5G Multi-Beam Fingerprint Positioning via Neural Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current indoor positioning technologies, such as WIFI, Bluetooth, and Ultra-Wide Band, struggle to achieve large-scale coverage and high precision navigation in complex indoor environments, limiting their application in meeting the needs of mobile smart terminal positioning.
Innovation Solution
A method and system for fingerprint positioning based on 5G multi-beam downlink signals, which utilizes the characteristics of commercial 5G multi-beam signals to construct multi-characteristic and multi-beam fingerprints, enabling high precision positioning without the need for additional positioning base stations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional indoor positioning technologies (WIFI, Bluetooth, Ultra-Wide Band) are used, then positioning can be achieved in indoor environments, but large-scale coverage and high precision navigation cannot be simultaneously achieved
Solution Approach 1:
The patent applies 5G downlink signals for both communication and positioning functions simultaneously. The 5G base station serves dual purposes: providing high-speed communication and enabling indoor positioning through its existing infrastructure, eliminating the need for separate positioning base stations and achieving both wide coverage and high precision.
Solution Approach 2:
The patent utilizes the large bandwidth parameter of 5G downlink signals to achieve high positioning precision. By leveraging the substantial bandwidth resources available in 5G networks, the system can extract precise timing and frequency information needed for accurate positioning while maintaining wide area coverage through the existing 5G network deployment.
2Ease of manufacture
If 5G single base station scenarios are used, then deployment is simplified, but geometric positioning cannot be formed
Solution Approach 1:
The patent segments the 5G downlink signal into multiple beams directed toward different locations. Each beam acts as an independent positioning reference, allowing the system to form geometric positioning relationships even with a single base station. The terminal measures parameters from multiple beams to calculate its position, achieving both deployment simplicity and positioning capability.
Solution Approach 2:
The patent introduces the spatial beam direction dimension to positioning. Instead of relying on multiple base stations in different locations, the system uses multiple beams from a single base station that are directed toward different spatial directions. This dimensional approach allows geometric positioning to be formed through beam spatial distribution rather than base station spatial distribution.
3Measurement precision
If additional positioning base stations are deployed to achieve high precision positioning, then positioning accuracy improves, but system complexity and cost increase
Solution Approach 1:
The patent makes the 5G downlink signal serve dual functions: communication and positioning. By extracting positioning information from the existing 5G communication signals, the system eliminates the need for separate positioning infrastructure, thereby achieving high positioning accuracy without increasing system complexity or deployment cost.
Solution Approach 2:
The 5G network infrastructure serves itself by utilizing its own downlink signals for positioning purposes. The existing 5G base stations and signals automatically provide positioning services without requiring additional dedicated positioning equipment, achieving cost-effective high-precision positioning through self-service utilization of existing resources.
Data Source
AI summary
A method and system for fingerprint positioning based on 5G multi-beam downlink signals, includes: extracting multi-beam SSB from 5G downlink signals, and obtaining multi-beam SSS signal and DM-RS signal from multi-beam SSB; calculating multi-beam reference signal received power RSRP based on multi-beam SSS signal and DM-RS signal; calculating the RSSI of 5G downlink synchronization channel based on multi-beam SSB autocorrelation, and calculating the multi-beam reference signal received quality RSRQ based on multi-beam RSRP and RSSI; stacking the multi-beam RSRP and RSRQ to form multi-beam fingerprint features; inputting multi-beam fingerprint characteristic into neural networks, and the neural networks output positioning results.