Radio Node Cluster Batching for Coverage Continuity
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Solution Overview
Problem
Current bulk firmware updates in radio access networks cause catastrophic scenarios such as coverage blackouts and handover failures due to random selection of radio nodes, without considering the impact on network coverage and user connectivity.
Innovation Solution
A smart scheduler that uses artificial intelligence and unsupervised machine learning to sequence and prioritize neighbor nodes for compensation, ensuring minimal disruption during firmware updates by clustering nodes based on coverage, handover success, and hotspot importance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If bulk firmware updates are performed by randomly selecting radio nodes, then firmware updates can be completed, but coverage blackouts and handover failures occur
Solution Approach 1:
The patent segments the firmware update process into multiple batches, where radio nodes are divided into different update groups. Each batch is updated sequentially rather than simultaneously, ensuring that sufficient nodes remain active to maintain network coverage and handover functionality during the update process.
Solution Approach 2:
The system performs preliminary analysis of neighbor node relationships and compensating capacity before executing firmware updates. It identifies which nodes can compensate for others during updates and pre-determines optimal update sequences, preventing coverage blackouts before they occur.
2Ease of manufacture
If radio nodes are shut down for firmware updates, then firmware can be updated, but network coverage and handover success rates deteriorate
Solution Approach 1:
The patent introduces compensating neighbor nodes as intermediaries that take over the coverage and handover functions of nodes undergoing firmware updates. The system identifies neighbor nodes with sufficient compensating capacity and configures them to serve as temporary replacements, ensuring continuous network service during updates.
Solution Approach 2:
The system dynamically adjusts operational parameters such as batch size, update timing, and neighbor node selection criteria based on network conditions, traffic patterns, and node capabilities. This allows optimization of the update process to minimize impact on network performance.
3Ease of operation
If neighbor nodes are selected without prioritization, then update scheduling is simple, but compensating capacity is insufficient
Solution Approach 1:
The patent implements a priority-based selection mechanism that considers multiple parameters including compensating capacity, geographic location, current load, and historical performance. Nodes are ranked based on these parameters, and the highest-priority nodes are selected for compensation roles, ensuring adequate capacity while maintaining manageable complexity through automated scoring.
Data Source
AI summary
A master node list is filtered. A node is a network node configured to create, receive, or transmit information, into an isolated nodes grouping and a filtered nodes grouping based on a single-coverage threshold. For each node in the filtered nodes grouping, a priority-based sequence of compensating neighbor nodes is sequenced. Compensating neighbor nodes are prioritized in terms of compensating capacity. A collective neighbor compensation performance is determined based on an overall compensation provided by the compensating neighbor nodes for a given node that is to be shut down. Each node from the master node list is distributed into one or more batches, based on a batch criteria.