Adaptive Roaming Network Query Timing for Lower UE Battery Drain
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Solution Overview
Problem
Conventional wireless communication devices experience significant battery drainage due to frequent querying operations when searching for more preferred VPLMNs or HPLMNs while roaming, leading to inefficient use of computing resources and increased power consumption.
Innovation Solution
Implementing a system that varies query intervals 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 unnecessary querying and conserve battery life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the UE performs frequent querying operations to search for more preferred VPLMNs or HPLMNs while roaming, then the network coverage availability is improved, but the battery consumption increases significantly
Solution Approach 1:
The patent applies dynamics by making the query interval adaptive rather than fixed. The system dynamically adjusts the querying frequency based on multiple factors including distance to HPLMN, movement speed, signal strength, and time since last query. This dynamic adjustment allows the UE to maintain adequate network coverage monitoring while consuming less battery power by reducing queries when conditions indicate low likelihood of finding a better network.
Solution Approach 2:
The patent changes the parameter of query interval from a constant value to a variable parameter that adjusts based on environmental conditions. By modifying parameters such as distance metric, velocity, signal strength, and time elapsed, the system optimizes the balance between network coverage availability and battery consumption, querying more frequently when conditions favor finding a better network and less frequently when conditions suggest the current network is adequate.
2Reliability
If the UE performs frequent querying operations to search for more preferred networks, then the network service quality is improved, but the computing resource usage increases
Solution Approach 1:
The system dynamically adjusts querying frequency based on computed parameters including distance to HPLMN, movement velocity, and signal strength metrics. By making the query interval adaptive, the system reduces unnecessary computing operations while maintaining adequate network service quality monitoring, thus lowering overall computing resource consumption.
Solution Approach 2:
The patent implements partial action by performing querying operations selectively rather than continuously. The system determines a probability metric for finding a better network and only initiates queries when this probability exceeds a threshold or when conditions warrant it, avoiding excessive querying operations that would consume unnecessary computing resources while still maintaining adequate network service quality.
3Ease of operation
If the UE uses a fixed query interval to search for networks, then the querying simplicity is maintained, but the battery life is reduced due to unnecessary queries
Solution Approach 1:
The patent transitions from a static fixed query interval to a dynamic adaptive interval that responds to changing conditions. The system calculates an optimal query interval based on distance to HPLMN, movement speed, signal strength, and time since last query, automatically adjusting the timing of network searches to minimize battery consumption while maintaining adequate coverage monitoring.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring parameters such as distance to HPLMN, signal strength, and query results, then using this feedback to adjust future query intervals. This feedback loop allows the system to learn from past queries and optimize battery usage by reducing queries when conditions indicate low likelihood of success while maintaining simplicity through automated decision-making.
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.