Modular Base Station Orchestration for Demand Spikes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The static nature of cell towers and network infrastructure makes it difficult for telecommunications service providers to manage permanent or temporary network demand spikes, leading to unsatisfactory service delivery during increased demand periods.
Innovation Solution
A system that orchestrates network resource movement and redirection using artificial intelligence to dynamically adjust the configuration and location of cell towers and other network infrastructure, incorporating modular components and machine learning for real-time optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If cell towers and network infrastructure are made static, then infrastructure stability and simplicity are improved, but the ability to manage permanent or temporary network demand spikes deteriorates
Solution Approach 1:
The patent applies dynamics by making base station components movable rather than fixed. Base stations can physically relocate to different geographic positions to follow user equipment and accommodate changing network demand patterns, thereby resolving the contradiction between infrastructure stability and demand management adaptability
Solution Approach 2:
The patent segments the network infrastructure into movable base station components that can be independently deployed and repositioned. This segmentation allows the system to maintain stable core infrastructure while enabling flexible edge components to adapt to demand changes
2Reliability
If AI-based real-time analysis and dynamic resource orchestration are implemented, then service reliability during demand spikes is improved, but system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where AI systems continuously analyze network data, detect demand triggers, and automatically orchestrate resource movements. This closed-loop feedback control enables reliable service during demand spikes while automating the complexity rather than increasing manual system complexity
Solution Approach 2:
The system employs self-service through autonomous AI-driven decision-making that automatically detects triggers and orchestrates base station movements without human intervention, managing service reliability while keeping operational complexity contained within the automated system
Data Source
AI summary
A computer-implemented method includes receiving information associated with a plurality of user equipment, wherein the information comprises location information for each of the plurality of user equipment and receiving information associated with a plurality of base stations. The computer-implemented method further includes analyzing the information associated with the plurality of user equipment or the information associated with the plurality of base stations. The computer-implemented method further includes in response to the analysis, detecting a trigger, wherein the trigger comprises reaching one or more thresholds. The computer-implemented method further includes based on the trigger, sending an alert to one or more of the plurality of user equipment or orchestrating movement of a component of each of the plurality of base stations.