Network Node Signal Prediction for Proactive 5G Handover
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Solution Overview
Problem
Wireless communication networks face challenges with quick signal drops due to higher frequencies used in 5G, particularly in urban environments where obstacles cause sudden signal quality variations, leading to dropped services.
Innovation Solution
A network node predicts future signal quality variations based on historical data from served UEs, adjusting network configurations to mitigate signal drops, and UEs measure and report signal quality to facilitate proactive network adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If higher frequency bands are used in 5G to increase capacity and data transfer speeds, then the ability to support high data transfer speeds without interference increases, but shadowing and penetration loss increase leading to quick signal drops
Solution Approach 1:
The network node performs preliminary actions by predicting future signal quality variations using historical data from one or more first UEs before the actual signal drop occurs. This allows the network to proactively adjust network configuration for the second UE, such as preparing alternative communication paths or adjusting transmission parameters, thereby maintaining service continuity despite the inherent instability of high-frequency signals
2Measurement precision
If historical data from multiple UEs is collected and processed to predict signal quality variations, then the accuracy of prediction improves, but the complexity of data processing and network configuration increases
Solution Approach 1:
The network node uses signal quality data from one or more first UEs as a copy or reference model to predict the behavior of a second UE. Instead of directly measuring and analyzing the second UE's signal in real-time, the system copies the historical signal quality patterns from other UEs and applies them to predict future variations, reducing the need for complex real-time processing while maintaining prediction accuracy
Data Source
AI summary
Embodiments herein relate to a method performed by a network node (110) for handling signal quality variations for a UE (120). The network node (110) predicts a future signal quality variation indicative of a path of movement for a second UE (120b) being served by the network node (110). The predicting is based on historical data of previous signal quality variations for one or more first UEs (120a) having been served by the network node (110). The historical data of previous signal quality variations is indicative of a path of movement of the one or more first UEs (120a). The network node (110) determines to change a network configuration for the second UE (120b) based on the predicted signal quality variation. The network node (110) initiates the change of the network configuration for the second UE (120b). Embodiments herein further relate to a method performed by a UE (120a, 120b) for handling signal quality variations for the UE (120a, 120b).