Deep Learning Techniques For Signal Processing On Spherical Manifolds In Near-Field Applications

Improvements in deep learning have benefited signal processing on spherical surfaces in particular. This work addresses deep learning for close-range spherical surface signal processing. Traditional signal processing algorithms may struggle to handle complex data on spherical surfaces. Deep learning has the potential to close this gap. Our deep learning in signal processing examination begins with its principles, difficulties, and accomplishments. The article goes on to illustrate how spherical geometry influences data encoding and signal processing, underscoring the need for understanding signal processing on spherical manifolds. The research emphasises the need for cutting-edge solutions to tackle non-Euclidean data representation challenges quickly. We are experimenting with several deep learning architectures for spherical manifold data. Among these architectures are attention mechanisms, GNNs, CNNs, and so on. Integrating these models with spherical data layers and methodologies should allow for a thorough evaluation of their ability to detect complex close-range patterns.Deep learning algorithms increase near-field signal processing on spherical manifolds in terms of accuracy, efficiency, and resilience, according to the results. Deep learning architectures for curved spherical data processing are advanced in this work. This objective necessitates research on its theoretical underpinnings, existing approaches, and practical applications. These unique and adaptable data management solutions provide remarkable data management on spherical manifolds for near-field applications. Their data management systems are more precise and trustworthy than earlier ones.