AI-RRM Wireless Network Configuration Update Scheduling
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Solution Overview
Problem
Current Radio Resource Management (RRM) systems in wireless networks often cause disruptions and client disconnects due to inadequate consideration of network utilization patterns, particularly during periods of high activity, leading to inefficient configuration updates and increased energy consumption.
Innovation Solution
An AI-RRM system that analyzes telemetry data from wireless access points to identify busy periods and defer non-urgent RRM updates to non-busy times, applying configuration changes during periods of low network activity to minimize client disruptions and optimize network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If RRM updates are performed immediately after collecting network samples, then network configuration optimization is achieved, but client disconnects and network disruption occur during high-activity periods
Solution Approach 1:
The system collects and analyzes network telemetry data in advance to identify busy periods before they occur. By predicting high-activity periods ahead of time, the system can proactively schedule RRM updates for non-busy periods, preventing client disconnects before they happen while maintaining configuration optimization
Solution Approach 2:
The system dynamically adjusts the timing of RRM updates based on real-time network conditions and historical patterns. Instead of fixed scheduling, the system adapts update timing to match actual network utilization patterns, allowing flexible coordination between update performance and client activity levels
2Reliability
If RRM updates are deferred to non-busy periods, then client disconnects are reduced, but network performance degradation occurs during high-activity periods
Solution Approach 1:
The system performs preliminary analysis of telemetry data to identify patterns and predict future busy periods. By understanding network behavior patterns in advance, the system can make informed decisions about when to defer updates, ensuring that performance-critical periods are protected while still allowing optimization updates to occur
Solution Approach 2:
The system changes the timing parameter of RRM updates based on network conditions. By dynamically adjusting when updates occur rather than changing the updates themselves, the system maintains configuration quality while protecting client connectivity during high-activity periods
3Measurement precision
If network sampling is performed during peak hours, then accurate network telemetry is obtained, but energy consumption and computation power increase unnecessarily
Solution Approach 1:
The system performs preliminary identification of busy periods using historical data and patterns, then uses this knowledge to optimize when detailed sampling occurs. By pre-characterizing network behavior, the system can reduce the frequency or intensity of sampling during predictable low-activity periods while maintaining accuracy during critical periods
Solution Approach 2:
The system applies partial sampling strategies where the depth and frequency of network monitoring are adjusted based on predicted importance. During identified non-busy periods, reduced sampling may suffice, while during predicted busy periods, full-precision sampling is activated, avoiding unnecessary computation during low-stakes periods
Data Source
AI summary
The disclosed technology relates to determining a period in which a non-urgent RRM update should be deferred. The method may comprise applying a first update to an existing configuration of the plurality of wireless access points in the network based on an analysis of telemetry received from the plurality of wireless access points received over a period spanning at least two busy periods. The method may further comprise applying a second update that modifies the first preferred network configuration based on an analysis of telemetry received during the first busy period. The method may further comprise applying a maintenance update to the tweaked network configuration based on telemetry received during the next busy period.