Neural Network Optimization for Real-Time Wireless Systems

The patent applies preliminary action by training neural networks offline before real-time deployment. The neural networks are pre-trained using iterative optimization algorithms on historical data, so that when deployed online, they can provide rapid optimization solutions without performing heavy computational optimization in real-time. This shifts the computational burden from online to offline, enabling fast real-time performance while maintaining accuracy.

The patent performs the computationally intensive training process in advance (offline) before the actual real-time application. The neural networks are trained using iterative optimization algorithms that require significant computational resources, but this training is completed beforehand. During real-time operation, the pre-trained networks only require inference computations, which are much faster and enable real-time processing without sacrificing solution quality.

The patent substitutes traditional mechanical optimization algorithms with a neural network-based system. Instead of directly applying complex iterative optimization algorithms during real-time operations, the system uses neural networks that have been trained to approximate the optimization process. This substitution reduces the computational complexity and device requirements while maintaining solution precision, as the neural network inference is much simpler than running full optimization algorithms.