Uplink Traffic-Based Compression Using Dynamic Dictionary Selection
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Solution Overview
Problem
Existing wireless communication systems face inefficiencies in data compression due to the need for separate processing of data packets with different traffic parameters, leading to increased latency, reduced throughput, and spectral inefficiency.
Innovation Solution
Implementing traffic-based data compression techniques using artificial intelligence and machine learning models to predict traffic parameters and select appropriate compression dictionaries and buffers, allowing for efficient classification and processing of data packets based on their type, thereby reducing latency and increasing throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate processing of data packets with different traffic parameters is implemented, then data compression can be performed, but latency increases and throughput decreases
Solution Approach 1:
The patent segments data packets into different groups based on traffic parameters (e.g., QoS flows, data types) and applies appropriate compression dictionaries to each segment. This allows simultaneous processing of multiple packet types without sequential bottlenecks, reducing overall latency while maintaining compression quality for each traffic type.
Solution Approach 2:
The system dynamically selects compression dictionaries based on real-time traffic parameters and packet characteristics. Instead of using a single static compression method, the system adapts the compression approach according to the specific traffic type, enabling faster compression for certain packet types while maintaining high compression ratios for others, thereby reducing overall processing time and latency.
2Productivity
If multiple compression dictionaries are used for different traffic types, then compression efficiency improves, but system complexity increases
Solution Approach 1:
The patent divides the compression system into multiple independent dictionary modules, each optimized for specific traffic types. This segmentation allows the system to maintain multiple specialized dictionaries without creating a monolithic complex system, as each dictionary can be independently managed, selected, and updated based on traffic requirements.
Solution Approach 2:
The system implements dynamic dictionary selection mechanisms that automatically choose the appropriate compression dictionary based on traffic parameters. This dynamic approach reduces the need for manual configuration and management of multiple dictionaries, thereby lowering operational complexity while maintaining high compression efficiency across different traffic types.
3Reliability
If frequent dictionary updates are performed to maintain compression quality, then compression efficiency is maintained, but processing time and resource consumption increase
Solution Approach 1:
The patent segments the dictionary management process by maintaining separate dictionaries for different traffic types. This allows updates to be performed independently for each dictionary based on specific traffic pattern changes, rather than requiring system-wide updates. Consequently, compression quality is maintained for updated traffic types without disrupting processing of other traffic types, preserving overall throughput.
Solution Approach 2:
The system implements dynamic dictionary update mechanisms that trigger updates only when specific traffic patterns or parameters change. This selective updating approach prevents unnecessary frequent updates, reducing processing overhead and resource consumption while maintaining compression quality for the specific traffic types that require updated dictionaries.
Data Source
AI summary
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a first wireless communication device may receive configuration information associated with uplink data compression, the uplink data compression being associated with at least one of multiple compression dictionaries or multiple compression buffers. The first wireless communication device may compress, in accordance with the configuration information, uplink data to generate compressed uplink data via at least one of: one or more compression dictionaries of the multiple compression dictionaries, or a compression buffer of the multiple compression buffers, and the one or more compression dictionaries or the compression buffer being selected in association with one or more traffic parameters of the uplink data. The first wireless communication device may transmit an uplink communication that includes the compressed uplink data. Numerous other aspects are described.