ML Embeddings for UE Infrastructure Vendor Identification
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Solution Overview
Problem
Existing wireless communication systems, particularly 5G NR, face challenges in efficiently adapting to different infrastructure vendors due to varying scheduling algorithms and radio access network configurations, leading to inconsistent link level behaviors and inaccurate capability advertisement by user equipment (UEs).
Innovation Solution
User equipment (UEs) utilize machine learning models to infer infrastructure vendors based on link level protocol interactions, creating representations of network nodes to enhance communication efficiency and capability advertisement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If UEs use traditional communication methods without ML-based infrastructure identification, then device complexity is low, but adaptability to different infrastructure vendors is poor and capability advertisement is inaccurate
Solution Approach 1:
The patent introduces an embedding engine as an intermediary component that translates infrastructure vendor-specific characteristics into standardized embedding representations. This mediator enables UEs to understand and adapt to different infrastructure vendors without requiring complex vendor-specific processing logic in the UE itself, thus improving adaptability while controlling device complexity.
Solution Approach 2:
The patent replaces traditional mechanical communication adaptation mechanisms with machine learning-based embedding models. Instead of using complex protocol parsing and vendor-specific configuration handling, the system uses ML embeddings to represent infrastructure characteristics, enabling more efficient and accurate adaptation with reduced computational complexity in the UE.
2Measurement precision
If UEs collect and process link level protocol interaction data to create embeddings, then measurement precision of infrastructure characteristics improves, but loss of time increases due to data processing requirements
Solution Approach 1:
The patent implements preliminary action by pre-defining the embedding engine and pre-establishing the framework for collecting link level protocol interaction data. The embedding models are prepared in advance to process this data, allowing for efficient extraction of infrastructure characteristics without requiring complex real-time processing, thus improving measurement precision while minimizing time loss.
3Reliability
If UEs perform accurate capability advertisement based on embedded representations, then reliability of communication improves, but device complexity increases due to additional processing requirements
Solution Approach 1:
The embedding engine serves as an intermediary that handles the complex task of generating and processing capability advertisements based on infrastructure embeddings. By offloading this complex processing to the embedding engine, the UE can achieve reliable capability advertisement without significantly increasing its own processing complexity.
Data Source
AI summary
Apparatus, methods, and computer program products for wireless communication are provided. An example method may include collecting a set of observation data associated with at least one network node. The example method may further include feeding the set of observation data to an embedding engine. The example method may further include receiving, from the embedding engine, a set of embeddings, the set of embeddings being a set of representations of one or more characteristics of a set of network nodes including the at least one network node. The example method may further include communicating with a second network node based on the set of embeddings.