Distributed Machine Learning Nodes for Wireless Network Optimization
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Solution Overview
Problem
Current wireless communication systems lack an architecture and protocol for effectively integrating machine learning, leading to inefficient data usage and performance in data-driven solutions, as they do not provide ubiquitous distributed machine intelligence and efficient communication between different machine learning models.
Innovation Solution
A wireless communication system architecture and protocol that includes a central network node and intermediate nodes with machine learning units, enabling prediction of network node performance based on input data and transmitting this information to other nodes, thereby optimizing resource usage and reducing signaling in the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning is integrated into wireless communication systems, then performance and data-driven solutions are improved, but device complexity and network architecture complexity increase
Solution Approach 1:
The patent segments the machine learning functionality into separate network nodes (ML nodes) that can independently execute ML models. This segmentation allows the system to distribute ML workloads across multiple nodes rather than concentrating complexity in a single controller, thereby improving overall system performance while managing architectural complexity through modular design.
Solution Approach 2:
The patent introduces an ML protocol as an intermediary layer that facilitates communication and coordination between ML nodes. This protocol handles the complexity of ML model distribution, execution coordination, and result aggregation, allowing the underlying wireless communication system to benefit from ML capabilities without directly managing the associated complexity.
2Productivity
If machine learning models are distributed across multiple nodes, then productivity and adaptability are improved, but loss of information and communication overhead increase
Solution Approach 1:
The patent extracts only the essential ML-related information (model parameters, execution results, and coordination signals) from the full ML workflow and transmits them through the network. By taking out and transmitting only the critical data elements rather than complete model datasets or raw processing information, the system maintains high productivity while minimizing signaling overhead and information loss.
3Adaptability or versatility
If machine learning is implemented in distributed network nodes, then adaptability and machine intelligence capabilities are improved, but device complexity and energy consumption increase
Solution Approach 1:
The patent implements dynamic ML model selection and execution, where nodes can adaptively choose which ML models to execute based on current network conditions, available resources, and task requirements. This dynamic approach allows the system to maintain high adaptability and machine intelligence capabilities while optimizing energy consumption by avoiding unnecessary ML computations and only executing models when beneficial.
Data Source
AI summary
A wireless communications system and a method therein for handling of machine learning. The system includes a central node and one or more intermediate nodes arranged between the central node and one or more leaf nodes. Further, at least one out of the nodes includes a machine learning unit. The system determines, by means of the machine learning unit and a machine learning model relating to at least one node out of the one or more intermediate nodes or the one or more leaf nodes, a prediction of a performance of the at least one node based on input data relating to the at least one node. Further, the system performs, based on the determined prediction, an operation relating to the at least one node, and communicates the determined prediction and/or information relating to the machine learning model to one or more other nodes.