Distributed Neural Network Input Layer Segmentation for Radio Resource Management
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Solution Overview
Problem
Current machine learning implementations in radio access networks face challenges in efficiently processing and transmitting data between user equipment and artificial neural networks, particularly in selecting appropriate input layers and configuring parameters for effective radio resource management.
Innovation Solution
A distributed machine learning system comprising a first apparatus with a receiver, processor, and transmitter configured to receive and process input data from user equipment, determine inputs for artificial neural networks, and transmit outputs, while a second apparatus processes these outputs for radio resource management, using configuration commands and training information to select input layers and configure parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning is implemented in radio access networks, then radio resource management performance is improved, but data processing complexity and system configuration requirements increase
Solution Approach 1:
The artificial neural network is divided into multiple input layers (first input layer, second input layer, etc.), each processing different types of data from user equipment. This segmentation allows the system to handle complex data processing by breaking it down into manageable stages, improving both performance and configurability without overwhelming complexity
Solution Approach 2:
The system dynamically selects which input layers to activate and which parameters to configure based on real-time conditions and configuration commands. This dynamic approach allows the neural network to adapt to varying network conditions, improving resource management performance while managing complexity through selective activation rather than processing all data simultaneously
2Adaptability or versatility
If multiple input layers and parameters are configured for the artificial neural network, then machine learning effectiveness is improved, but system configuration and operation complexity increase
Solution Approach 1:
The interface is designed to receive multiple types of configuration commands (first configuration command, second configuration command, etc.) that can selectively enable different input layers and parameters. This universal interface simplifies operation by providing a single point of control for managing the complex neural network configuration, allowing operators to activate specific layers based on needs without configuring everything manually
Solution Approach 2:
The system includes feedback mechanisms where configuration commands are received, processed, and the results are transmitted back. This feedback loop allows the system to learn from configuration outcomes and adjust operations accordingly, making the complex multi-layer neural network easier to manage through automated adaptation rather than manual reconfiguration
3Measurement precision
If data is processed through multiple layers of the artificial neural network, then radio resource management accuracy is improved, but data transmission time and processing delay increase
Solution Approach 1:
The system processes data through multiple input layers selectively rather than always processing through all layers. Based on configuration commands and network conditions, the system can process data through only the necessary layers to achieve required accuracy, reducing unnecessary processing delay while maintaining sufficient measurement precision for effective resource management
Data Source
AI summary
A first apparatus (101) for a first method comprising receiving input data of at least one user equipment (102), determining an input to at least a part of at least one input layer (112A) of an artificial neural network depending on the input data, determining an output of a first part of the artificial neural network and transmitting the output of this part of the artificial neural network, and a second apparatus (105) for a second method comprising receiving an input for another part of the artificial neural network, determining an output of this part of the artificial neural network for at least one user equipment (102) depending on the input, the other part of the rtificial neural network comprising at least a part of at least one hidden layer (118A) or at least a part of an output layer (114A) of he artificial neural network or at least a part of at least one hidden layer (118A) and at least a part of an output layer (114A) of the artificial neural network, and outputting the output.