Radio Node Software Updates with Coverage-Aware Scheduling
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Solution Overview
Problem
Bulk firmware updates in wireless mobile networks, particularly in Virtualized Central Units (VCUs) or Open CUs, often result in catastrophic scenarios such as coverage blackouts and steep drops in hand-over success due to random selection and sequential updates, which are time-consuming and difficult to manage.
Innovation Solution
Implementing a smart scheduler that monitors radio nodes for coverage and status information, performs node clustering based on neighbor node compensation capacity, and schedules updates using artificial intelligence to minimize network impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If bulk firmware updates are performed by randomly selecting radio nodes, then update efficiency is improved, but network reliability deteriorates due to coverage blackouts and hand-over success drops
Solution Approach 1:
The patent segments the network into clusters of radio nodes based on geographic proximity and dependency relationships. Instead of updating nodes randomly across the entire network, the system divides nodes into manageable clusters that can be updated sequentially, ensuring that at least one node in each cluster remains operational during updates. This segmentation prevents widespread coverage blackouts while maintaining update efficiency.
Solution Approach 2:
The patent performs preliminary actions by assessing the impact of potential node failures before executing updates. The system evaluates which nodes are critical for maintaining coverage and hand-over success, and schedules updates to non-critical nodes first. This preliminary assessment allows the system to plan update sequences that minimize disruption to network reliability.
2Reliability
If sequential updates are performed one element after another, then network reliability is maintained, but update time increases significantly
Solution Approach 1:
The system performs preliminary clustering of radio nodes based on geographic and dependency relationships before executing updates. This preorganization allows the system to parallelize updates across multiple clusters while maintaining reliability, significantly reducing total update time compared to strict sequential processing of individual nodes.
Solution Approach 2:
The patent implements dynamic scheduling that adjusts update sequences based on real-time network conditions. The system monitors network traffic patterns and node status, allowing it to optimize update timing and sequencing to minimize overall update duration while maintaining reliability constraints.
3Speed
If updates are executed during peak traffic hours, then update speed is improved, but service quality deteriorates due to increased network load
Solution Approach 1:
The system dynamically determines optimal update timing based on monitored network traffic patterns. Instead of fixed scheduling, the system identifies periods of low network activity and schedules updates during those windows, automatically adjusting to maintain both update speed and service quality by avoiding peak traffic periods.
Solution Approach 2:
The patent implements feedback mechanisms that continuously monitor network traffic and node status during the update process. This feedback allows the system to adjust update timing and sequencing in real-time, preventing service quality degradation by detecting and responding to increasing network load conditions.
Data Source
AI summary
Radio nodes are monitored to obtain radio node coverage information and to obtain radio node status information. Based on the monitoring, coverage information and handover information for the radio nodes is collected, and transport traffic information and subscriber count information for the radio nodes are collected. Based on the radio node coverage information, node clustering is performed to identify one or more batches of source nodes to update based on a compensation capacity of neighbor nodes to the source nodes. Based on the one or more batches of source nodes identified from the node clustering, and on the radio node status information, a scheduling operation is performed to choose a time-slot for executing an update to at least one of the one or more batches of source nodes.