Machine-Learned Paging Area Selection for Lower Wireless Paging Load
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Solution Overview
Problem
Existing wireless communication systems face challenges in reducing the number of paging messages and improving the paging success rate, particularly in 5G networks, which consume significant network resources and can lead to increased latency.
Innovation Solution
A machine learning algorithm is employed to derive an optimal paging area by analyzing mobility data from multiple terminals, determining the reliability of the derived area, and transmitting paging messages to base stations where the terminals are likely to move, thereby minimizing the number of unnecessary paging attempts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If paging messages are transmitted to multiple base stations to improve paging coverage, then paging success rate is improved, but paging load and network resource consumption increase
Solution Approach 1:
The patent changes the parameter of paging area configuration by using machine learning to dynamically determine the optimal paging area size and scope based on terminal mobility patterns, rather than using fixed or statically configured paging areas. This allows the system to transmit paging messages to the precise number of base stations needed, improving success rate while minimizing message quantity.
Solution Approach 2:
The patent replaces the traditional mechanical/manual configuration of paging areas with an AI-based machine learning system that automatically analyzes mobility data and determines optimal paging areas. This substitution enables dynamic optimization of paging message transmission without manual intervention, resolving the contradiction between coverage and resource usage.
2Area of stationary object
If paging area is expanded to cover more base stations, then paging coverage is improved, but latency increases due to more paging attempts
Solution Approach 1:
The patent dynamically changes the paging area parameter based on terminal mobility characteristics learned from historical data. By adjusting the paging area size and scope according to actual mobility patterns rather than using fixed large areas, the system achieves adequate coverage with fewer base stations, thereby reducing paging latency.
Solution Approach 2:
The patent performs preliminary analysis of mobility data using machine learning to predict the likely paging area before actual paging occurs. This preliminary action enables the system to pre-determine the optimal set of base stations for paging, avoiding the need to expand coverage unnecessarily and reducing the time required for paging attempts.
3Quantity of substance
If machine learning algorithm is applied to optimize paging area, then paging load is reduced, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary machine learning model that acts as a mediator between raw mobility data and paging decisions. This intermediary component processes the complex patterns in mobility data and outputs simplified paging area recommendations, reducing the complexity burden on the core paging system while achieving load reduction.
Solution Approach 2:
The patent uses mobility data from multiple terminals as training copies to create a generalized machine learning model. By learning from copied patterns in historical mobility data rather than processing each individual case, the system reduces computational complexity while maintaining effective paging optimization.
Data Source
AI summary
A method for identifying a base station to which a network node is to transmit a paging message in a wireless communication system is provided. The method includes receiving, from multiple terminals registered with an access and mobility management function (AMF) entity, mobility data related to movement frequencies of the multiple terminals, based on a learning algorithm trained to derive a candidate paging area based on the mobility data, and mobility data for a first period among the mobility data, deriving a first candidate paging area, determining reliability of the derived first candidate paging area based on test data for the first period, identifying another base station to receive a paging message transmitted from the network node, and transmitting, to the at least one base station, the paging message, wherein the test data for the first period comprises mobility data related to the multiple terminals after the first period.