Cellular Traffic Shaping via Dynamic Cell-Based Pricing
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Solution Overview
Problem
Current cellular phone rate plans do not account for geographically based costs associated with cellular services, leading to underutilized or overutilized cell towers within the same rate region, resulting in negative revenue and the need for new equipment installation to meet demand, without a means to redirect calls to lower-cost cells.
Innovation Solution
Implementing dynamic pricing and discounts based on real-time usage data to encourage consumers to delay usage in high-traffic or high-cost cells and utilize low-traffic or low-cost cells within the same rate plan region, using a graduated pricing model that adjusts pricing dynamically in response to cell tower utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional area-based rate plans are used, then simplicity of pricing is maintained, but traffic imbalance among cell towers occurs leading to underutilized or overutilized towers
Solution Approach 1:
The patent applies local quality by transitioning from uniform area-based pricing to cell-specific dynamic pricing. Each cell tower is assigned individual pricing rates based on its real-time utilization metrics, allowing different parts of the service area to have differentiated pricing structures. This enables underutilized towers to attract more traffic through lower rates while overutilized towers can charge premium rates, thereby balancing load distribution across the network.
Solution Approach 2:
The patent implements dynamics by introducing real-time or near-real-time dynamic pricing that adjusts based on current cell tower utilization conditions. Pricing rates are not fixed but fluctuate according to measured traffic patterns, allowing the system to adapt to changing demand conditions and automatically balance load across towers without manual intervention.
2Productivity
If dynamic cell-based pricing is implemented, then traffic balancing among towers is achieved, but system complexity increases
Solution Approach 1:
The patent applies self-service by enabling the pricing system to automatically adjust rates based on real-time utilization data without requiring manual configuration or complex centralized control. The system monitors its own performance metrics and autonomously modifies pricing to achieve load balancing, reducing the need for complex external management infrastructure.
Solution Approach 2:
The patent implements feedback mechanisms where utilization data from each cell tower is continuously collected and fed back into the pricing system. This feedback loop allows the system to automatically adjust pricing rates in response to measured conditions, creating a self-regulating mechanism that balances traffic without requiring complex manual intervention or prediction algorithms.
3Measurement precision
If real-time utilization monitoring is deployed, then dynamic pricing accuracy is improved, but data collection and processing requirements increase
Solution Approach 1:
The patent applies partial action by implementing monitoring and dynamic pricing for only the critical parameters needed to achieve load balancing, rather than comprehensively tracking all possible network metrics. This selective approach focuses data collection on essential utilization measures, reducing processing overhead while maintaining sufficient accuracy for effective pricing decisions.
Data Source
AI summary
Changes in wireless service user behavior are encouraged and produced to shape utilization patterns among cells within a rate plan region by using shaping rules which define potential discounts from a standard charge rate under certain cell-specific utilization conditions, analyzing the shaping rules and utilization statistics of a cell upon service initiation, generating a discount from the standard rate for underutilized cells. The user is notified of these discount opportunities using text messages, icons, or other means. Over time, the users learn that at certain places and times, significant discounts are offered, and thus changes their behavior to take advantage of those discounts, thereby shaping traffic in a manner desired by the service provider.