Neural Signal Configuration for Fast Wireless Channel Estimation
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Solution Overview
Problem
Existing wireless communication systems face challenges in accurately configuring signals due to the complexity of radio environments, which can lead to inefficient use of network resources and suboptimal signal designs.
Innovation Solution
A neural network-based system is employed to collect data samples, train neural networks, and evaluate candidate signal configurations to optimize signal configurations in wireless networks, utilizing a knowledge center and UE agents to predict environmental changes and improve signal effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reference signals are transmitted to estimate channel state information, then channel estimation accuracy is improved, but network resource consumption increases
Solution Approach 1:
The patent creates a virtual copy of the radio environment using a trained neural network model. Instead of continuously transmitting reference signals to measure channel state, the system trains a neural network on collected data samples representing the radio environment, then uses this virtual model to estimate channel conditions. This copying approach maintains estimation accuracy while eliminating the ongoing resource cost of reference signal transmission.
Solution Approach 2:
The system performs preliminary training of the neural network model using collected data samples before actual channel estimation is needed. By preparing the environmental model in advance through training phases, the system establishes a ready-to-use virtual representation of the radio environment, avoiding the need for continuous reference signal transmissions during operation.
2Adaptability or versatility
If traditional feedback-based methods are used to determine channel state, then signal configuration can be adjusted, but the process is slow and resource-intensive
Solution Approach 1:
The patent replaces the mechanical feedback-based measurement system with an information-processing approach using neural networks. Instead of physically transmitting reference signals and collecting feedback measurements, the system uses a trained neural network to computationally estimate channel state information. This substitution dramatically reduces the time required for channel estimation while maintaining the ability to adapt signal configurations.
Solution Approach 2:
The neural network environmental model serves as an intermediary between the transmitter and receiver. Rather than requiring direct feedback communication between UE and base station, the trained model acts as a mediator that predicts channel conditions based on input parameters, enabling rapid signal configuration adjustments without traditional feedback loops.
3Measurement precision
If more data samples are collected for neural network training, then model accuracy is improved, but storage and processing requirements increase
Solution Approach 1:
The patent applies partial action by using a subset of collected data samples for training the neural network rather than requiring all available data. The system collects data samples from multiple sources including UE measurements and base station observations, then selectively uses representative subsets for training, achieving sufficient model accuracy without storing and processing every possible data point.
Data Source
AI summary
A wireless network can generate candidate signal configurations for physical transmissions to or from a user equipment (UE) in a radio environment. The generation of candidate signal configurations can be performed using a first neural network that is associated with the UE. These signal configurations can then be evaluated using a second neural network that is associated with the radio environment. The second neural network can be trained using measurements from previous physical transmissions in the radio environment. The trained second neural network generates a reward value that is associated with the candidate signal configurations. The first neural network is then trained using the reward values from the second neural network to produce improved candidate signal configurations. When a signal configuration that produces a suitable reward value is generated, this signal configuration can be used for the physical transmission in the radio environment.