Predictive Traffic Status Signaling for 5G Network Load Balancing
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Solution Overview
Problem
Current self-configuration and self-optimization techniques in 5G wireless networks are reactive and delay-prone, failing to promptly address network performance degradation due to increased interference and traffic load, leading to inefficiencies in resource management and user equipment (UE) handovers.
Innovation Solution
Implementing methods for network nodes to receive and analyze traffic status information from neighboring nodes, including measurements and predictions, to proactively adjust configurations, perform mobility load balancing, and optimize communication settings, thereby enhancing spectral efficiency and throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive self-configuration and self-optimization techniques are used, then current network conditions can be monitored, but delays occur in addressing performance degradation
Solution Approach 1:
The patent implements predictive mechanisms that perform preliminary actions by forecasting future traffic loads and interference conditions before they actually degrade network performance. Network nodes use measurement data and prediction algorithms to anticipate problems and proactively adjust configurations, avoiding the reactive delay inherent in traditional approaches.
Solution Approach 2:
The system dynamically transitions from static, reactive configuration adjustments to dynamic, predictive adjustments. Network nodes continuously update their configurations based on real-time measurements and predictions, enabling adaptive response that anticipates performance degradation rather than reacting to it after it occurs.
2Productivity
If traffic status information is exchanged between network nodes, then proactive resource management is enabled, but signaling overhead increases
Solution Approach 1:
The patent reuses existing signaling message formats and procedures for multiple purposes. Traffic status information is embedded within existing resource status reporting and handover signaling frameworks, allowing the same signaling infrastructure to serve both traditional resource management functions and the new predictive information exchange, thereby avoiding proportional increases in overall signaling overhead.
Solution Approach 2:
The system selectively exchanges traffic status information based on changing network conditions and node capabilities. Not all nodes exchange all types of information at all times - the signaling is adapted dynamically based on whether predictive information is actually needed for current resource management decisions, reducing unnecessary signaling traffic.
Data Source
AI summary
Embodiments include methods for a first network node of a wireless network. Such methods include receiving, from a second network node of the wireless network, a first message comprising traffic status information for the second network node and performing one or more of the following operations based on the traffic status information: predicting a change in load and/or interference in a coverage area of the first network node; adjusting configurations of one or more cells and/or one or more beams served by the first network node; requesting the second network node to adjust configurations of one or more cells and/or one or more beams served by the second network node; mobility load balancing with respect to one or more user equipment (UEs) served by the first network node; and configuring one or more UEs served by the first network node to use communication settings that are more robust to interference.