Communication Device AI/ML Entities for Distributed Data Processing
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Solution Overview
Problem
Existing communication systems lack integration of AI/ML technology, leading to inefficiencies in real-time data processing and problem resolution, particularly in 5G networks, due to centralized data processing modes that are resource-intensive and lack timely, accurate, and secure data transmission.
Innovation Solution
Integration of AI/ML entities within communication devices, including management, core network, access network, and terminal devices, to perform AI/ML-related tasks, enabling decentralized and efficient data processing and real-time optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If centralized data processing mode is used in communication systems, then data transmission and processing can be performed, but resource consumption increases and processing efficiency decreases
Solution Approach 1:
The patent segments the centralized data processing function into distributed AI/ML entities deployed across multiple communication devices including terminals, access network devices, and core network devices. Each device runs local AI/ML models to process data locally, eliminating the need to transmit all data to a central processing point, thereby reducing resource consumption and improving processing efficiency.
Solution Approach 2:
The patent introduces a new dimension of processing by integrating AI/ML capabilities directly into communication devices. Instead of traditional centralized processing, the system adds intelligence at the edge devices, transforming the processing architecture from a single-dimension centralized model to a multi-dimension distributed model that operates at different levels of the network hierarchy.
2Reliability
If traditional communication systems process data in real-time, then timely response can be achieved, but accuracy and security of data transmission are insufficient
Solution Approach 1:
The patent implements preliminary action by pre-training and deploying AI/ML models within communication devices before actual data processing occurs. These models are prepared in advance to perform inference operations locally, enabling devices to immediately process and secure data without transmitting it elsewhere, thus maintaining both real-time response and enhanced security through localized intelligent processing.
Solution Approach 2:
The patent introduces AI/ML models as intermediary components between raw data and processing outcomes. These models act as intelligent mediators that locally analyze, filter, and process data within devices, providing accurate real-time responses while maintaining data security by keeping sensitive information within the device rather than transmitting it to external systems.
Data Source
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AI summary
Provided in the embodiments of the present application are a communication device, method and apparatus, a storage medium, a chip, a product, and a program. When the communication device is a management device, the management device includes at least one target AI/ML entity, and each target AI/ML entity is configured to perform an AI/ML related task corresponding to the management device.