Roaming Network Query Timing for Battery-Efficient Coverage Search
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Solution Overview
Problem
Conventional mobile devices in roaming mode frequently query for better network coverage, leading to excessive battery drainage and wastage of computing resources, especially when the user is unlikely to find a more preferred VPLMN or HPLMN soon.
Innovation Solution
Implementing a variable query interval system based on distance metrics and UE data, incrementally increasing the query timer value when no preferred network is found, up to a maximum, to reduce querying frequency and conserve battery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the UE frequently queries for network coverage in roaming mode, then the UE can find a more preferred VPLMN or HPLMN faster, but battery drainage increases excessively
Solution Approach 1:
The query interval is made dynamic rather than fixed. The system adjusts the query timer value based on the current query count, incrementing it progressively (e.g., from 30 seconds to 1 minute to 2 minutes) as queries continue without finding a preferred network. This dynamic adjustment reduces energy consumption over time while maintaining the ability to discover networks when available.
Solution Approach 2:
The system changes the parameter of query frequency by modifying the query timer value. Instead of maintaining a constant high-frequency query interval, the timer value is increased progressively, thereby changing the effective query frequency parameter to balance network discovery needs with battery conservation.
2Speed
If the UE frequently queries for network coverage, then network discovery speed improves, but computing resource wastage increases
Solution Approach 1:
The system implements periodic querying with increasing intervals. Instead of continuous or fixed-interval querying, the system performs queries at periodic intervals that progressively lengthen (30 seconds, 1 minute, 2 minutes, etc.), reducing the total number of queries required while maintaining adequate network discovery capability.
Solution Approach 2:
The query interval parameter is made dynamic, adjusting based on the number of queries performed. The system transitions from high-frequency initial queries to lower-frequency subsequent queries, optimizing the balance between discovery speed and resource consumption.
3Device complexity
If the query timer value remains fixed, then the querying process is simple to implement, but battery drainage cannot be optimized
Solution Approach 1:
The query timer value transitions from a fixed parameter to a dynamic one that automatically adjusts based on query performance. The system implements a simple incrementing logic (doubling the timer value after each query interval) that adds minimal complexity while achieving significant battery optimization.
Solution Approach 2:
The querying system serves itself by automatically adjusting its own query interval based on its performance. The system monitors its own query count and autonomously modifies the timer value without external intervention, simplifying the overall control architecture while achieving optimization goals.
Data Source
AI summary
Techniques for intelligently setting a query timer value are described herein. For example, when a user equipment (UE) is connected to a visited public land mobile network (VPLMN) (e.g., when the UE is roaming), the UE can determine a distance metric based on the location of the UE’s home public land mobile network (HPLMN) and the VPLMN and/or based on other factors to determine a query timer value. In some examples, the UE can input data to a machine learned model to determine the query timer value. The UE can initiate a querying application based on the query timer value to search for the HPLMN or more preferred VPLMNs. Determining a query timer value in this manner can minimize such querying application and can reduce battery usage for a UE.