18666539. DEEP NEURAL NETWORK PROCESSING FOR A USER EQUIPMENT-COORDINATION SET simplified abstract (Google LLC)

DEEP NEURAL NETWORK PROCESSING FOR A USER EQUIPMENT-COORDINATION SET

Organization Name

Google LLC

Inventor(s)

Jibing Wang of San Jose CA (US)

Erik Richard Stauffer of Sunnyvale CA (US)

DEEP NEURAL NETWORK PROCESSING FOR A USER EQUIPMENT-COORDINATION SET - A simplified explanation of the abstract

This abstract first appeared for US patent application 18666539 titled 'DEEP NEURAL NETWORK PROCESSING FOR A USER EQUIPMENT-COORDINATION SET

Simplified Explanation: The patent application describes techniques and apparatuses for processing deep neural networks (DNN) for a user equipment-coordination set (UECS) using machine learning configurations.

Key Features and Innovation:

• Selection of an end-to-end machine-learning configuration for processing UECS communications.
• Formation of sub-DNNs by multiple devices in the UECS based on the ML configuration.
• Feedback mechanism for adjusting the ML configuration.
• Updating sub-DNNs based on the adjustment.

Potential Applications: This technology can be applied in telecommunications, network optimization, and machine learning systems.

Problems Solved: The technology addresses the efficient processing of communications in a UECS, optimizing network performance, and enhancing machine learning configurations.

Benefits:

• Improved communication processing in UECS.
• Enhanced network performance and efficiency.
• Dynamic adjustment of machine learning configurations.

Commercial Applications: The technology can be utilized in telecommunications networks, IoT systems, and AI-driven applications for optimizing communication processes and network performance.

Prior Art: Readers can explore prior research in the fields of machine learning, telecommunications, and network optimization for related technologies.

Frequently Updated Research: Stay updated on advancements in machine learning algorithms, network optimization techniques, and communication protocols relevant to this technology.

Questions about UECS: 1. What are the potential challenges in implementing machine learning configurations in UECS communications? 2. How can the feedback mechanism in the patent application improve the efficiency of UECS processing?

Original Abstract Submitted

Techniques and apparatuses are described for deep neural network (DNN) processing for a user equipment-coordination set (UECS). A network entity selects () an end-to-end (E2E) machine-learning (ML) configuration that forms an E2E DNN for processing UECS communications. The network entity directs () each device of multiple devices participating in an UECS to form, using at least a portion of the E2E ML configuration, a respective sub-DNN of the E2E DNN that transfers the UECS communications through the E2E communication, where the multiple devices include at least one base station, a coordinating user equipment (UE), and at least one additional UE. The network entity receives () feedback associated with the UECS communications and identifies () an adjustment to the E2E ML configuration. The network entity then directs at least some of the multiple devices participating in an UECS to update the respective sub-DNN of the E2E DNN based on the adjustment.