MU-MIMO Grouping via Channel Correlation
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Solution Overview
Problem
In multi-user multiple input multiple output (MU-MIMO) systems, power leakage occurs when transmitting data to multiple devices simultaneously, reducing efficiency due to similarities in channel characteristics between client devices, leading to ineffective data delivery and reduced throughput.
Innovation Solution
A network device dynamically arranges client devices into MU-MIMO groups based on coherency times and inter-client channel correlations, using historical data to optimize group formation and replacement, thereby minimizing power leakage and maximizing PHY channel capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If client devices are arranged into MU-MIMO groups using standardized precoding and filtering, then simultaneous data transmission to multiple devices is enabled, but power leakage occurs due to similar channel characteristics reducing transmission effectiveness
Solution Approach 1:
The patent applies local quality by creating distinct MU-MIMO groups where each group has unique channel characteristics. Instead of treating all clients uniformly, the system segments clients into groups based on their specific channel properties (coherency time, channel correlation), ensuring that precoding and filtering are optimized for each group's local characteristics rather than applying a generic approach to all clients.
Solution Approach 2:
The patent segments the client population into multiple MU-MIMO groups based on channel characteristics such as coherency time and channel correlation. This segmentation allows the system to transmit to multiple groups simultaneously with reduced interference, as each group represents a distinct segment with unique channel properties that can be handled independently through targeted precoding.
2Measurement precision
If standard precoding is used to null out power to other client devices, then data delivery to individual clients is attempted, but similar channel characteristics cause power intended for one client to be received by others reducing effectiveness
Solution Approach 1:
The patent enhances data delivery accuracy by applying local quality through group-specific precoding matrices. Each MU-MIMO group receives customized precoding tailored to its unique channel characteristics, allowing more precise control over power delivery to intended recipients while minimizing leakage to other groups with different channel properties.
Solution Approach 2:
The patent introduces MU-MIMO groups as an intermediary layer between the access point and individual client devices. These groups act as intermediaries that aggregate clients with similar channel characteristics, allowing the system to manage power delivery at the group level rather than directly to individual clients, thereby reducing the complexity and improving the effectiveness of precoding operations.
3Productivity
If dynamic grouping based on coherency times and channel correlations is implemented, then power leakage is minimized and channel capacity is maximized, but system complexity increases due to continuous performance evaluation and group replacement
Solution Approach 1:
The patent applies dynamics by making MU-MIMO groups dynamic rather than static. Groups are continuously evaluated based on performance metrics and reconfigured according to changing channel conditions, client movements, and traffic patterns. This dynamic approach allows the system to adapt to varying conditions and maintain optimal performance, with groups being created, modified, or dissolved as needed based on real-time assessments.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors MU-MIMO group performance and uses this information to make informed decisions about group configuration. Performance feedback from actual transmissions guides the formation and modification of groups, allowing the system to learn from past performance and optimize future group assignments based on observed outcomes rather than relying solely on theoretical channel characteristics.
Data Source
AI summary
Embodiments herein describe a network device (e.g., an access point) that dynamically arranges multi-user (MU) multiple input multiple output (MIMO) compatible client devices into MU-MIMO groups. That is, the network device uses network metrics and historical data to change the assignment of client devices in the MU-MIMO groups which may improve MU-MIMO efficiency by reducing the amount of power that leaks between the clients devices in the group. In one embodiment, the AP identifies a MU-MIMO group based on a performance evaluation such as evaluating network metric or determining if the group is underutilized. The AP can replace the identified MU-MIMO group with a substitute MU-MIMO group where the substitute MU-MIMO group is selected based on historical data corresponding to the client devices assigned to the substitute MU-MIMO group.