End-to-end ML Controller for Wireless Signal Quality
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Solution Overview
Problem
Current wireless communication systems face challenges in managing increased data throughput, particularly with higher frequency ranges like 5G mmW signals, which are susceptible to signal distortions and require complex, costly hardware.
Innovation Solution
Implementing an end-to-end machine learning controller that determines and communicates an end-to-end machine learning configuration to devices, enabling them to form deep neural networks for processing communications, thereby adapting to changing operating conditions and improving communication efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If higher frequency ranges (5G mmW) are used to increase data capacity, then data throughput is improved, but signal quality deteriorates due to multipath fading, scattering, and atmospheric absorption
Solution Approach 1:
A machine learning controller is introduced as an intermediary between the transmitter and receiver. This controller processes channel state information and determines optimal transmission parameters, acting as a mediator that compensates for signal distortions caused by high-frequency propagation issues like multipath fading and atmospheric absorption
Solution Approach 2:
The system dynamically changes transmission parameters (such as modulation scheme, coding rate, and resource allocation) based on channel conditions. The machine learning controller continuously adjusts these parameters to optimize the trade-off between data throughput and signal quality in response to varying channel states
2Productivity
If complex hardware is used to transmit and receive higher frequencies, then data capacity increases, but device complexity and processing costs increase
Solution Approach 1:
The patent replaces complex hardware-based signal processing with software-based machine learning algorithms. Instead of using additional physical components to handle high-frequency signals, the system uses a machine learning controller that processes channel information and makes optimization decisions through computational methods
Solution Approach 2:
The machine learning controller serves multiple functions: it processes channel state information, determines optimal transmission parameters, and adapts to varying channel conditions. This single multi-functional component replaces what would otherwise require multiple specialized hardware modules
Data Source
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AI summary
Techniques and apparatuses are described for generating an end-to-end machine-learning configuration for wireless networks. An end-to-end machine-learning controller (318) determines an end-to-end machine-learning configuration (2115, 2230) for processing information exchanged through an end-to-end communication (1902, 1904) in a wireless network. The end-to-end machine-learning controller obtains capabilities (at 2215, at 2220) of one or more devices that are utilized in the end-to-end communication. The end-to-end machine-learning controller (318) determines the end-to-end machine-learning configuration (2115, 2230) for processing the information exchanged through the end-to-end communication (1902, 1904), and directs the one or more devices (110, 120, 302) to process the information exchanged through the end-to-end communication (1902, 1904) by forming one or more deep neural networks (1908, 1910, 1912, 1914, 1916, 1918) based on the end-to-end machine-learning configuration.