Idle-Mode UE Sampling for 5G Propagation Model Accuracy
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Solution Overview
Problem
Existing wireless communication technologies face challenges in generating accurate signal propagation models due to insufficient measurements, especially with new frequency bands that are easily blocked and have narrower beams, leading to difficulties in positioning user devices and maintaining network coverage.
Innovation Solution
Collecting and analyzing signal propagation data from user equipment in idle mode, combining it with active mode data to generate more accurate propagation models, using techniques such as machine learning and AI to supplement sparse data, and adjusting antenna resources based on these models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If new shorter wavelength frequency bands are used to provide faster broadband connections, then connection speed is improved, but signal blockage increases and beam coverage decreases
Solution Approach 1:
The system performs preliminary signal measurements and propagation modeling using idle mode user equipment before actual service deployment. By collecting path loss data and generating propagation models in advance, the network can proactively identify coverage areas and adjust antenna resources, preventing service interruptions and ensuring reliable signal delivery when users actually connect.
Solution Approach 2:
The patent introduces a new dimension of data collection by utilizing user equipment in idle mode (a previously underutilized state) for propagation measurements. This adds a temporal and state-based dimension to data gathering, enabling the system to build comprehensive propagation models without impacting active service users, thus maintaining high connection speeds while improving reliability.
2Measurement precision
If propagation models are generated using traditional measurement methods, then model accuracy is limited, but data collection time and cost increase
Solution Approach 1:
Idle mode user equipment is leveraged for dual purposes: maintaining network coverage through normal idle state functions and simultaneously performing propagation measurements. This multi-functional utilization of idle devices enables the system to gather measurement data without requiring dedicated measurement campaigns, significantly reducing data collection time while improving model accuracy through diverse geographic sampling.
Solution Approach 2:
User equipment in idle mode autonomously performs signal measurements and reports propagation data without requiring network intervention or user action. The devices self-organize to collect and transmit measurement information, enabling the network to obtain accurate propagation models passively, thereby minimizing time loss and operational overhead.
3Measurement precision
If more measurement data is collected to improve propagation models, then model accuracy improves, but network overhead and complexity increase
Solution Approach 1:
The system collects measurement data from a selective subset of idle mode user equipment rather than attempting to gather data from all devices. By strategically sampling propagation data from representative idle devices across different geographic locations, the system achieves sufficient model accuracy without the excessive complexity of universal data collection, balancing precision with manageable system complexity.
Data Source
AI summary
The technologies described herein are generally directed to generating a propagation model based on sampling by user equipment in idle mode in a fifth generation (5G) network or other next generation networks. An example method can include, based on a first location in a geographic area of a signal measurement measured by a user equipment in an idle mode, and a transmission location of the signal, estimating an estimated first path loss value. The method can further include, based on the estimated first path loss value and a second path loss value of a second carrier signal received from the carrier signal source, determining an antenna pattern of the carrier signal source. Further, the method can include based on the antenna pattern, generating a propagation model for the carrier signal source.