shifts, and array distortion, allowing for consistent performance even under dynamic scenarios. Since the system continues to improve with new data, the flexibility of the system makes it very suitable for use in wireless communication, radar, and audio signal processing. By blending deep learning and signal processing know-how, the project creates a new standard in direction-of-arrival estimation, adding substantially to the development of advanced communication systems today.