Neural Network Configuration for RAN Nodes
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Solution Overview
Problem
Determining a suitable configuration for neural networks in radio access network (RAN) nodes is complex due to varying information and conditions, including hardware capabilities and measurements, which existing technologies do not effectively address.
Innovation Solution
A method involving a controller that receives neural network support and measurement information from RAN nodes to determine and send the appropriate neural network configuration, including type and parameters, tailored to each node's specific features and conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network is configured for a RAN node, then the performance and accuracy of wireless communication functions are improved, but the device complexity and computational requirements increase
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting neural network configuration parameters (such as network size, layer depth, activation functions) based on the specific capabilities and conditions of each RAN node. The controller receives information about node capabilities and modifies the neural network parameters accordingly, allowing the system to achieve high performance accuracy while adapting to different device complexities and computational constraints.
2Adaptability or versatility
If neural network configuration is tailored to each node's specific features, then adaptability and performance are improved, but the configuration process complexity increases
Solution Approach 1:
The patent implements preliminary action by having the controller pre-assess the capabilities of each RAN node (including hardware resources, processing power, and operational conditions) before configuring the neural network. This advance evaluation allows the system to prepare appropriate configuration parameters in advance, reducing the complexity of the actual configuration process while maintaining high adaptability to node-specific features.
Solution Approach 2:
The system dynamically changes neural network configuration parameters based on the assessed node capabilities. By adjusting parameters such as network architecture, training data selection, and computational resources allocated, the system achieves optimal adaptability to each node while managing configuration complexity through automated parameter optimization.
3Ease of operation
If existing technologies are used for neural network configuration, then implementation is simpler, but the ability to handle varying node capabilities and conditions is insufficient
Solution Approach 1:
The patent overcomes the limitations of existing technologies by implementing dynamic parameter changes based on received node capability information. The controller adjusts neural network configuration parameters (architecture, resources, training parameters) according to the specific hardware and operational conditions of each RAN node, thereby achieving both ease of operation through automated adaptation and high versatility in handling varying node capabilities.
Data Source
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AI summary
According to an example embodiment, a method may include receiving, by a controller from a radio access network (RAN) node within a wireless network, at least one of a neural network support information, and a measurement information that includes one or more measurements by the radio access network node or one or more measurements by a wireless device that is in communication with the radio access network node; determining, by the controller based on the at least one of the neural network support information and the measurement information, a configuration of a neural network for the radio access network node; an sending, by the controller to the radio access network node, neural network configuration information that indicates the configuration of the neural network for the radio access network node.