Applied Nonlinear Analysis and Machine Learning Transforming the Horizons of Communication Technology

Abstract

The combination of machine learning and applied nonlinear analysis has significantly changed the state of communication technology. The fundamental structure of communication networks has undergone a radical change as a result of this convergence. The convergence of machine learning and nonlinear analysis methods has yielded hitherto untapped possibilities for improving network optimisation, data transfer, and signal processing. Because it can describe complex systems, nonlinear analysis provides a greater understanding of the complex dynamics found in communication networks. Through the application of dynamical systems, fractal geometry, and chaos theory, it is possible to identify underlying patterns and behaviours in data, leading to more effective information processing and transmission. Machine learning is added to further enhance these skills. Communication systems are able to automatically optimise their performance, anticipating network behaviours and making real-time adjustments to changing circumstances thanks to algorithms that learn from data and adapt to it. In tasks ranging from resource allocation and interference mitigation to signal denoising and modulation categorization, machine learning approaches including neural networks, deep learning, and reinforcement learning have shown to be highly effective. Combining these two fields is accelerating the creation of communication systems that are more resilient, dependable, and flexible. Communication systems can overcome past constraints by utilising the complementary strengths of nonlinear analysis and machine learning. Massive data quantities may now be handled by them, along with the ability to reduce signal distortions, anticipate and avoid network congestion, and dynamically adjust to shifting communication settings.