Neural Network CSI Extrapolation for Wireless Antenna Port Efficiency
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Solution Overview
Problem
Conventional methods for determining channel state information (CSI) in wireless communications systems are inefficient, leading to increased power consumption and signaling overhead, especially when dealing with a large number of antenna ports.
Innovation Solution
The use of a neural network model by user equipment (UE) to extrapolate CSI for a set of antenna ports based on measurements made on a subset of those ports, reducing the need for extensive CSI measurements and reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to determine CSI for all antenna ports, then measurement precision is maintained, but power consumption and signaling overhead increase significantly
Solution Approach 1:
The patent applies partial action by having the UE measure CSI-RS signals only on a subset of antenna ports (e.g., 4 ports) rather than all configured ports (e.g., 8 ports), and then uses neural networks to extrapolate the CSI for the remaining ports. This reduces the measurement burden and power consumption while maintaining adequate CSI accuracy for network optimization.
Solution Approach 2:
The patent introduces neural networks as an intermediary component between the CSI measurement process and the final CSI determination. The neural networks act as a mediator that takes measurements from a subset of antenna ports and transforms them into estimated CSI values for all antenna ports, thereby reducing direct measurement requirements and associated power consumption.
2Measurement precision
If CSI measurements are performed on all antenna ports, then measurement precision is maintained, but signaling overhead increases
Solution Approach 1:
The patent reduces signaling overhead by requiring CSI measurements only on a subset of antenna ports rather than all ports. The neural networks then extrapolate the CSI for unmeasured ports, significantly reducing the quantity of CSI data that needs to be measured, processed, and signaled while maintaining sufficient precision for network optimization.
Solution Approach 2:
The neural networks serve as an intermediary that transforms a smaller set of actual measurements into comprehensive CSI estimates for all antenna ports. This intermediary process reduces the volume of data that needs to be handled through signaling, thereby reducing overall signaling overhead while preserving measurement precision through intelligent extrapolation.
3Measurement precision
If extensive CSI measurements are performed, then measurement precision is improved, but processing time and latency increase
Solution Approach 1:
The patent reduces processing latency by performing CSI measurements only on a subset of antenna ports rather than all ports. The neural networks then rapidly extrapolate the remaining CSI values, significantly reducing the total processing time required while maintaining adequate measurement precision for effective network optimization.
Solution Approach 2:
The neural networks act as an intermediary that accelerates the CSI determination process by taking measurements from a reduced set of antenna ports and quickly generating estimates for all ports through trained extrapolation models. This intermediary approach eliminates the need for exhaustive measurements and processing, thereby reducing overall processing latency while preserving measurement precision.
Data Source
AI summary
Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive a first indication of a first number of antenna ports for which the UE may report channel state information (CSI), and a second indication of a second number of antenna ports on which the UE may measure CSI reference signals (CSI-RSs). The second number may be less than the first number. The UE may receive a third indication of one or more neural networks to be used by the UE for determination of the CSI associated with the first number. The UE may determine the CSI using the one or more neural networks and using measurements made by the UE on the second number as inputs to the one or more neural networks. The UE may transmit a report including the CSI associated with the first number determined via the one or more neural networks.