Dynamic Paging Optimization via xApp and RIC
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Solution Overview
Problem
Current paging optimization methods in mobile networks are static and do not adapt to dynamic network conditions and deployment scenarios, leading to inefficiencies in radio resource usage and signaling overheads.
Innovation Solution
Implementing a mechanism using Near-RT RIC and Non-RT RIC to dynamically determine and optimize paging cache timeout, stage 1, and stage 2 timeout values based on real-time data collection and AI/ML algorithms, considering subscriber mobility, network congestion, and time-based patterns, to enhance paging performance across different deployment scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static paging optimization methods are used, then implementation simplicity is maintained, but radio resource usage efficiency deteriorates
Solution Approach 1:
The patent implements dynamic paging optimization by introducing an xApp that continuously monitors network conditions and dynamically adjusts paging parameters such as paging cycle, paging frame, and paging occasion based on real-time traffic patterns and network state, transforming the static configuration into an adaptive system that optimizes radio resource usage
Solution Approach 2:
The patent establishes a feedback mechanism where the xApp collects performance metrics from the RAN node, analyzes them using AI/ML algorithms, and adjusts paging parameters based on the analyzed data, creating a closed-loop system that continuously improves paging efficiency while maintaining manageable complexity
2Loss of energy
If dynamic paging optimization using xApp/rApp is implemented, then radio resource usage efficiency is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary xApp component that sits between the RAN node and the optimization algorithms, handling the complexity of data collection, analysis, and parameter adjustment while presenting a simplified interface to the RAN node, thereby isolating and managing system complexity
Solution Approach 2:
The patent replaces manual configuration and static optimization mechanisms with AI/ML-based automated optimization, allowing the system to adapt to changing conditions without requiring complex manual intervention or reconfiguration
3Adaptability or versatility
If paging parameters are optimized based on real-time data, then adaptability to deployment scenarios is improved, but processing overhead increases
Solution Approach 1:
The patent implements preliminary action by pre-training AI/ML models with historical network data and pre-configuring multiple paging parameter profiles for different deployment scenarios, allowing the xApp to quickly select and apply appropriate parameters without extensive real-time computation
4Measurement precision
If AI/ML algorithms are used for paging optimization, then optimization precision is improved, but computational requirements increase
Solution Approach 1:
The patent applies partial action by using simplified AI/ML models that focus on the most critical paging parameters and deployment scenarios, achieving sufficient optimization precision without the computational burden of comprehensive complex models, thereby balancing precision with computational efficiency
Data Source
AI summary
A method for optimizing parameters affecting Paging Optimization is disclosed, comprising: determining a first paging stage timeout value based on at least one of how much congested is the radio network, and whether paging from a same sub from a same location but paging optimization had already moved to a second stage; determining a second paging stage timeout value based on receiving retransmissions from core network for the same sub within this time; and how many times the subscribers respond later after the timer expiry; and applying a function to the paging cache timeout value, the first paging stage timeout value; and the second first paging stage timeout value to obtain a paging optimization value.