MU-MIMO UE Grouping via 3D Geographic Container Correlation
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Solution Overview
Problem
The calculation of MU-MIMO correlation factors for wireless user devices is a heavy burden on the processing resources of wireless access nodes, which affects the efficiency of MU-MIMO operations in wireless communication networks.
Innovation Solution
The network circuitry determines UE locations and associates UE correlation factors with 3D geographic containers, generating container correlation factors to select UEs for shared wireless resource blocks, thereby reducing the processing burden and improving MU-MIMO efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If MU-MIMO correlation factors are calculated for each wireless user device, then MU-MIMO grouping accuracy is improved, but processing resource burden on access nodes increases
Solution Approach 1:
The patent segments the service area into multiple geographic containers (e.g., sectors, beams, or spatial regions). Instead of calculating correlation factors for all UEs across the entire network, the system divides the problem into smaller sub-problems by container, reducing the computational burden on each access node while maintaining accurate correlation measurements within each segment.
Solution Approach 2:
The patent extracts and pre-calculates correlation factors between geographic containers themselves, rather than between every pair of UEs. By taking out the container-level correlation as a representative metric, the system reduces the number of calculations needed while still enabling accurate MU-MIMO grouping through the use of these extracted container correlation factors.
2Productivity
If container correlation factors are used for UE grouping, then processing efficiency is improved, but grouping precision may be reduced
Solution Approach 1:
The patent creates a copy or representation of the UE correlation characteristics at the container level. By generating container correlation factors that represent the aggregate correlation properties of UEs within geographic containers, the system achieves a balance between processing efficiency and grouping accuracy, using the container-level copy as a proxy for detailed UE-level analysis.
Solution Approach 2:
The patent transitions from analyzing correlation in the UE domain to analyzing correlation in the geographic container domain. This dimensional shift allows the system to process correlation information at a higher level of abstraction, improving processing efficiency while maintaining sufficient accuracy for practical MU-MIMO grouping through the spatial aggregation inherent in container definitions.
Data Source
AI summary
A wireless communication network configured to share a wireless resource block that comprises a same time interval and a same radio subcarrier. The wireless communication network comprises network circuitry and transceiver circuitry. The network circuitry determines UE locations and determines UE correlation factors between the UEs based on the UE locations. The network circuitry associates the UE correlation factors with Three-Dimensional (3D) geographic containers based on the first UE locations and generates container correlation factors for the Three-Dimensional (3D) geographic containers responsive to the associations. The network circuitry selects UEs for the shared wireless resource block. The transceiver circuitry wirelessly transfers user data to the selected UEs over the shared wireless resource block that comprises the same time interval and the same radio subcarrier.