Radio Access Network Measurement Collection for Real-Time Optimization
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Solution Overview
Problem
The vast amount of data collected for network optimization is challenging to analyze and process efficiently, necessitating improved data collection methods for radio access networks.
Innovation Solution
An apparatus and method for configuring measurement collection with specific requests for optimization problems, including time sampling and granularity, to provide tailored measurement results for machine learning algorithms, enabling real-time or near-real-time data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive measurement data is collected for network optimization, then network optimization capability is improved, but data processing complexity and time consumption increase
Solution Approach 1:
The patent extracts only the necessary measurement parameters from comprehensive data collection. The measurement request specifies particular measurements needed for optimization problems, and the measurement response filters and transmits only those specific parameters, not all available data. This extraction approach reduces data volume and processing time while maintaining optimization effectiveness.
Solution Approach 2:
The patent segments the measurement collection process into distinct components: measurement configuration, measurement execution, data filtering, and selective transmission. The measurement request is divided into specific parameter requests, and the measurement response is segmented to transmit only relevant data. This segmentation enables efficient processing by handling data in manageable portions rather than processing entire datasets at once.
2Measurement precision
If measurement data is collected with high granularity and frequent sampling, then measurement precision is improved, but data volume and processing burden increase
Solution Approach 1:
The patent applies local quality by customizing measurement parameters according to specific optimization problems. Different measurement granularities and sampling frequencies are selected based on the local requirements of each optimization task rather than uniformly applying high granularity throughout. This allows high precision where needed while reducing data volume where lower precision suffices.
Solution Approach 2:
The patent dynamically adjusts measurement parameters including granularity and sampling frequency based on the specific optimization problems being solved. The measurement request specifies parameter changes tailored to each optimization task, and the measurement response reflects these parameter adjustments. This parameter adaptation enables precision optimization without proportionally increasing data volume.
3Productivity
If tailored measurement requests are implemented for specific optimization problems, then data processing efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal measurement request-response framework that can handle multiple optimization problems through a standardized interface. The same basic mechanism of sending measurement requests with specific parameters and receiving filtered responses works across different optimization scenarios. This universality reduces system complexity by using a single paradigm for diverse tasks rather than requiring separate specialized systems for each optimization problem.
Data Source
AI summary
There is provided an apparatus comprising means for: configuring a measurement collection for one or more pre-determined optimization problems at a first data producer; transmitting a first measurement request to the first data producer, wherein the first measurement request is indicative of requested measurement(s) for the one or more pre-determined optimization problems, time sampling indication and granularity; receiving a measurement response comprising first measurement results corresponding to the first measurement request; and providing the first measurement results to one or more optimization algorithms for solving the one or more pre-determined optimization problems.