Spatial Stream Selection via Group Granularity Feedback
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Solution Overview
Problem
In LTE systems, improper spatial stream scheduling by base stations leads to waste of system resources due to inaccurate channel state feedback based on code word granularity, rather than group granularity.
Innovation Solution
The method involves a base station sending a feedback mode indication to user equipment to report channel state information at a group granularity level, including multiple spatial streams, allowing for more accurate CSR information feedback and improved resource utilization by determining optimal spatial streams for data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If user equipment feeds back channel state report information based on code word granularity, then the feedback process is simple, but the spatial stream scheduling becomes improper and system resources are wasted
Solution Approach 1:
The patent segments the channel state feedback by introducing group granularity that divides spatial streams into multiple groups. Each group's channel state is reported separately, allowing the base station to perform more precise spatial stream scheduling based on group-level channel conditions rather than overall code word-level conditions, thus resolving the contradiction between feedback simplicity and scheduling accuracy.
2Quantity of substance
If user equipment feeds back channel state report information based on code word granularity, then the feedback overhead is reduced, but channel state information accuracy deteriorates
Solution Approach 1:
The patent segments the channel state feedback into group-level reports where each group contains spatial streams with similar channel characteristics. This segmentation provides more granular channel state information than code word-level feedback while avoiding the excessive overhead of per-spatial-stream feedback, thus balancing feedback overhead and accuracy.
Solution Approach 2:
The patent applies local quality by reporting channel state information at different granularities for different groups of spatial streams. Each group's channel state is reported with appropriate detail based on its specific characteristics, providing locally optimized accuracy without uniformly increasing feedback overhead across all spatial streams.
Data Source
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AI summary
The present invention provides a spatial stream determining method, a base station, and user equipment. The method includes: sending, by a base station, a feedback mode indication to user equipment, where the feedback mode indication is used to instruct the user equipment to feed back, based on a group granularity, channel state report (CSR) information, and each group granularity includes at least one spatial stream (101); receiving, by the base station, CSR information of each group granularity that is sent by the user equipment (102); and determining, by the base station according to the CSR information of each group granularity, a spatial stream used to transmit data to the user equipment (103). Because the user equipment uses at least one spatial stream as a group granularity to report the CSR information, the CSR information is more accurate, so that the present invention enhances properness of selecting, by the base station for a user, a spatial stream used to transmit data, and improves utilization of system resources.