18557374. MACHINE LEARNING FOR WIRELESS COMMUNICATION simplified abstract (Telefonaktiebolaget LM Ericsson (publ))
Contents
MACHINE LEARNING FOR WIRELESS COMMUNICATION
Organization Name
Telefonaktiebolaget LM Ericsson (publ)
Inventor(s)
MACHINE LEARNING FOR WIRELESS COMMUNICATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18557374 titled 'MACHINE LEARNING FOR WIRELESS COMMUNICATION
The abstract describes a machine learning system that provides an output based on the status of a wireless communication system, with a focus on phase ambiguity limitation.
- Machine learning system configured for output based on wireless communication system status
- Output represents an action for the wireless communication system
- Focus on phase ambiguity limitation
- Related devices and methods include radio nodes and wireless communication system
Potential Applications: - Enhancing the efficiency and reliability of wireless communication systems - Optimizing network performance and resource allocation - Improving signal processing and data transmission in radio nodes
Problems Solved: - Addressing phase ambiguity in wireless communication systems - Enhancing decision-making processes based on system status - Improving overall system performance and reliability
Benefits: - Increased accuracy and precision in system actions - Enhanced communication quality and network stability - Streamlined operations and maintenance of wireless networks
Commercial Applications: Title: "Enhanced Wireless Communication System Optimization" This technology can be applied in telecommunications companies, IoT networks, and smart city infrastructure to improve connectivity and data transmission efficiency.
Questions about Machine Learning System for Wireless Communication Systems: 1. How does the machine learning system handle phase ambiguity in wireless communication systems? - The machine learning system utilizes advanced algorithms to analyze system status and provide accurate outputs while limiting phase ambiguity.
2. What are the key advantages of using machine learning in optimizing wireless communication systems? - Machine learning enables real-time decision-making, adaptive network configurations, and improved overall system performance.
Original Abstract Submitted
There is disclosed a machine learning system. The machine learning system is configured to provide an output based on an input, the input representing a status of a wireless communication system comprising a plurality of radio nodes, the output representing an action for the wireless communication system, the machine learning system is configured for a phase ambiguity limitation regarding the output. The disclosure also pertains to related devices and methods, for example radio nodes and a wireless communication system.
