Adaptive OTFS Modulation for Multi-Domain Networks Using Dynamic Neural Networks

This project presents a novel dynamic neural network-based adaptive Orthogonal Time Frequency Space (OTFS) modulation framework designed for efficient and robust communication in multi-domain wireless networks. The proposed system leverages Deep Residual Denoising Networks (DRDNs) for highly accurate channel estimation, enabling the system to effectively mitigate the challenges posed by rapidly varying and heterogeneous channel environments. To enhance adaptability, reinforcement learning algorithms are employed to dynamically adjust modulation parameters in response to real-time channel conditions, optimizing performance metrics such as throughput, latency, and energy consumption. Furthermore, the integration of Integrated Sensing and Communication (ISAC) technologies allows the system to perform simultaneous environmental sensing and data transmission, supporting the demands of next-generation intelligent wireless applications such as autonomous vehicles and smart cities. Comprehensive simulation results demonstrate that the adaptive OTFS framework significantly outperforms conventional systems in terms of reliability, spectral efficiency, and energy utilization, particularly in complex and dynamically changing communication scenarios.