Non-intrusive Wireless Traffic Analysis via Radio Characteristics
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Solution Overview
Problem
Existing methods for determining types of network traffic in heterogeneous wireless networks, such as deep packet inspection, are inefficient and intrusive, especially when traffic is encrypted or encoded using proprietary mechanisms, making it difficult to allocate radio resources effectively.
Innovation Solution
A non-intrusive traffic analysis system and method that monitors radio network characteristics, such as resource allocation, modulation schemes, and flow characteristics, to determine the type of network traffic session without observing packet types or headers, allowing for optimal resource allocation and network optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep packet inspection or parsing headers is used to determine network traffic types, then traffic classification accuracy is improved, but network intrusion and resource consumption increase
Solution Approach 1:
The patent introduces radio network characteristics as an intermediary parameter to indirectly classify traffic types. Instead of directly inspecting packet contents (which causes intrusion), the system monitors characteristics like resource allocation patterns, modulation schemes, and flow behavior that emerge from the radio interface, providing traffic classification without compromising packet integrity or security.
Solution Approach 2:
The patent replaces the mechanical packet inspection approach with a statistical pattern recognition system. Instead of manually parsing packet headers and payloads, the system uses algorithms to detect patterns in radio network characteristics, substituting complex mechanical inspection with automated statistical analysis that is less intrusive and more efficient.
2Measurement precision
If deep packet inspection or parsing headers is used to determine network traffic types, then traffic classification accuracy is improved, but processing resource consumption increases
Solution Approach 1:
The patent extracts only the necessary characteristics from the radio network interface (such as resource allocation patterns, modulation schemes, and flow characteristics) for traffic classification, rather than processing entire packets or all packet headers. This selective extraction significantly reduces processing load while maintaining classification accuracy.
Solution Approach 2:
The patent applies partial action by monitoring only specific radio network characteristics relevant to traffic classification, rather than performing complete packet inspection. This partial monitoring approach provides sufficient information for accurate traffic type determination while consuming far fewer processing resources.
3Measurement precision
If existing traffic analysis methods are used, then traffic type identification is achieved, but scalability and implementation complexity increase
Solution Approach 1:
The patent creates a universal traffic analysis framework that works across different network types and traffic protocols by focusing on common radio network characteristics. This multi-functional approach enables the same classification system to handle diverse traffic types (voice, video, data) and network technologies without requiring protocol-specific complex processing logic.
Data Source
AI summary
Systems, methods, and processing nodes for obtaining radio network characteristics associated with network traffic, determining a type of network traffic and/or network session, allocating resources towards a network traffic session based on the type thereof (or type of network traffic), and performing additional network optimization operations based thereon, including but not limited to metering, billing, and service adjustments such as reallocation of resources. Consequently, resource usage patterns of different data types can be made predictable to a network operator, enabling optimal allocation of network resources, adjustments of latency, and thus providing an optimal user experience.