Neural Network Configuration Messaging for Adaptive Wireless Recovery
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Solution Overview
Problem
Evolving wireless communication systems face challenges in processing higher frequency ranges due to increased signal distortions and complex hardware requirements, leading to inefficiencies and higher costs.
Innovation Solution
Implementing dynamic neural network configurations using a neural network table to quickly reconfigure deep neural networks (DNNs) based on changing operating conditions, reducing data transmissions and improving information recovery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If higher frequency ranges are used to increase data capacity, then data throughput is improved, but signal quality deteriorates due to increased susceptibility to multipath fading, scattering, atmospheric absorption, and interference
Solution Approach 1:
The patent implements dynamic neural network configuration where the DNN architecture and parameters are adjusted in real-time based on changing operating conditions such as signal quality metrics. This allows the system to adapt to varying channel conditions at higher frequencies, maintaining reliable information recovery while utilizing high-throughput frequency bands
Solution Approach 2:
The system changes neural network parameters (architecture, layers, filters) based on operating conditions to optimize performance. By dynamically modifying these parameters, the system can compensate for signal quality deterioration at higher frequencies while maintaining high data throughput capability
2Productivity
If complex hardware capable of transmitting and receiving higher frequencies is deployed, then data capacity increases, but processing costs and device complexity increase
Solution Approach 1:
The patent replaces complex hardware-based signal processing with software-based deep neural network processing. This substitution allows the system to handle higher frequency signals and compensate for their vulnerabilities through intelligent algorithms rather than requiring increasingly complex hardware architectures
Solution Approach 2:
The system uses neural network models that can be configured and reconfigured through software to handle different frequency bands and signal conditions, avoiding the need for dedicated hardware for each frequency range and reducing overall hardware complexity
3Ease of operation
If traditional configuration methods are used for DNN, then configuration is simple, but responsiveness to changing operating conditions is slow
Solution Approach 1:
The system implements dynamic configuration where the DNN is reconfigured based on real-time operating conditions. The network entity monitors conditions and sends reconfiguration signals to update the DNN parameters, enabling rapid adaptation while maintaining operational simplicity through automated processes
Solution Approach 2:
The system pre-configures multiple DNN configurations that can be quickly switched between based on operating conditions. This preliminary preparation of multiple configurations allows rapid response to changing conditions without requiring complex real-time optimization calculations
Data Source
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AI summary
Techniques and apparatuses are described for enabling base station-user equipment messaging regarding deep neural networks. A network entity (base station 121, core network server 320) determines a neural network formation configuration (architecture and/or parameter configurations 1208) for a deep neural network (deep neural network(s) 604, 608, 612, 616) for processing communications transmitted over the wireless communication system. The network entity (base station 121, core network server 302) communicates the neural network formation configuration to a user equipment (UE 110). The user equipment (UE 110) configures a first neural network (deep neural network(s) 608, 612) based on the neural network formation configuration. In implementations, the user equipment (UE 110) recovers information communicated over the wireless network using the first neural network (deep neural network(s) 608, 612). This allows the wireless communication system to adapt to changing operating conditions and improve information recovery.