D-SON RACH Optimization for 5G Network Access
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Solution Overview
Problem
The increasing complexity of 5G network environments due to a vast number of diverse communication devices and data bandwidth demands efficient optimization of random access channels (RACH) to reduce access time and minimize failures, which is challenging for operators to configure manually.
Innovation Solution
Implementing a Distributed Self-Organizing Network (D-SON) management function that sets RACH optimization targets, collects performance data, evaluates optimization performance, and adjusts RACH parameters automatically to achieve optimal performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration of RACH parameters is performed, then operators can control network access performance, but the process becomes costly and time-consuming
Solution Approach 1:
The system implements self-service through automated RACH optimization where the network automatically configures and adjusts RACH parameters based on real-time performance measurements and machine learning algorithms, eliminating the need for manual operator intervention while maintaining optimal access performance
Solution Approach 2:
The system employs feedback mechanisms by continuously measuring RACH performance metrics (access success rate, access delay, collision rate) and using this feedback to automatically adjust RACH parameters through closed-loop control, enabling the network to self-optimize without manual configuration
2Loss of time
If manual RACH optimization is performed, then network access time can be reduced, but operational costs increase
Solution Approach 1:
The automated RACH optimization system performs self-service by using embedded measurement capabilities and machine learning models to automatically reduce network access time without requiring costly manual optimization operations, making the system economically viable for large-scale deployment
Solution Approach 2:
The system replaces manual mechanical configuration processes with automated electronic measurement and control systems, using software-based algorithms to optimize RACH parameters instead of human operators, thereby reducing operational costs while maintaining performance improvements
3Quantity of substance
If the number of communication devices increases, then network capacity is enhanced, but RACH configuration complexity increases
Solution Approach 1:
The system handles increased device complexity through self-service automation, where the network automatically adapts RACH parameters based on real-time measurements from the actual device population, eliminating the need for operators to manually manage the complexity associated with large numbers of diverse devices
Solution Approach 2:
The system manages configuration complexity by dynamically changing RACH parameters based on network conditions and device density measurements, using automated algorithms to adjust parameters such as preamble formats, time offsets, and resource allocations without requiring complex manual configuration for each device type
Data Source
AI summary
Systems and methods of supporting RACH optimization and monitoring of UP packet delay performance are described. During RACH optimization, a NF provisioning MnS with modify MOIAttributes operation to configure targets for RACH optimization and a NF provisioning MnS with modifyMOIAttributes operation are separately consumed to enable a RACH optimization function for a NR cell. After this, a performance assurance MnS with notifyFileReady or reportStreamData operation is consumed to collect RACH optimization-related measurements for the NR cell and RACH performance data of the RACH optimization-related measurements analyzed to evaluate RACH optimization performance for the NR cell. During monitoring of UP packet delay performance, raw performance measurements related to UP packet delay based on at least one of NG-RAN measurement results or time stamps in GTP packets are obtained from a NG-RAN or UPF, UP packet delay performance measurements are generated. UP packet delay performance could be optimized based on the performance measurements.