MU-MIMO Frequency Resource Allocation via User Grouping
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Solution Overview
Problem
Current massive MIMO systems lack an effective user grouping and frequency resource allocation strategy, leading to significant losses in sum capacity and data rate for users with excellent channel conditions due to being grouped with those having bad channel conditions.
Innovation Solution
A universal user grouping strategy is developed based on Channel Quality Information (CQI), Channel Estimation Error (CEE), and UE Speed Indication Information (SII), where UEs are divided into groups and allocated frequency resources accordingly, employing specific precoding or decoding algorithms suited to each group's channel conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users of diverse channel conditions are placed in the same frequency resource, then device complexity is reduced and ease of operation is improved, but sum capacity is significantly lost and data rate of users with excellent channel conditions is reduced
Solution Approach 1:
The patent segments users into different groups based on channel conditions (e.g., good, medium, bad channel conditions) and allocates different frequency resources to each group. This segmentation allows the system to achieve high sum capacity by matching appropriate precoding algorithms to each user group while maintaining manageable operational complexity through automated grouping based on CQI feedback.
2Device complexity
If users of diverse channel conditions are placed in the same frequency resource, then device complexity is reduced, but manufacturing precision of channel condition matching is worsened
Solution Approach 1:
The patent applies local quality by assigning different precoding algorithms tailored to specific user groups with different channel conditions. Users with good channel conditions receive algorithms optimized for high data rates, while users with bad channel conditions receive algorithms optimized for reliability. This local optimization achieves precise channel condition matching without requiring complex manual configuration.
3Productivity
If aggressive spatial multiplexing is employed in massive MIMO systems, then capacity increase is achieved, but reliability under specified conditions is reduced due to sensitivity to channel estimation errors
Solution Approach 1:
The patent changes the parameter of precoding algorithm selection based on channel condition parameters (CQI, CEE, SII). By dynamically selecting appropriate precoding algorithms (e.g., CB, ZFB, MRT, ZF) based on measured channel conditions, the system achieves both high capacity through aggressive spatial multiplexing where appropriate and high reliability through robust algorithms when channel conditions are poor or estimation errors are high.
Data Source
AI summary
This invention presents a method for frequency resource allocation in a MU-MTMO system comprising assigning UEs with CQI higher than a predefined value and/or CEE lower than a predefined value and/or SII lower than a predefined value to a first group and the rest UEs to a second group, allocating different frequency resource to each group, and applying different precoding or decoding algorithm to each group.