Satellite Bandwidth Optimization via Subscriber Usage Prediction
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Solution Overview
Problem
High throughput satellite systems face challenges in efficiently managing capacity allocation due to fixed bandwidth, leading to inefficient resource utilization as the number of subscribers increases, with heavy users consuming more resources than needed, limiting service quality for others.
Innovation Solution
A method and apparatus that create a model to predict bandwidth usage by analyzing subscriber data patterns, recommending optimized service plans to allocate resources effectively, allowing for better bandwidth management and motivating heavy users to upgrade their plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If additional satellites are launched to increase bandwidth, then system capacity increases, but cost and complexity increase significantly
Solution Approach 1:
The patent implements dynamic bandwidth allocation through a learning system that continuously monitors subscriber usage patterns and adjusts data cap assignments in real-time. This dynamic approach allows the fixed satellite bandwidth to be optimally distributed among varying numbers of subscribers without requiring additional satellites or infrastructure changes.
Solution Approach 2:
The system changes the parameter of data cap limits based on learned subscriber behaviors and current network conditions. By adjusting these software-defined parameters rather than physical infrastructure, the system can adapt bandwidth allocation to match actual usage patterns, effectively increasing capacity utilization without adding hardware complexity.
2Productivity
If data caps are set low to conserve bandwidth, then resource efficiency improves, but service quality for legitimate users deteriorates
Solution Approach 1:
The patent employs a feedback mechanism where the learning system continuously monitors actual subscriber usage patterns, compares them against allocated data caps, and adjusts future allocations accordingly. This closed-loop feedback ensures that bandwidth efficiency is maintained while service quality is preserved, as the system learns to allocate appropriate limits based on actual needs rather than static assumptions.
Solution Approach 2:
The system performs preliminary learning during an initial period to establish baseline usage patterns before implementing data cap assignments. This preliminary action allows the system to pre-configure appropriate data limits based on learned behaviors, ensuring that service quality is maintained from the outset while bandwidth is efficiently allocated.
3Reliability
If data caps are set high to ensure service quality, then user satisfaction improves, but bandwidth is wasted on heavy users who don't need it
Solution Approach 1:
The patent implements dynamic adjustment of data caps based on continuously learned subscriber patterns. Instead of static high limits that waste bandwidth, the system adapts allocations in real-time, increasing limits for users who need more bandwidth and decreasing them for users with lower实际需求, thereby eliminating bandwidth waste while maintaining service quality for all users.
Solution Approach 2:
The system applies differentiated data cap allocations to individual subscribers based on their specific usage patterns and needs. Rather than a uniform high limit for all users, each subscriber receives a customized allocation that matches their actual requirements, preventing bandwidth waste on users who don't need high limits while ensuring adequate service quality for those who do.
4Productivity
If the system monitors detailed usage patterns to optimize allocation, then bandwidth management improves, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent implements a self-learning system that automatically monitors usage patterns, analyzes data, and adjusts allocations without requiring complex manual intervention or centralized control. The distributed learning approach allows each node to independently process and learn from local usage data, reducing overall system processing complexity while maintaining high bandwidth management efficiency.
Data Source
AI summary
An apparatus and method for optimizing selection of data cap limited service plans. A model is created in order to predict bandwidth usage by existing subscribers in a satellite communication system. The model is trained with usage data for all subscribers of the satellite communication system over a predetermined time interval, and used to analyze usage patterns of each subscriber. Bandwidth usage is predicted for each subscriber relative to an existing service plan based, at least one recommendation is generated for optimizing use of bandwidth in the satellite communication system based on the analysis and predicted bandwidth usage.