CSI Parameter Selection Using Neural Networks to Cut MIMO Feedback
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Solution Overview
Problem
As the number of antennas increases in MIMO wireless communication, the pilot and channel state feedback information also increases, leading to a decrease in the actual effective data transmission rate.
Innovation Solution
A method involving acquiring sampling data at a target location point, determining a target data stream number and a target precoding matrix using a neural network model, and calculating a channel quality indicator to infer channel state information without feedback, thereby avoiding the increase in feedback information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the quantity of antennas is increased to improve signal quality and transmission capacity, then the MIMO transmission capability is enhanced, but the pilot and channel state feedback information increases, resulting in a decrease in the actual effective data transmission rate
Solution Approach 1:
The patent extracts and selects only the most critical channel state parameters (precoding matrix indicator and channel quality indicator) from the complete channel state information, discarding redundant information. This extraction approach reduces feedback volume while maintaining essential transmission quality, directly resolving the contradiction between transmission capacity and feedback overhead.
Solution Approach 2:
Instead of uniformly processing all antenna elements, the patent applies selective sampling at specific location points within the base station coverage area. By focusing computational resources on critical spatial locations and selecting parameters locally rather than globally, the system reduces overall feedback requirements while maintaining effective transmission quality where it matters most.
2Reliability
If the quantity of antennas is increased to improve signal quality, then the MIMO transmission capability is enhanced, but the pilot and channel state feedback information increases, resulting in a decrease in the actual effective data transmission rate
Solution Approach 1:
The patent extracts and selects only the most critical channel state parameters (precoding matrix indicator and channel quality indicator) from the complete channel state information, discarding redundant information. This extraction approach reduces feedback volume while maintaining essential transmission quality, directly resolving the contradiction between transmission capacity and feedback overhead.
Solution Approach 2:
The patent uses neural network models to generate accurate copies or estimates of channel state parameters at unsampled location points based on data from sampled points. This copying approach allows the system to maintain reliable signal quality across the entire coverage area while only collecting feedback from selective location points, thereby reducing overall feedback volume.
3Loss of information
If traditional channel state feedback methods are used, then complete channel information is provided to the base station, but the feedback overhead increases with the number of antennas, reducing effective data transmission rate
Solution Approach 1:
The patent extracts and selects only the most critical channel state parameters (precoding matrix indicator and channel quality indicator) from the complete channel state information, discarding redundant information. This extraction approach reduces feedback volume while maintaining essential transmission quality, directly resolving the contradiction between transmission capacity and feedback overhead.
Solution Approach 2:
The patent uses neural network models to generate accurate copies or estimates of channel state parameters at unsampled location points based on data from sampled points. This copying approach allows the system to maintain reliable signal quality across the entire coverage area while only collecting feedback from selective location points, thereby reducing overall feedback volume.
Data Source
AI summary
Disclosed are a method, an apparatus and a device for selecting parameters of channel state information, and a storage medium. The method includes: acquiring sampling data at a target location point at different times, the target location point is a location point of a parameter to be selected, and the location point that needs to be sampled among geographical location points within a base station coverage area; determining a target data stream number of the target location point based on the sampling data; determining a target precoding matrix of the target location point based on the target data stream number and a preset neural network model; and determining a channel quality indicator of the target location point based on the target precoding matrix, the data stream number, the precoding matrix, and the channel quality indicator are parameters of channel state information.