Massive MIMO Vector Updates for Accurate Downlink Precoding
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Solution Overview
Problem
Existing Massive MIMO technologies face significant errors in precoding matrices due to insufficient sparsity in space and frequency domain information of uplink and downlink channels, leading to suboptimal signal quality and spectrum utilization.
Innovation Solution
A method for updating space and frequency domain vectors using statistical information from downlink channels to correct precoding matrices, enhancing sparsity and improving matching with actual channel conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If precoding matrix is constructed using space domain vector and frequency domain vector obtained based on reciprocity between uplink and downlink channels, then device complexity is reduced, but manufacturing precision (accuracy of precoding matrix) deteriorates due to very large error
Solution Approach 1:
The patent introduces downlink channel statistical information as an intermediary to bridge the gap between uplink and downlink channels. The terminal device obtains downlink channel statistics (such as spatial covariance matrix) and feeds back updated space domain vectors to the network device, which then corrects the precoding matrix. This intermediary statistical information enables accurate precoding without requiring direct downlink channel measurement, thus reducing complexity while improving accuracy.
Solution Approach 2:
The patent implements a feedback mechanism where the terminal device updates the space domain vectors based on downlink channel statistics and feeds back the updated vectors to the network device. The network device then uses these updated vectors to construct the precoding matrix. This feedback loop ensures that the precoding matrix accurately reflects downlink channel conditions while maintaining the benefits of reciprocity-based construction.
2Difficulty of detecting and measuring
If space domain vector and frequency domain vector are obtained based on reciprocity between uplink and downlink channels, then measurement process is simplified, but measurement precision deteriorates due to insufficient sparsity
Solution Approach 1:
The patent applies preliminary action by having the terminal device pre-obtain downlink channel statistical information before precoding matrix construction. The terminal device calculates downlink channel statistics (such as spatial covariance matrix) in advance and uses this information to update the space domain vectors. This preliminary processing of channel statistics enables more precise channel representation while keeping the measurement process simple.
Solution Approach 2:
The patent changes the parameter representation from direct channel measurements to channel statistical information. Instead of using raw channel measurements which have insufficient sparsity, the system transforms the channel information into statistical parameters (covariance matrices, correlation coefficients) that capture the essential spatial and frequency characteristics. This parameter transformation improves measurement precision while maintaining measurement simplicity.
Data Source
AI summary
Embodiments of this application provide an update method and a communications apparatus. The method includes: generating space domain vector update information, where the space domain vector update information is for updating a first group of space domain vectors to obtain a second group of space domain vectors, the space domain vector update information includes a plurality of groups of space domain vector coefficients, and each group of space domain vector coefficients is for performing weighted combination on the first group of space domain vectors, to obtain one space domain vector in the second group of space domain vectors; and sending the space domain vector update information.