Tracking Area Identity List Classification for Wireless Networks
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Solution Overview
Problem
Current wireless network systems face challenges in reducing latency during serving gateway selection and tracking area identity list generation, leading to inefficient resource allocation and increased latency in paging and tracking area updates.
Innovation Solution
The system classifies user equipment based on mobility characteristics and paging loads, generating tailored tracking area identity lists and selecting serving gateways to minimize unnecessary paging and tracking area updates, using DNS queries and graph-based methods to optimize S-GW selection and TAI list allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If user equipment is assigned a large tracking area identity list, then the number of tracking area updates is reduced, but the paging load increases
Solution Approach 1:
The patent applies local quality by classifying user equipment into different categories (high mobility, low mobility, high paging, low paging) and assigning different tracking area identity list sizes to each category. This allows the system to optimize for either reduced tracking area updates or reduced paging load depending on the specific UE characteristics, rather than using a one-size-fits-all approach.
Solution Approach 2:
The system dynamically changes the parameter of tracking area identity list size based on UE classification. The MME records mobility characteristics (number of tracking area changes) and paging load, then adjusts the TAI list size accordingly - high mobility UEs receive larger lists while low mobility UEs receive smaller lists, optimizing overall network performance.
2Loss of time
If the tracking area identity list is generated using traditional methods, then the system is simple to operate, but latency in list generation is increased
Solution Approach 1:
The patent implements preliminary action by pre-calculating and storing optimal tracking area identity lists for different UE categories before actual UE attachment. The MME pre-generates these lists based on historical mobility patterns and network topology, so when a UE attaches, the appropriate pre-computed list can be immediately assigned without calculation latency.
Solution Approach 2:
The system introduces an intermediary mechanism - a classification system that maps UE characteristics to pre-computed TAI lists. Rather than directly calculating optimal lists for each UE, the system uses the classification category as an intermediary to select from pre-prepared lists, significantly reducing generation latency while maintaining optimization benefits.
3Loss of time
If serving gateways are selected using conventional DNS queries, then the selection process is straightforward, but latency in gateway selection is increased
Solution Approach 1:
The patent applies preliminary action by performing DNS queries and establishing serving gateway mappings in advance, before actual user equipment needs service. The system pre-resolves DNS names for tracking areas and caches the resulting serving gateway assignments, so when a UE attaches, the gateway is already identified and no real-time DNS lookup is needed.
Solution Approach 2:
The system introduces a caching mechanism as an intermediary between DNS queries and serving gateway selection. Rather than performing direct DNS lookups for each UE attachment, the system uses a cache of pre-resolved mappings as an intermediary layer, returning cached results when available and only performing DNS queries when necessary, thereby reducing selection latency.
Data Source
AI summary
Some embodiments disclose methods for classifying user equipment in a network, comprising: recording a first number of times a tracking area identity has changed for a user equipment device during a period of time based on messages exchanged between the user equipment device and a mobility management entity; recording a second number of times the user equipment device is paged during a second period of time; classifying the user equipment device into one of at least three categories based on the first and second recorded numbers; generating a tracking area identity list for the user equipment based device on its category; and sending the generated tracking area identity list to the user equipment device.