Wireless Burstiness Profile Determination via DCT Analysis
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Solution Overview
Problem
Existing methodologies fail to accurately measure burstiness in data traffic within communication networks, leading to network performance degradation due to self-similar traffic patterns, which cause fluctuations in packet delay and jitter, affecting application performance.
Innovation Solution
Active wireless devices in a communication system are detected, prioritized, and provided with data in various modulation and coding schemes to determine a burstiness metric, allowing for the creation of a burstiness profile that accounts for traffic behavior across different schemes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If periodic sample measurements are used to measure network utilization, then average throughput can be measured, but burstiness of the samples cannot be measured
Solution Approach 1:
The patent replaces traditional periodic sampling methodology with a mathematical transformation approach using Discrete Cosine Transform (DCT) to extract burstiness characteristics. This substitution enables precise burstiness measurement by transforming the measurement from time-domain sampling to frequency-domain analysis, where burstiness manifests as specific spectral components that can be quantified.
Solution Approach 2:
The patent changes the measurement parameter from average throughput (first-order statistic) to burstiness metric (higher-order statistical characteristic). By applying DCT to the throughput samples and analyzing the spectral components, the system extracts burstiness information that is invisible to traditional averaging methods, thereby measuring a different parameter that captures traffic variability.
2Productivity
If self-similar data traffic patterns are present, then network utilization can be maintained, but packet delay fluctuations and jitter increase
Solution Approach 1:
The patent implements a feedback mechanism where burstiness metrics are continuously measured and used to inform network management decisions. By monitoring the DCT-based burstiness indicator, the system can detect self-similar traffic patterns early and trigger appropriate responses such as traffic shaping, rate limiting, or resource allocation adjustments to prevent delay fluctuations and maintain service quality.
3Ease of manufacture
If voice traffic models are used to describe data traffic, then modeling simplicity is maintained, but accuracy in describing data traffic behavior is lost
Solution Approach 1:
The patent fundamentally changes the parameter set used to characterize traffic from voice traffic parameters (designed for constant bitrate, periodic patterns) to data traffic parameters (capturing self-similarity, burstiness, heavy tails). The DCT-based burstiness metric specifically captures the self-similar nature of data traffic that voice models cannot represent, providing accurate characterization without requiring complex alternative models.
Data Source
AI summary
Active wireless devices in communication with an access node of a wireless communication system are detected and prioritized, and a group of the active wireless devices is selected. Data is provided to each selected wireless device, a burstiness metric is received based on the provided data, and a burstiness profile of the wireless communication system is determined.