Neural Network Feedback Encoding for Wireless Nodes

The patent applies preliminary action by performing pre-training of neural network encoders and decoders using shared common latent data sets before actual feedback transmission. This pre-training establishes compatibility between communication nodes in advance, allowing them to work together effectively even though their neural network structures are not identical. The common latent data set serves as a preliminary training resource that aligns the neural networks of different nodes before they engage in actual feedback operations.

The patent applies copying by using common latent data sets that are shared between communication nodes. Instead of requiring each node to develop and maintain completely independent neural network models, the system creates copies of the training data (common latent data sets) that can be used by multiple nodes. This reduces system complexity while maintaining transmission accuracy, as nodes can leverage the same training resources rather than requiring unique complex models for each node.

The patent applies universality by designing a system where common latent data sets serve multiple functions: they are used for pre-training neural networks, for maintaining compatibility during operations, and for enabling smooth transitions during node updates and handovers. The same common latent data set infrastructure supports both efficient feedback processing and adaptability to changes, making the system multi-functional without requiring separate mechanisms for each purpose.