ML Orchestrator Parameter Initialization in Wireless Networks
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Solution Overview
Problem
Existing ML/RL-based solutions for wireless communication networks do not provide an efficient method for initializing and re-initializing ML agent parameters, which is crucial for optimal performance and adaptability to changing radio conditions.
Innovation Solution
An ML orchestrator entity is introduced to group network nodes into clusters based on radio conditions and to generate a common set of parameters by combining the training results from each node cluster, facilitating efficient parameter initialization and re-initialization for ML agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ML agents are initialized with parameters trained individually at each network node, then each node can adapt to its specific radio conditions, but the initialization process becomes complex and time-consuming
Solution Approach 1:
The patent combines training results from multiple network nodes by aggregating parameter sets from different nodes into a common parameter set. This merging approach reduces individual node complexity while maintaining collective intelligence, allowing each node to benefit from aggregated learning without performing extensive individual training.
Solution Approach 2:
The patent performs preliminary training at multiple network nodes before deployment, storing parameter sets that can be later combined and distributed. This preliminary action prepares the system in advance, reducing the complexity of real-time initialization when ML agents are first deployed or re-initialized.
2Measurement precision
If ML agents undergo extensive training to achieve optimal performance, then accuracy improves, but the time required for initialization and re-initialization increases
Solution Approach 1:
The patent performs training in advance at multiple network nodes and stores the resulting parameter sets for later use. This preliminary training action allows the system to have pre-computed optimized parameters ready for rapid deployment, reducing the time loss during actual initialization while maintaining high accuracy.
Solution Approach 2:
The patent creates parameter sets that can be copied and distributed from trained nodes to other nodes. Instead of re-training each node individually, the system copies proven parameter sets, significantly reducing initialization time while preserving the accuracy benefits of extensive training.
3Stability of the object's composition
If a centralized parameter generation approach is used, then parameter consistency across nodes is improved, but the system loses flexibility and adaptability to local conditions
Solution Approach 1:
The patent allows each network node to contribute its locally-trained parameter sets to the common set, preserving local adaptations while achieving overall consistency. Each node's unique radio condition knowledge is embedded in the aggregated parameters, maintaining local quality while ensuring system-wide stability.
Solution Approach 2:
The patent creates a common parameter set that serves multiple network nodes simultaneously, making the parameter initialization process universal across the network. This common set can be adapted to different nodes' needs while maintaining consistency, achieving both universality and local adaptability.
4Adaptability or versatility
If ML agents are re-initialized frequently to adapt to changing radio conditions, then adaptability improves, but the overhead of continuous training and parameter updates increases
Solution Approach 1:
The patent merges parameter updates from multiple nodes into a single common parameter set, reducing the computational overhead of frequent re-initializations. Instead of each node independently re-training, the system combines updates efficiently, lowering energy consumption while maintaining adaptability to changing conditions.
Solution Approach 2:
The patent implements a feedback mechanism where training results from multiple nodes are continuously aggregated and distributed back to the network. This feedback loop enables adaptive re-initialization based on actual performance and changing conditions, optimizing the balance between adaptability and computational overhead.
Data Source
AI summary
The present disclosure relates to a machine-learning (ML) orchestrator entity that provides distributed, flexible, and efficient parameter initialization for ML agents installed on network nodes operating under similar radio conditions. For this end, the ML orchestrator entity instructs two or more of the network nodes to run two or more ML agents in a training mode, which results in generating two or more sets of parameters. Then, the ML orchestrator entity uses the sets of parameters to derive a common set of parameters for the network nodes. The common set of parameters is to be used in an inference mode of the ML agent at each of the network nodes. The transmission of the common set of parameters to the network nodes may be subsequently initiated by the ML orchestrator entity itself or by each of the network nodes independently.