Cellular Network Parameter Adaptation for Safety-Based Resource Allocation
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Solution Overview
Problem
Existing cellular network infrastructure lacks the ability to dynamically adjust network parameters based on the safety level of mobile devices, leading to inefficient resource allocation and potential safety risks in autonomous operations.
Innovation Solution
A method using a machine learning model to determine network parameters based on device observations and safety levels, allowing for flexible and efficient resource allocation by signaling these parameters to the core network for optimal resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed network requirement with over specified values is adopted, then safety requirements are met, but network resources are wasted due to inefficient allocation
Solution Approach 1:
The patent implements dynamic adjustment of network parameters (latency, bandwidth, reliability) based on real-time safety levels of mobile devices. The network infrastructure transitions from fixed over-specified requirements to adaptive parameters that change according to device safety classification, enabling efficient resource allocation while maintaining required safety standards for each device type.
Solution Approach 2:
The system changes network parameter values (latency bounds, bandwidth allocation, reliability thresholds) based on safety levels. Different mobile devices receive customized network parameter sets matched to their safety requirements, avoiding the waste of applying uniform over-specified parameters to all devices regardless of their actual safety needs.
2Productivity
If network infrastructure is made aware of device state, then resource allocation efficiency improves, but system complexity increases
Solution Approach 1:
The patent applies preliminary classification of mobile devices into safety levels before resource allocation decisions are made. By pre-defining safety categories and associated network parameters, the system simplifies the complexity burden - instead of continuously analyzing detailed device states, the network infrastructure works with pre-established safety classifications to determine appropriate resource allocation.
3Ease of manufacture
If fixed network parameters are used, then implementation is simple, but adaptability to different safety levels is poor
Solution Approach 1:
The system implements dynamic network parameters that automatically adapt to different mobile device safety levels. Rather than requiring complex manual configuration for each device type, the network infrastructure dynamically selects and applies appropriate parameter sets based on device safety classification, maintaining implementation simplicity while achieving high adaptability.
Data Source
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AI summary
A method performed by a first network node in a communication network is provided. The method includes determining (901), from a machine learning model at the first network node, a value for a network parameter for a cellular network for operation of a communication device based on a set of observations of the communication device, a safety level of the communication device, and a key performance indicator, KPI, of the cellular network. The method further includes signaling (903) the value of the network parameter to a core network node in the cellular network for a resource allocation of the cellular network. A method performed by a core network node in a cellular network is also provided.