Sector Capacity Analysis Using Time-Trending Data
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Solution Overview
Problem
Current methods for estimating sector capacity in data-only wireless networks are inaccurate and fail to provide leading indicators for future capacity needs, relying on simplistic approaches or outdated models that do not account for varying resource allocation and link quality.
Innovation Solution
A time-trending analysis based on data communication performance measurements, such as time needed to complete data transfers, is performed using logarithmic linear regressions to determine current capacity and predict future needs, allowing for more accurate estimation and planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional Erlang models are used to estimate sector capacity, then discrete and equal capacity resources can be calculated, but the model becomes inaccurate for data-only networks where transmission time is the limited resource and users are allocated differing amounts of time based on link quality
Solution Approach 1:
The patent changes the fundamental parameters of the capacity model from Erlang's discrete user-counting approach to continuous time-based measurements. It uses time-to-complete data transfers, total transmitted data, and average number of users as continuous variables, applying logarithmic linear regression to model the relationship between these parameters and sector capacity, making the model adaptable to data-only networks where transmission time is the constrained resource
Solution Approach 2:
The patent replaces the mechanical Erlang table lookup method with a statistical regression-based system. Instead of using predetermined Erlang tables based on discrete user counts, the system continuously collects performance measurement data and uses logarithmic linear regression to dynamically estimate sector capacity, providing a more flexible and accurate model for modern data-only networks
2Ease of manufacture
If fixed maximum theoretical capacity values are assigned to all sector-carriers, then planning can be based on theoretical calculations, but the approach fails to account for varying actual capacity due to height above average terrain, clutter environment, and geographic distribution of users
Solution Approach 1:
The patent enables the network system to automatically measure and report its own performance characteristics. Base stations continuously collect and report time-to-complete data transfer measurements, total transmitted data, and average user counts. This self-measuring capability allows the system to generate accurate, location-specific capacity data without requiring manual site surveys or theoretical calculations, resolving the contradiction between simplicity and accuracy
Solution Approach 2:
The patent implements a feedback mechanism where actual network performance measurements are continuously collected from base stations and used to update sector capacity estimates. The system uses logarithmic linear regression on the collected performance data to dynamically adjust capacity models, creating a closed-loop system that continuously improves accuracy based on real-world operating conditions rather than relying on fixed theoretical values
3Measurement precision
If lagging indicators such as average data speed or drive testing results are used for capacity planning, then current network performance can be measured, but these methods cannot effectively predict future capacity deficiencies
Solution Approach 1:
The patent performs preliminary capacity assessment by continuously collecting and analyzing performance measurement data to estimate current sector capacity before actual congestion occurs. The logarithmic linear regression model extrapolates from current trends to predict future capacity deficiencies, enabling network operators to take preliminary actions such as deploying additional base stations or upgrading capacity before service degradation becomes apparent to users
Solution Approach 2:
The system establishes continuous feedback loops where performance measurements are constantly collected and analyzed to update capacity estimates. This ongoing monitoring and prediction capability allows the system to detect capacity trends in real-time and alert operators to future deficiencies before they impact service quality, transforming static lagging indicators into dynamic leading indicators
Data Source
AI summary
Infrastructure network service measurements of time needed to complete data transfers are used to determine the capacity of a technology sector of a wireless packet data communication base station, such as a 1xEV-DO sector, using infrastructure network service measurements. The process, for example, may predict when the radio-frequency link between wireless data subscribers and a wireless base station becomes sufficiently congested that each user experiences reduced data speeds. The determination of capacity can be made down to the sector-carrier (a single carrier within a base station sector) level. The prediction can be cast in terms of time, which allows network service providers to plan the growth of their base stations to meet subscriber needs.