Grid-Based Multipath Prediction for Wireless Signal Measurement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing multipath prediction methods using ray-tracing are slow and inaccurate, hindering the improvement of communication system performance due to intersymbol interference and signal fading.
Innovation Solution
A communication method and apparatus that utilizes a multipath prediction model trained by data from a communication system to learn multipath rules, deployed on either the network device or terminal device, improving prediction speed and accuracy by using neural networks to infer multipath characteristics in unknown scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ray-tracing simulation method is used to predict multiple paths, then multipath prediction can be performed, but the prediction speed is slow and accuracy is low
Solution Approach 1:
The patent pre-divides the service area into multiple grid cells and pre-calculates propagation paths for each grid cell before actual communication occurs. This preliminary preparation allows the system to quickly retrieve and use pre-computed multipath information during real-time communication, avoiding slow ray-tracing simulations while maintaining prediction accuracy.
Solution Approach 2:
The patent segments the continuous service area into discrete grid cells, and further segments the multipath prediction task into independent calculations for each grid cell. This segmentation allows parallel processing and efficient memory management, improving both prediction speed and accuracy by handling each segment with optimized algorithms.
2Reliability
If ray-tracing method is used to simulate multiple paths, then multipath characteristics can be obtained, but the calculation complexity is high and efficiency is low
Solution Approach 1:
The system performs preliminary ray-tracing calculations to generate propagation paths for each grid cell before actual communication. These pre-calculated paths are stored and reused during real-time operation, significantly reducing calculation complexity while maintaining prediction reliability.
Solution Approach 2:
The patent implements a dynamic multipath prediction system that adapts to changing communication scenarios. The system dynamically selects and combines propagation paths from different grid cells based on current terminal and network device locations, optimizing calculation complexity while maintaining high reliability.
3Measurement precision
If traditional channel measurement is performed repeatedly to obtain multipath characteristics, then accurate multipath information can be obtained, but signaling overhead increases and system performance deteriorates
Solution Approach 1:
The patent pre-calculates and stores multipath characteristics for each grid cell in advance, eliminating the need for repeated channel measurements during communication. This preliminary preparation reduces signaling overhead while maintaining accurate multipath information availability.
Solution Approach 2:
The system creates and stores copies of multipath characteristic data for each grid cell, allowing direct retrieval without repeated measurements. These pre-computed copies are used during actual communication, significantly reducing signaling overhead while maintaining measurement precision.
Data Source
AI summary
A method includes: receiving a first signal from a network device; determining first information, where the first information includes a receiving parameter of the first signal, or the first information includes a sending parameter and a receiving parameter of the first signal, the first information is used to determine a corresponding first grid cell of a path corresponding to the first signal in a preset geographical range, and the preset geographical range is divided into a plurality of grid cells; and sending a measurement result of the first signal to the network device, where the measurement result includes the first information. Data in a communication system is sent to the network device to train a multipath prediction model, so that the multipath prediction model can learn a multipath rule in the communication system.An apparatus is configured to implement or perform the described methods.