Cell-on-Drone Orchestration for Predictive 5G Traffic Surges
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Solution Overview
Problem
Current drone-based cell implementations lack the capability to predict traffic surges and automate dynamic mitigation, leading to degraded network performance during demand spikes, and Wi-Fi deployment is complex and inefficient.
Innovation Solution
An automated system for end-to-end orchestration of machine learning-enabled and software-defined network-equipped drones (COD) that predicts traffic surges and dynamically deploys drone clusters with intelligent SDN functionality to offload network traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual control is used for drone-based cell implementations, then operational simplicity is maintained, but network performance degrades during traffic surges due to lack of predictive capability and automated mitigation
Solution Approach 1:
The drone-based cell system performs self-monitoring of network traffic conditions and self-deployment to appropriate locations. The system automatically detects traffic surges and mitigates them without manual intervention, enabling the system to serve itself while maintaining high reliability during demand spikes
Solution Approach 2:
The system continuously monitors network traffic conditions and uses this feedback to automatically deploy drones to locations experiencing traffic surges. This closed-loop feedback mechanism enables predictive capability and automated mitigation, resolving the contradiction between manual operational simplicity and automated performance optimization
2Quantity of substance
If Wi-Fi deployment is used to offload traffic, then network capacity increases, but deployment complexity and inefficiency worsen
Solution Approach 1:
The patent replaces complex mechanical Wi-Fi deployment infrastructure with autonomous drone-based cellular nodes. These drones carry compact cellular base station equipment and can be rapidly deployed without complex installation procedures, reducing deployment complexity while maintaining network capacity expansion
Solution Approach 2:
The system transitions from static, complex Wi-Fi infrastructure to dynamic, mobile drone-based cells that can be rapidly deployed and repositioned. This dynamic deployment approach simplifies the overall system while providing flexible network capacity expansion where needed
3Reliability
If fixed access network capacity is maintained, then system stability is preserved, but network performance degrades during demand spikes
Solution Approach 1:
The system transforms fixed network capacity into dynamic, adjustable capacity by deploying mobile drone-based cells. These drones can be rapidly deployed to locations experiencing demand spikes, enabling the network to adapt its capacity dynamically while maintaining overall system stability through controlled deployment protocols
Solution Approach 2:
The system uses machine learning to predict traffic surges before they occur and pre-deploys drones to anticipated high-demand locations. This preliminary action enables the network to maintain high reliability during demand spikes by having capacity ready in advance, rather than reacting after performance degradation begins
Data Source
AI summary
An automated system provides end-to-end (E2E) orchestration for machine learning (ML)-enabled and software-defined network (SDN)-enabled reactive and predictive (e.g., 5G) cell-on-drone (COD) dispatch and deployment. The system may be configured to detect or predict traffic surges (e.g., relative to typical or baseline levels) in a given cell site and orchestrate provisioning of resources, including COD deployment, to address the surge. COD deployment may involve one or more clusters or colonies of drones equipped with low-powered cellular radio access (or small cell) nodes, where each cluster includes (or is led by) one or more SDN equipped drones that provide intelligent SDN-on-drone (SOD) functionality.