Neural Network Downlink Control Channel for Privacy-Aware AI Transmission

The system divides the AI processing into separate encoder and decoder components that can be independently deployed on different devices (UE and base station). The encoder is deployed on the UE while the decoder resides on the base station, segmenting the complex autoencoder architecture into manageable parts that reduce overall system complexity while maintaining design efficiency benefits.

The patent introduces federated learning as an intermediary mechanism that enables collaborative training of the autoencoder without requiring centralization of sensitive data. This intermediary approach allows the system to achieve improved design efficiency while managing data privacy and reducing the complexity of centralized AI deployment by enabling distributed model training.

Federated learning serves as an intermediary that enables model training without direct data sharing. The system allows the server to receive only model parameters (gradients/weights) rather than raw device data, acting as a mediator that facilitates accurate model training while protecting personal information privacy through cryptographic and architectural safeguards.

The patent extracts only the essential model training information (gradients and weights) from the device, leaving the sensitive raw data on the local device. This extraction approach allows the server to improve model accuracy by receiving updated parameters without accessing or storing personal information, thereby resolving the contradiction between model accuracy and privacy protection.

The patent segments the AI computational workload by deploying different components of the autoencoder on different devices in the network. The encoder runs on UEs with limited computational resources while the decoder runs on base stations with more processing power, and model training occurs on cloud servers. This segmentation enables balanced load distribution across the network hierarchy while utilizing the computational power of each device appropriately.

The system transitions from a single-dimensional centralized cloud processing model to a multi-dimensional distributed architecture spanning device edge (UE), network edge (base station), and cloud server layers. This dimensional expansion of the computational architecture enables efficient load distribution by placing processing tasks at the appropriate level of the network hierarchy, optimizing both computational power utilization and load balance.