Base Station Measurement Reporting After Wireless Handover
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Solution Overview
Problem
Existing communication networks face challenges in efficiently managing data traffic and optimizing resource allocation across diverse wireless devices and base stations, particularly in heterogeneous networks with varying capabilities and release levels, leading to suboptimal performance and resource inefficiencies.
Innovation Solution
Implementing artificial intelligence and machine learning (AI/ML) techniques to enhance information exchange and resource management in radio access networks, enabling dynamic adaptation and optimization based on device and network conditions, such as traffic load, packet sizes, and capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional resource management methods are used in heterogeneous networks, then device compatibility is maintained, but network performance and resource allocation efficiency deteriorate
Solution Approach 1:
The patent transforms network resource management from static to dynamic by changing the state of resource allocation parameters. AI/ML models continuously adjust resource allocation parameters based on real-time network conditions, device capabilities, and traffic patterns, enabling optimal performance across heterogeneous devices without sacrificing compatibility
Solution Approach 2:
The system enables self-service through AI/ML-driven autonomous decision-making at the network edge. Base stations and network functions automatically analyze local conditions and make resource allocation decisions without centralized control, improving responsiveness and performance while maintaining standard compliance through learned behaviors
2Productivity
If static resource allocation is used, then system complexity is reduced, but resource allocation efficiency and network optimization deteriorate
Solution Approach 1:
The patent introduces AI/ML models as intermediary components between network conditions and resource allocation decisions. These models process complex inputs from multiple sources (traffic patterns, device capabilities, network state) and transform them into optimized resource allocation outputs, managing complexity through modular architecture while achieving superior efficiency
Solution Approach 2:
The system segments resource management into independent AI/ML models deployed at different network levels (base station, core network, edge). Each model handles specific resource allocation tasks independently, reducing overall system complexity through modular design while collectively achieving high resource allocation efficiency across the heterogeneous network
Data Source
AI summary
A method can include receiving, by a first base station from a second base station, one or more first messages comprising one or more reporting configurations. The one or more reporting configurations can indicate to the first base station to report to the second base station one or more measurements for a wireless device based on at least one of one or more conditions being satisfied. The one or more conditions can include a condition indicating to report the one or more measurements after a handover of the wireless device from a first cell of the first base station to a second cell. The method can also include sending, by the first base station to the second base station and based on determining that at least one of the one or more conditions is satisfied for the wireless device, the one or more measurements.