Predictive Mobility Adjustment for 5G Network Stability
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Solution Overview
Problem
Current self-configuration and self-optimization techniques in 5G wireless networks are reactive, responding to current network conditions and UE traffic load, leading to significant delays in addressing performance degradations due to increased interference or resource utilization, resulting in reduced user data throughput and potential UE disconnections.
Innovation Solution
Implementing predictive methods where network nodes send and receive messages indicating predicted future adjustments to mobility-related settings, allowing proactive adjustments to handover trigger points and resource allocation based on anticipated load and traffic changes, enabling proactive management of network resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive self-optimization techniques are used to adjust mobility settings based on current network conditions, then the network can respond to current load and traffic conditions, but significant delays occur in addressing performance degradations due to increased interference or resource utilization
Solution Approach 1:
The patent applies predictive algorithms to forecast future network conditions (load, traffic patterns, interference levels) before actual performance degradations occur. Network nodes proactively adjust mobility settings such as handover trigger points and resource allocation in advance, eliminating the reactive delay inherent in traditional self-optimization techniques. This preliminary action allows the system to anticipate and prevent performance issues rather than responding after they manifest.
2Productivity
If reactive adjustments are made to mobility settings based on current measurements, then the network can adapt to present conditions, but user data throughput deteriorates due to delays in addressing performance issues
Solution Approach 1:
The system uses predictive models to forecast future network conditions and proactively adjusts mobility settings before throughput degradation occurs. By anticipating future load patterns and interference levels, the network can maintain optimal throughput by preparing handover parameters and resource allocations in advance, rather than reacting after degradation has begun.
Solution Approach 2:
The patent implements a feedback mechanism where network nodes continuously monitor current network conditions and compare them against predictive forecasts. This feedback loop allows the system to verify whether predictions align with actual conditions and adjust mobility settings accordingly, ensuring that proactive adjustments maintain throughput while adapting to real-time network behavior.
3Reliability
If proactive predictive adjustments are implemented to anticipate future load and traffic changes, then performance degradations can be prevented, but network complexity increases due to predictive algorithms and coordination mechanisms
Solution Approach 1:
The patent implements self-service by enabling network nodes to autonomously execute predictive adjustments without requiring centralized control or manual intervention. Each node uses its own predictive algorithms to forecast local conditions and independently adjusts mobility settings, reducing the complexity burden on centralized controllers while maintaining coordinated network-wide optimization through standardized protocols.
4Loss of time
If predictive algorithms are deployed to forecast future network conditions, then proactive mobility setting adjustments can be made, but computational resources and processing complexity increase
Solution Approach 1:
The patent applies partial action by implementing predictive algorithms at strategic network nodes rather than all nodes uniformly. The system performs predictive analysis only where it most impacts overall network performance, such as at border nodes between cells or areas with historically volatile conditions. This selective deployment reduces total computational resource consumption while maintaining effective proactive optimization where it matters most.
Data Source
AI summary
Embodiments include methods for a first network node of a wireless network. Such methods include sending, to a second network node in the wireless network, a first message comprising an indication of predicted future adjustments of mobility-related settings, of the first network node, for mobility of users between a coverage area of the first network node and an adjacent coverage area of the second network node. Such methods include receiving, from the second network node in response to the first message, a second message indicating one or more of the following: acknowledgement, confirmation, or rejection of the predicted future adjustments; and corresponding adjustments of mobility-related settings for the second network node. Other embodiments include complementary methods for a second network node and a third network node, as well as network nodes configured to perform such methods.