Neural Network Precoding for Uplink Wireless Communications
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Solution Overview
Problem
In wireless communications, especially in high mobility scenarios, existing uplink precoding techniques face challenges with outdated channel state information leading to degraded throughput, increased latency, and high overhead in CSI reports.
Innovation Solution
The use of neural network (NN) based precoding functions at user equipment (UE) to determine precoding parameters directly from channel estimations, with network entities providing coefficients for NN precoding functions, reducing the frequency of CSI reports and optimizing precoding based on UE capabilities and restrictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional uplink precoding techniques are used with frequent CSI reports, then channel state information is updated frequently, but signaling overhead increases and battery power is consumed
Solution Approach 1:
The UE performs channel estimation autonomously using downlink reference signals and applies it to uplink precoding through channel reciprocity, eliminating the need for frequent uplink CSI reports and reducing signaling overhead while maintaining precoding accuracy
Solution Approach 2:
Instead of frequent periodic CSI reports, the system uses periodic downlink reference signals for channel estimation, which are already transmitted for downlink communication, thereby reducing additional signaling overhead and battery consumption without sacrificing channel state awareness
2Reliability
If traditional uplink precoding techniques are used with frequent CSI reports, then channel state information is updated frequently, but signaling overhead increases
Solution Approach 1:
The UE autonomously performs channel estimation using downlink reference signals and applies it to uplink precoding through channel reciprocity, eliminating the need for frequent uplink CSI reports and reducing signaling overhead while maintaining precoding accuracy
Solution Approach 2:
The downlink reference signals serve dual purposes: downlink channel estimation and uplink channel estimation through reciprocity, making the existing signaling resources universal for both downlink and uplink precoding needs
3Productivity
If machine-learning based precoding functions are implemented at UE, then precoding parameters are determined directly from channel estimation, but device complexity increases
Solution Approach 1:
Instead of implementing complex neural network models at the UE, the system uses simplified precoding functions that replicate the essential functionality of machine-learning based precoding while reducing computational complexity at the UE end
Solution Approach 2:
The system changes the parameters of the precoding function to be simpler and more suitable for UE implementation, transforming complex machine-learning models into parameter-based functions that can be efficiently executed at the UE
Data Source
AI summary
Methods, systems, and devices for wireless communications are described that provide machine-learning models, such as neural network (NN) based precoding functions, that may be used at a user equipment (UE) to determine precoding parameters directly based on channel estimations performed at the UE. A network entity may measure a channel used for communications with the UE and determine coefficients that are to be applied to a machine learning precoding function (e.g., a NN precoding function) at the UE. The network entity may provide the determined coefficients to the UE. The UE may use the indicated coefficients in the NN based precoding function, along with a channel estimation of a channel associated with an uplink transmission, to determine a precoding matrix that is to be applied for the uplink transmission.