UAV Cellular Base Stations for Demand-Based Coverage
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Solution Overview
Problem
Inadequate cellular phone coverage leads to issues such as dropped calls, low sound quality, and limited data/voice throughput, especially during events with increased user demand, due to factors like RF interference, physical obstacles, and zoning laws, which existing technologies struggle to address effectively.
Innovation Solution
A system and method that deploy unmanned aerial vehicles (UAVs) equipped with telecommunications devices to act as temporary cellular network towers, optimizing their placement based on predicted or actual increases in cellular usage demand to augment signal strength and capacity, using predictive methods and real-time data from various sources like social media, transportation, and infrastructure monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If fixed cellular towers are built to provide coverage, then coverage area is improved, but deployment difficulty and cost increase due to zoning laws and land acquisition
Solution Approach 1:
The patent transforms the static nature of traditional cellular towers into a dynamic system by using UAVs that can move and reposition themselves to provide cellular coverage. Instead of fixed infrastructure, the system employs mobile aerial base stations that can dynamically adjust their locations to follow user demand patterns, eliminating the need for permanent land-based installations and associated zoning challenges.
Solution Approach 2:
The patent moves the cellular infrastructure from the ground plane to the three-dimensional airspace. By deploying UAVs at elevated positions, the system creates coverage in a different spatial dimension, bypassing ground-based obstacles such as zoning restrictions, physical barriers, and land acquisition requirements while expanding coverage area.
2Productivity
If cellular tower capacity is increased to handle peak demand, then throughput is improved, but cost increases due to over-provisioning for low-capacity periods
Solution Approach 1:
The system dynamically adjusts cellular capacity by deploying additional UAVs or repositioning existing ones to high-demand areas only when needed. During low-demand periods, fewer UAVs are deployed or they are relocated, allowing the network to scale capacity flexibly rather than maintaining fixed over-provisioned infrastructure year-round.
Solution Approach 2:
The system uses predictive analytics to anticipate user demand patterns and pre-deploy UAVs to locations where high throughput will be needed before demand actually occurs. This preliminary positioning ensures capacity is ready when needed without requiring permanent over-provisioning of infrastructure in all potential high-demand areas.
3Productivity
If more cellular towers are deployed to cover high-demand areas, then capacity is improved, but deployment time and complexity increase
Solution Approach 1:
Rather than statically pre-deploying multiple towers to all potential high-demand areas, the system uses mobile UAVs that can be rapidly deployed and repositioned based on real-time demand conditions. This dynamic approach allows capacity to be scaled up quickly in response to specific events without the lengthy planning and installation processes required for fixed infrastructure.
Solution Approach 2:
The UAV-based cellular infrastructure serves multiple functions: it can be deployed for temporary events, relocated for different high-demand areas, and scaled flexibly. A single UAV platform can provide capacity in various locations and contexts, eliminating the need for location-specific fixed tower installations and reducing overall deployment complexity.
4Reliability
If fixed infrastructure is sized for peak capacity, then reliability during events is improved, but cost increases due to underutilization during normal periods
Solution Approach 1:
The system achieves peak-capacity reliability on-demand by deploying sufficient UAVs to high-demand areas only when events are anticipated or detected, rather than maintaining permanent peak-capacity infrastructure everywhere. During normal periods, fewer resources are deployed, eliminating waste while ensuring reliability when needed through rapid scalability.
Solution Approach 2:
The system uses predictive analytics to identify upcoming events and pre-deploy adequate cellular capacity to those specific locations before demand occurs. This ensures reliability during events without requiring permanent over-provisioning, as the additional infrastructure is prepared in advance only where and when needed based on predicted user convergence patterns.
Data Source
AI summary
System, method and computer program product for extending mobile device cellular carrier network coverage using unmanned aerial vehicles (UAVs) equipped with telecommunications devices to act as temporary mobile device cellular network towers. The system and method extends cellular phone coverage of a land-based cellular phone network by: receiving information for determining a predicted or actual increase in aggregate cellular phone usage demand in an area at a determined time; identifying, based on an the predicted or actual increased aggregate cellular phone usage demand for the area, whether there exists a mismatch of existing usage coverage compared with the determined increased aggregate demand for that area; determining, based on an identified mismatch, a plan for sending signals to and deploying one or more mobile cellular unmanned aerial vehicles (UAV) having telecommunications equipment configured to extend cellular phone network coverage of an existing land-based cellular phone network at the mismatched area.