Opportunistic GNSS Action Selection Balancing Accuracy and Energy
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Solution Overview
Problem
Existing wireless positioning systems, particularly in 5G NR, face challenges in improving positioning accuracy and energy efficiency of GNSS devices in vehicles due to suboptimal states and high energy consumption.
Innovation Solution
A user equipment (UE) at a vehicle calculates positioning attributes and environmental inputs to determine expected reward values for potential actions on a GNSS device, performing opportunistic actions like soft resets or low-power modes based on these values to optimize GNSS performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the GNSS device operates continuously to maintain positioning accuracy, then positioning accuracy is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts the operational state of the GNSS device between normal mode and low-power mode based on real-time positioning quality assessment. The controller monitors positioning attributes and environmental inputs to determine when to transition between states, optimizing the balance between positioning accuracy and energy consumption.
Solution Approach 2:
The system changes operational parameters of the GNSS device by switching between different processing modes (normal mode with high processing for accurate positioning, and low-power mode with reduced processing for energy saving). This parameter change allows the system to adapt positioning quality to energy constraints.
2Reliability
If the GNSS device performs frequent resets to recover from suboptimal states, then positioning reliability is improved, but CPU usage and energy consumption increase
Solution Approach 1:
The system implements a feedback mechanism where the controller continuously monitors positioning attributes and environmental inputs to assess the current state of the GNSS device. Based on this feedback, the controller determines whether a reset is necessary and selects the appropriate reset type, avoiding unnecessary CPU-intensive reset operations.
Solution Approach 2:
The system provides multiple types of resets with different intensities (soft reset vs. hard reset). Instead of always performing a complete hard reset, the system can apply a softer, less intensive reset when appropriate, reducing CPU usage while still recovering from suboptimal states.
Data Source
AI summary
A user equipment (UE) at a vehicle may receive a set of positioning signals from a set of positioning signal transmission devices (e.g., global positioning satellites (GPSs)). The UE may calculate a set of positioning attributes of the UE using a positioning device (e.g., a global navigation satellite system (GNSS) device) based on the received set of positioning signals. The UE may calculate an initial state of the positioning device based on the calculated set of positioning attributes and a set of environmental inputs associated with the UE. The UE may calculate an expected reward value for each of a set of potential positioning actions for the positioning device based on the calculated initial state. The UE may perform a positioning action of the set of potential positioning actions on the positioning device based on the calculated expected reward value for each of the set of potential positioning actions.