Packet Header Profiling for Adaptive Compression of Unknown Protocols
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Solution Overview
Problem
Existing header compression systems in mobile networks face challenges in dealing with new types of packet data, as they require pre-configured header profiles, which is inconvenient, especially for mobile stations where profile updates are difficult to manage.
Innovation Solution
A method and apparatus for analyzing data packets to generate profile data indicating how packet fields vary, allowing for dynamic implementation of compression schemes without prior knowledge of the protocol stack, enabling efficient compression and decompression of packet headers in mobile networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pre-configured header profiles are used for compression, then compression efficiency is improved for known protocol stacks, but the system cannot handle new or unknown protocol stacks
Solution Approach 1:
The system performs preliminary analysis of packet headers to generate profiles before compression is applied. By analyzing the behavior of header fields in advance (preliminary action), the system can dynamically create compression profiles that match the actual protocol stack being used, rather than relying on pre-configured profiles for known protocols only.
Solution Approach 2:
The compression system automatically analyzes incoming packets and generates its own profiles without requiring external configuration or pre-programming. The system serves itself by autonomously adapting to new protocol stacks through automatic profile generation based on observed packet patterns.
2Adaptability or versatility
If header profiles are manually updated for new protocols, then the system can handle new protocol stacks, but this is inconvenient and difficult for mobile stations
Solution Approach 1:
The mobile station automatically generates compression profiles by analyzing incoming packets without requiring manual intervention. The system updates its own profile database autonomously based on observed packet patterns, eliminating the need for inconvenient manual profile updates on mobile devices.
Solution Approach 2:
The profile database is made dynamic rather than static. Instead of requiring manual updates, the system continuously adapts its profiles based on real-time analysis of packet traffic, allowing automatic adaptation to new protocol stacks as they appear in the network.
3Adaptability or versatility
If packet headers are transmitted without compression, then all protocol stacks are supported, but message overhead increases and bandwidth utilization decreases
Solution Approach 1:
The compression system dynamically adapts its behavior based on the observed protocol stack. By continuously analyzing packet patterns and updating profiles in real-time, the system can apply efficient compression to known protocols while automatically adapting to handle new protocols, thus maintaining both compression efficiency and broad protocol support.
Solution Approach 2:
The system changes its compression parameters and strategies based on the observed characteristics of the protocol stack. By analyzing field behavior patterns (static, irregular, linear) and adjusting compression parameters accordingly, the system optimizes bandwidth utilization for different protocol types while maintaining adaptability to new protocols.
Data Source
AI summary
There is described a method of processing data packets in a data stream in a communication system. In the method, a plurality of data packets from the data stream are analyzed in order to generate profile data which indicates how one or more parts of each of the plurality of data packets varies from data packet to data packet. The profile data is then made available so that a data packet compression scheme that accords with the profile data can be implemented on data packets in the communication system.