Traffic offloading and resource allocation enabled hybrid satellite-terrestrial networks in cognitive integrated systems using a recurrent neuro-fuzzy model

In recent years, the demand for high-speed and reliable communication networks has grown exponentially. To meet this demand, researchers and engineers have been exploring innovative solutions that combine the benefits of both satellite and terrestrial networks. The complexity of accurately modeling and predicting dynamic network conditions to optimize resource distribution and maintain seamless connectivity. The objective of this work is to develop and implement a recurrent neuro-fuzzy model (RNFM)for optimizing traffic offloading and resource allocation in hybrid satellite-terrestrial networks within cognitive integrated systems. This work, begins with cognitive integrated hybrid satellite-terrestrial networks employing spectrum-sharing techniques. These techniques integrate with software-defined networks (SDN) to facilitate traffic offloading in hybrid satellite-terrestrial networks (H-STN). The process incorporates a second-price sealed-bid auction mechanism to efficiently allocate resources. Joint resource allocation is then optimized using two convex optimization methods. This allocation, in turn, informs the resource allocation strategy. The system further incorporates user behavior analysis and employs a recurrent neuro-fuzzy model with deep feed-forward neural networks to enhance the accuracy and efficiency of the entire process. MATLAB simulation that incorporates adaptive learning algorithms and fuzzy logic to dynamically manage network resources and improve system efficiency. The findings show that the proposed technique outperforms both one-step and multi-step prediction algorithms with an accuracy increase of 99.23%. The future scope for this research is to integrate advanced machine learning algorithms, such as reinforcement learning, with the recurrent neuro-fuzzy model to further enhance dynamic traffic offloading and resource allocation in increasingly complex and heterogeneous satellite-terrestrial network environments.