Feature-Specific RACH Self-Optimization for 5G Random Access
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Solution Overview
Problem
Existing wireless communication systems face challenges in optimizing random access procedures, particularly for features like slice groups, msg3 repetitions, and reporting of additional parameters, which are crucial for efficient communication in 5G and beyond networks.
Innovation Solution
A method and system for self-optimization of random access channel (RACH) in wireless communication systems, involving a UE and network apparatus that detect and report feature-specific RACH information, including parameters such as msg3 repetitions, network slicing, and Reduced Capacity (Redcap), to optimize network parameters for efficient communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual optimization methods are used for RACH parameters, then implementation simplicity is maintained, but optimization precision and adaptability deteriorate
Solution Approach 1:
The system enables self-service optimization where the network device automatically collects RACH failure information from multiple sources (RACH failure cause indicators, failure counters, beam failure information), analyzes this data, and adjusts RACH parameters without manual intervention. This self-optimizing mechanism resolves the contradiction by achieving high optimization precision through automated collection and analysis of failure information, while maintaining implementation simplicity through standardized procedures.
Solution Approach 2:
The patent implements feedback mechanisms where RACH failure information is continuously collected from UEs and network devices, analyzed to identify optimization opportunities, and used to adjust RACH parameters. This closed-loop feedback system achieves precise optimization by incorporating real-world performance data while managing complexity through structured feedback collection and analysis procedures.
2Measurement precision
If comprehensive RACH information collection is implemented, then optimization accuracy improves, but information processing complexity increases
Solution Approach 1:
The patent segments RACH information collection into distinct categories: RACH failure cause indicators (radio conditions, interference, congestion), failure counters (number of failures, success rates), and beam failure information. This segmentation allows comprehensive data collection for accurate optimization while managing processing complexity through organized, modular information gathering and analysis.
3Productivity
If feature-specific RACH optimization is applied, then communication efficiency for specific features improves, but system complexity increases
Solution Approach 1:
The patent applies local quality optimization by identifying specific features (network slicing, msg3 repetitions, Redcap) and collecting feature-specific RACH failure information. The system then applies targeted optimizations for each feature based on its unique failure patterns and requirements. This resolves the contradiction by achieving high communication efficiency for specific features through tailored optimization while managing overall system complexity through modular, feature-specific processing.
Data Source
AI summary
The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. The method for self-optimization of RACH includes storing a feature specific RACH information corresponding to a feature specific RACH applied by the UE for one of multiple features of a plurality of features and a specific feature of the plurality of features. Further, the method includes receiving an information request message from a network apparatus. Further, the method includes including the feature specific RACH information corresponding to the feature specific RACH applied by the UE for one of the multiple features and the specific feature in an information response message for self-optimization of the RACH. Further, the method includes sending the information response message includes the feature specific RACH information corresponding to the feature specific RACH applied by the UE to the network apparatus in the wireless network.