Traffic Estimation Using State Space Models for Overloaded Network Lines
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Solution Overview
Problem
Existing methods for bandwidth design in network lines are inadequate for overloaded states, as they fail to accurately estimate potential traffic demand and account for noise patterns in cyclic traffic fluctuations, leading to insufficient communication quality.
Innovation Solution
A traffic estimation device using a state space model that inputs observed traffic and communication quality data from both overloaded and normal lines to estimate parameters, accounting for cyclical patterns and the influence of communication quality on traffic demand, allowing for accurate calculation of potential traffic demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional band design approaches are used in overloaded states, then bandwidth calculation is simple, but traffic demand estimation is inaccurate due to communication quality degradation
Solution Approach 1:
The patent introduces a state space model as an intermediary framework that processes both traffic volume data and communication quality data. This model acts as a mediator between the observed metrics and the underlying traffic demand, enabling accurate estimation even in overloaded states by accounting for communication quality degradation effects on user behavior.
Solution Approach 2:
The patent implements feedback mechanisms by using Kalman filtering to continuously update the state space model with new observations. The model incorporates feedback loops where communication quality metrics feed back into the traffic demand estimation process, allowing the system to adapt and correct estimates based on actual network conditions and user response to quality degradation.
2Measurement precision
If direct comparison of time series data is used, then processing is simple, but cyclic traffic patterns and noise cannot be properly handled
Solution Approach 1:
The patent applies dynamic modeling through the state space framework, which explicitly models the temporal evolution of traffic patterns. The model captures cyclic variations and noise dynamics by representing traffic demand as a dynamic process that changes over time, allowing differentiation between systematic cyclic patterns and random noise components.
Solution Approach 2:
The patent performs preliminary decomposition of traffic data into cyclic components and noise components before final estimation. The state space model pre-processes the time series data by separating systematic variations (cyclic patterns) from random fluctuations, enabling more accurate subsequent analysis of underlying traffic demand.
3Reliability
If bandwidth is increased to resolve communication quality degradation, then communication quality improves, but latent traffic demand becomes apparent requiring further bandwidth increases
Solution Approach 1:
The patent performs preliminary estimation of latent traffic demand using the state space model before actually increasing bandwidth. By analyzing communication quality degradation patterns and user behavior responses, the model predicts the underlying traffic demand that would be revealed upon quality improvement, enabling proactive and optimized bandwidth planning rather than reactive increases.
Data Source
AI summary
A first time series of traffic volume and a first time series of communication quality that are observed in a first communication line in which traffic demand is equal to or greater than a line bandwidth and a second time series of traffic volume and a second time series of communication quality that are observed in a second communication line in which traffic demand is less than a line bandwidth are input to a state space model to estimate values of a parameter group of the state space model, the traffic demand is calculated based on the values of the parameter group, and the state space model is a state space model in which the first time series of traffic volume and the second time series of traffic volume are generated from a third time series indicating cyclicity of traffic common to the first communication line and the second communication line under influence of the first time series of communication quality and the second time series of communication quality on the traffic demand, thereby supporting appropriate band design of an overload line in which traffic demand is equal to or greater than a line bandwidth.