Radio Node Software Updates Using Coverage-Aware Sequencing
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Solution Overview
Problem
Centrally controlling network software updates in wireless mobile networks results in catastrophic scenarios like coverage blackouts and steep drops in hand-over success due to random node selection, and sequential updates are time-consuming and difficult to manage.
Innovation Solution
Implementing a Smart Scheduler that uses Neighbor Node sequencing methods, including formulas and Machine Learning, to prioritize nodes based on coverage, handover success rate, and total handover attempts, enabling automatic bulk updates with minimal service impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sequential updates are performed one element after another, then service impact is minimized and spare capacity is maximized, but the update process takes an excessive amount of time
Solution Approach 1:
The patent segments the network elements into different priority groups (critical, important, normal) and schedules updates in batches according to their priority level. This allows parallel processing of multiple elements within the same priority group while maintaining service continuity for higher priority elements, thus reducing total update time without compromising service impact.
Solution Approach 2:
The patent implements a dynamic scheduling approach where the update sequence is adjusted based on real-time network conditions, traffic patterns, and element interdependencies. The system can dynamically reprioritize elements and modify the update schedule to optimize both service continuity and update speed, rather than following a fixed sequential order.
2Productivity
If bulk firmware updates are performed by randomly selecting radio-nodes, then updates can be applied quickly, but catastrophic scenarios such as coverage blackout and steep drop in hand-over success occur
Solution Approach 1:
The patent performs preliminary analysis of network topology, traffic patterns, and element interdependencies before scheduling updates. Critical dependencies and potential risk scenarios are identified in advance, allowing the system to pre-determine a safe update sequence that avoids catastrophic failures while maintaining efficient update speed.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor network performance and update progress in real-time. Based on this feedback, the scheduling algorithm can adjust the update sequence to prevent coverage blackouts and hand-over failures, ensuring network stability while maintaining productivity.
3Reliability
If upgrades are performed during nighttime maintenance windows, then service impact is minimized due to low traffic volumes, but the ability to take into consideration many variables involved with upgrading thousands of nodes is difficult
Solution Approach 1:
The patent implements an automated scheduling system that independently analyzes network variables, traffic patterns, and element dependencies to generate optimal update sequences. This self-service approach eliminates the need for manual scheduling complexity while maintaining service impact minimization during maintenance windows.
Solution Approach 2:
The system dynamically adjusts scheduling parameters such as update batch size, priority thresholds, and time windows based on network conditions and variables. This allows the system to handle thousands of nodes efficiently by adapting parameters rather than requiring complex manual configuration.
Data Source
AI summary
Neighbor Nodes of a Source Node to be upgraded are sequenced by a Neighbor Node Sequencer. A first Neighbor Node sequencing method or a second Neighbor Node sequencing method is applied by the Neighbor Node Sequencer. In applying a first Neighbor Node sequencing method, Neighbor Nodes are iteratively sorted using a first formula until a calculated Gain in Collective Coverage is not greater than a predetermined Coverage Gain Threshold. A second formula is applied based on Machine Learning. In applying a second Neighbor Node sequencing method, Neighbor Nodes are sorted using the second formula based on the Machine Learning. Neighbor Nodes are sorted using a variant of the first formula.