Cellular Network Location Prediction for UE Power Reduction
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Solution Overview
Problem
Current cellular radio networks face inefficiencies in user equipment cell assignment and reselection, leading to increased network traffic and power consumption due to frequent measurements and handovers, especially in high mobility scenarios.
Innovation Solution
A method that uses predicted location information of user equipment to optimize network resource utilization by determining control information for cell selection and monitoring rates, reducing the need for frequent handovers and measurements, and minimizing power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the user equipment constantly measures quality metrics and performs frequent cell reselections to maintain optimal connection, then the connection quality and network responsiveness are improved, but the power consumption increases and battery operating time decreases
Solution Approach 1:
The network determines predicted location information and control information in advance, allowing the user equipment to perform measurements only when necessary (e.g., when entering a predicted area), rather than constantly. This preliminary preparation reduces the frequency of measurements and power consumption while maintaining connection quality.
Solution Approach 2:
The measurement frequency and cell reselection behavior are dynamically adjusted based on predicted location information and control information from the network. Instead of constant high-frequency measurements, the system adapts the measurement parameters (frequency, threshold) according to the predicted movement pattern, reducing power consumption while maintaining reliability.
2Reliability
If the user equipment performs frequent metric measurements and cell reselections to maintain optimal connection, then the connection quality is improved, but the network traffic in the control plane increases
Solution Approach 1:
The network proactively determines predicted location information and sends control information to the user equipment in advance. This allows the network to anticipate and prepare for potential cell changes, reducing the need for frequent reactive signaling and control plane traffic that would otherwise occur with constant measurements and reselections.
Solution Approach 2:
The system uses predicted location information as feedback to optimize measurement and reselection behavior. By incorporating location predictions into the control information, the network can guide the user equipment to perform measurements only when relevant, thereby reducing unnecessary control plane traffic while maintaining connection quality.
3Speed
If the user equipment monitors multiple cells frequently to enable quick reselection, then the cell reselection speed and connection reliability are improved, but the computing power consumption and processing load increase
Solution Approach 1:
Instead of uniformly monitoring all neighboring cells with equal frequency, the system uses predicted location information to identify which cells are relevant to monitor. The control information guides the user equipment to focus measurements on specific cells in the predicted movement direction, reducing the overall measurement load and computing power consumption while maintaining fast reselection capability for the relevant cells.
Solution Approach 2:
The network determines predicted location information and control information in advance, allowing the user equipment to pre-identify which cells need to be monitored. This preliminary identification reduces the real-time computing burden during measurement and reselection, as the device already knows which cells are relevant based on the predicted trajectory.
4Adaptability or versatility
If the user equipment is defined as high mobility based on reselection history and performs more frequent measurements, then the adaptability to movement is improved, but the power consumption and operating time are reduced
Solution Approach 1:
The measurement frequency and reselection behavior are dynamically adjusted based on predicted location information rather than solely on historical reselection patterns. This allows the system to adapt to movement when necessary (maintaining adaptability) while avoiding excessive measurements in scenarios where the predicted trajectory indicates stability or predictable movement, thereby extending battery operating time.
Solution Approach 2:
The network determines predicted location information in advance and provides control information that guides measurement frequency. This preliminary determination allows the system to adapt to movement patterns proactively rather than reactively, reducing the need for frequent measurements and extending battery life while maintaining appropriate adaptability to actual movement conditions.
Data Source
AI summary
The present invention relates to a method for operating a cellular radio network (100). According to the method, a predicted location information (203) relating to a user equipment (110) is determined. The predicted location information (203) comprises a predicted future location of the user equipment (110). Depending on the predicted location information (203) control information is determined. Based on the control information, a registering of the user equipment (110) at cells (101, 101A-101H) of the cellular radio network (100) is controlled.