Meta-Learned Channel Estimation for New UE Connections
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In massive multi-antenna wireless communication systems, accurately estimating channels for new user equipment (UEs) connected to a base station is challenging due to mobility-induced channel changes, leading to reduced data transmission rates and increased interference.
Innovation Solution
A meta learning-based channel estimating device that uses pilot signals from existing UEs to estimate channels for new UEs via meta learning, incorporating a meta learning structure unit and a noise canceling unit to improve estimation efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional channel estimation methods are used for new UEs in massive MIMO systems, then the system can maintain existing connection stability, but the channel estimation accuracy deteriorates due to mobility-induced channel changes
Solution Approach 1:
The system performs preliminary channel estimation using pilot signals from existing UEs before actual data transmission begins. The meta learning model is pre-trained on historical channel data from existing UEs, enabling it to quickly adapt to new UEs' channel characteristics without requiring extensive real-time training, thus resolving the contradiction between accuracy and adaptability.
Solution Approach 2:
The invention creates a virtual copy of the channel estimation process by using meta learning to replicate channel characteristics from existing UEs. The model copies patterns and features from trained UE data to estimate channels for new UEs, achieving high accuracy without requiring the same amount of training data for each new connection, thereby improving both accuracy and adaptability.
2Measurement precision
If more pilot signals are collected from existing UEs to improve meta learning accuracy, then the channel estimation precision improves, but the processing time and system complexity increase
Solution Approach 1:
The system uses a carefully selected subset of pilot signals from existing UEs rather than processing all available signals. The meta learning model is designed to achieve sufficient accuracy with a partial set of training data, avoiding the diminishing returns of processing excessive amounts of pilot signals. This selective approach maintains high precision while minimizing processing time and system complexity.
3Productivity
If traditional channel estimation methods are used, then the system maintains simplicity in implementation, but the data transmission rate decreases due to interference and channel changes
Solution Approach 1:
The invention replaces traditional mechanical/mathematical channel estimation algorithms with a machine learning-based meta learning system. This substitution enables the system to adaptively learn channel patterns and predict channel states more accurately, thereby maintaining higher data transmission rates despite the increased computational complexity of implementing and training the neural network model.
Data Source
AI summary
A method and a device for estimating a channel in a wireless communication system are provided. Provided is a meta learning device including processing circuitry configured to receive pilot signals from a plurality of first user equipment (UEs) to obtain received pilot signals, the received pilot signals being based on existing wireless communication connections of the plurality of first UEs, and estimate channels of a plurality of second UEs via meta learning using the received pilot signals, the channels corresponding to new wireless communication connections of the plurality of second UEs.