Neural Network Packet Analyzer for Adaptive BER Thresholds
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Solution Overview
Problem
Characterizing the transmission qualities of packet-switching networks is challenging due to errors from format translations and changing bit-error-rates (BER) across heterogeneous networks, especially when alternative switching paths are introduced, making fixed threshold measurements inadequate.
Innovation Solution
A packet-network analyzer system utilizing neural network techniques to process raw digital data, generating adaptable error threshold values and identifying non-obvious behavioral patterns, thereby characterizing network conditions dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed threshold measurements are used to characterize packet-switching networks, then measurement simplicity is maintained, but measurement precision deteriorates due to changing BER and heterogeneous network conditions
Solution Approach 1:
The patent implements dynamic threshold adjustment by training neural networks on historical BER data to adapt to changing network conditions. The system continuously learns from incoming data and updates its characterization thresholds, transforming the static measurement approach into a dynamic one that adapts to heterogeneous network environments and varying traffic patterns.
Solution Approach 2:
The patent introduces neural networks as an intermediary layer between raw BER measurements and network characterization. This intermediary processes the raw data, learns patterns across different network segments, and generates adaptive thresholds, thereby improving measurement precision without requiring direct complex analysis of heterogeneous network conditions.
2Reliability
If alternative switching paths are introduced to improve network reliability, then network adaptability improves, but the difficulty of detecting and measuring transmission faults increases
Solution Approach 1:
The patent segments the packet-switching network into multiple measurable sections and applies separate neural network analysis to each segment. By dividing the complex multi-path network into manageable sections, the system can identify which specific path or segment is experiencing faults, thereby reducing the difficulty of fault detection despite the presence of alternative paths.
Solution Approach 2:
The patent implements feedback mechanisms where BER measurements from all network paths are continuously collected, analyzed by neural networks, and used to update the system's understanding of network conditions. This feedback loop enables the system to adapt to path changes and identify faults by comparing expected versus actual performance across alternative switching paths.
3Adaptability or versatility
If format translations are performed across multiple network links to enable heterogeneous communication, then network versatility improves, but transmission errors increase due to translation defects
Solution Approach 1:
The patent introduces neural networks as an intermediary that analyzes BER patterns across format translation points. Rather than trying to eliminate the translations themselves (which would reduce versatility), the system uses the neural network to learn and identify error patterns introduced by translations, thereby characterizing network performance despite the presence of these harmful factors.
Solution Approach 2:
The patent changes the approach from trying to maintain fixed BER thresholds to dynamically adjusting thresholds based on learned patterns from historical data. By changing the parameter from a fixed value to a dynamic, learned value, the system can accommodate the increased error rates from format translations while maintaining accurate network characterization.
Data Source
AI summary
A packet-network analyzer system for characterizing network conditions of a packet-network-under-test is provided. In this regard, one such system can be broadly summarized by a representative analyzer system that incorporates a data collection element to receive the raw digital data from a host analyzer, a data selection element to receive the raw digital data, a data processing element to process the selected data set to generate a normalized data set, a neural processing module to process the normalized data set to generate a set of rules and relationships, and a data mining module that uses the rules and relationships to generate a mined data set from the selected data set, the mined data set being used to characterize a packet-network-under test.