Predictive Radio Network Performance Management via Spatiotemporal Sensor Correlation
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Solution Overview
Problem
5G wireless networks face challenges in maintaining performance due to dynamic environmental changes, which can lead to radio network degradation below acceptable levels for critical applications like URLLC, as traditional reactive methods often fail to prevent further degradation in time.
Innovation Solution
A predictive approach using associated sensor and radio network information data samples to perform preemptive corrective actions, such as handovers or modulation changes, based on spatiotemporal data correlations to anticipate and mitigate performance drops.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional reactive methods are used to maintain radio network performance, then the system structure remains simple, but the network performance degrades below acceptable levels for critical applications
Solution Approach 1:
The patent applies preliminary action by using sensor data to predict future radio network performance degradation before it occurs. The system correlates sensor measurements (temperature, humidity, pressure, motion) with radio network key performance indicators, identifies patterns indicating upcoming degradation, and triggers preemptive corrective actions such as handovers or parameter adjustments, thereby maintaining reliability without requiring complex real-time intervention systems
Solution Approach 2:
The patent implements feedback by continuously monitoring both sensor data and radio network performance metrics, correlating them to identify environmental factors affecting network performance. The system uses this feedback loop to adjust network parameters dynamically based on predicted degradation patterns, resolving the contradiction by making the system adaptive rather than statically complex
2Reliability
If predictive approaches with sensor data correlation are implemented, then network performance reliability improves, but data processing complexity increases
Solution Approach 1:
The system performs preliminary correlation analysis between sensor data and radio network KPIs during normal operation to establish baseline patterns. When predictive degradation is detected, corrective actions are triggered in advance, improving reliability while keeping processing complexity manageable through pattern recognition rather than continuous complex analysis
Solution Approach 2:
The patent applies partial action by selectively processing and correlating only the most relevant sensor data types and radio network metrics necessary for prediction. The system focuses computational resources on identifying critical degradation patterns rather than analyzing all possible data combinations, thereby improving reliability while controlling processing complexity through targeted analysis
3Reliability
If real-time monitoring and predictive actions are implemented, then network reliability for URLLC applications improves, but response time requirements become more challenging
Solution Approach 1:
The patent resolves this contradiction by performing predictive analysis in advance to identify upcoming performance degradation. Corrective actions such as handovers or parameter adjustments are triggered before degradation occurs, ensuring URLLC reliability requirements are met while avoiding the time loss associated with reactive responses to actual degradation events
Data Source
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AI summary
A technique may include receiving, from one or more sensors, sensor data samples; receiving radio network information data samples associated with a radio network; determining one or more associated sensor and radio network information data samples based on an association of one or more received sensor data samples with one or more of the received radio network information data samples; selecting at least some of the one or more associated sensor and radio network information data samples that are relevant to performance of the radio network; and forwarding the selected associated sensor and radio network information data samples for subsequent use.