5G Channel Identification via Cross-Correlation Feature Vectors
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Solution Overview
Problem
Existing methods for high-resolution speed measurement in 5G communication networks using Rayleigh channels suffer from low accuracy due to their inability to effectively identify user mobility and utilize spatial domain resources efficiently.
Innovation Solution
A channel identification method that acquires channel data from terminals, constructs a feature vector based on cross-correlation values between channel data at different time intervals, and inputs this vector into a prediction model to predict terminal speeds or cluster speed types, thereby enabling adaptive transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional Rayleigh channel based speed measurement is used, then the measurement process is simple, but the measurement precision is low
Solution Approach 1:
The patent segments the channel identification process into distinct functional modules: channel data acquisition module, feature vector construction module, and speed prediction module. This segmentation allows each module to specialize in specific tasks, improving overall measurement precision while maintaining manageable system complexity through modular design.
Solution Approach 2:
The patent transitions from conventional single-dimensional speed measurement to multi-dimensional channel feature analysis. By constructing feature vectors that incorporate cross-correlation values across multiple time intervals and spatial domains, the system achieves higher measurement precision by utilizing additional dimensional information from the channel data.
2Productivity
If Massive MIMO technology is applied to achieve orthogonality between multi-user channels, then spectrum efficiency is improved, but the demand for user mobility identification increases
Solution Approach 1:
The patent performs preliminary channel identification and speed measurement before resource allocation and scheduling decisions. By constructing feature vectors and predicting speeds in advance, the system prepares mobility identification results that enable more effective Massive MIMO resource utilization, thereby improving spectrum efficiency while managing the complexity of user mobility detection.
Solution Approach 2:
The patent implements a feedback mechanism where predicted speed information is used to adjust and optimize Massive MIMO channel allocation. The speed prediction results feed back into the resource allocation process, enabling dynamic adaptation to user mobility patterns and improving both spectrum efficiency and mobility identification accuracy.
3Measurement precision
If cross-correlation values at multiple time intervals are analyzed, then speed measurement accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent extracts only the most relevant features from the channel data by constructing feature vectors that selectively incorporate cross-correlation values at specific time intervals. This extraction approach maintains high measurement precision by focusing on critical temporal relationships while reducing computational power consumption by avoiding processing of redundant information.
Data Source
AI summary
Provided is a channel identification method. The method includes: acquiring channel data of a terminal; constructing a first feature vector based on the channel data, where the first feature vector represents a numerical value set of cross-correlation values, which change along with time intervals, between channel data at different moments and the channel data themselves and between the channel data at different moments and subsequent channel data at different time intervals; and inputting the first feature vector into a predetermined prediction model to predict a speed of the terminal, or to predict a speed type of a cluster to which the terminal belongs. Further provided are an adaptive transmission method and apparatus based on the channel identification method, a transmission device, a base station, and a computer storage medium.