Adaptive Mobility Measurement Thresholds in Cellular Networks
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Solution Overview
Problem
Existing communication networks face challenges in optimizing cell reselection processes due to static measurement threshold settings, leading to excessive battery consumption, decreased serving cell quality, and increased likelihood of devices going out of service, while manual threshold configuration is costly and inefficient.
Innovation Solution
A method where mobile devices determine whether to perform mobility measurements based on bias and selection quality parameters received from the network, using predefined relationships to decide on the necessity of measurements, thereby reducing unnecessary battery drain and improving cell reselection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If measurement threshold is set too high, then device can save battery power, but serving cell quality decreases and device may go out of service
Solution Approach 1:
The patent applies dynamics by making the measurement threshold adaptive rather than static. The network dynamically adjusts the threshold based on actual serving cell quality conditions, allowing the threshold to change over time and space. This resolves the contradiction by enabling the system to raise thresholds when quality is good (saving battery) while lowering them when quality deteriorates (maintaining reliability).
Solution Approach 2:
The patent implements feedback mechanisms where the network monitors serving cell quality metrics and uses this information to adjust measurement thresholds. The network receives quality reports from devices and automatically optimizes thresholds accordingly, creating a closed-loop control system that balances battery savings with service reliability.
2Reliability
If measurement threshold is set too low, then serving cell quality is maintained, but battery consumption increases and standby time shortens
Solution Approach 1:
The system dynamically adjusts measurement thresholds based on real-time network conditions and device states. Rather than using a fixed low threshold that continuously triggers measurements, the threshold adapts to current serving cell quality, enabling higher thresholds (and thus lower power consumption) when conditions permit while maintaining quality when needed.
Solution Approach 2:
The patent changes the measurement threshold parameter from a static value to a dynamically adjustable parameter. The network modifies this parameter based on observed serving cell quality metrics, allowing the system to optimize the balance between measurement frequency (affecting battery life) and quality monitoring (affecting reliability) under different operating conditions.
3Reliability
If measurement parameters are not configured, then device performs measurements all the time, but this increases battery consumption excessively
Solution Approach 1:
The patent extracts the essential measurement functionality while removing unnecessary continuous measurements. By configuring selective measurement parameters, the system maintains adequate mobility measurement coverage for reliability while eliminating redundant measurements that would excessively drain battery power.
Solution Approach 2:
Instead of performing measurements all the time (excessive action), the system performs measurements partially - only when configured parameters indicate it is necessary. This partial action approach maintains sufficient measurement coverage for reliability while significantly reducing overall power consumption compared to continuous measurements.
4Reliability
If manual threshold configuration is performed, then network performance can be optimized, but field tests are time-consuming and costly
Solution Approach 1:
The patent implements self-service by enabling the network to automatically configure and optimize measurement thresholds without requiring manual field testing. The system uses automated algorithms to analyze network conditions and adjust parameters, allowing network performance optimization to occur autonomously, thereby eliminating time-consuming and costly manual field test procedures.
Solution Approach 2:
The system automatically changes measurement threshold parameters based on real-time network monitoring and analysis, replacing manual parameter configuration. This automated parameter optimization maintains network performance while eliminating the need for time-consuming field tests, as the system adapts parameters dynamically based on observed conditions rather than relying on pre-field-test configurations.
Data Source
AI summary
Methods, device and apparatus for use in a cellular network are disclosed. An example method disclosed herein comprises: receiving, via the cellular network, a bias parameter of a neighbor cell; and determining based on the bias parameter whether or not to perform at least one mobility measurement.