Adaptive Distribution Matching for Variable-Rate Data Streaming
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Solution Overview
Problem
Existing data signal conversion and transmission methods face challenges in achieving high reliability and low power consumption, especially under high data load streaming conditions, as they often require fixed distribution matching processes that are not adaptable to varying data rates and distributions.
Innovation Solution
A method and system that utilize a family of distribution matching functions controlled by selection rules to manage data signal conversion and transmission, ensuring a flexible handling of symbol processing and desired transfer rates by selecting distribution matching functions based on current transmission rates and buffer states, allowing for efficient data conversion and streaming capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed distribution matching process is used, then the device complexity is reduced, but the adaptability to varying data rates and distributions deteriorates
Solution Approach 1:
The patent implements a dynamic distribution matching process that adapts to varying data rates and distributions by selecting from multiple pre-defined distribution matching functions based on current buffer states and transmission rates. This dynamic adaptation resolves the contradiction by making the system flexible without requiring complex real-time calculations.
Solution Approach 2:
The patent changes the parameter of distribution matching by selecting different distribution matching functions from a predefined set based on current system conditions (buffer state, transmission rate). This allows the system to adapt to varying data rates and distributions while maintaining manageable complexity through pre-computed functions.
2Use of energy by moving object
If a fixed distribution matching process is used, then the power consumption is reduced, but the reliability under high data load deteriorates
Solution Approach 1:
The patent dynamically adjusts the distribution matching process based on buffer states and transmission rates, improving reliability under high data loads by selecting appropriate functions. The dynamic adaptation is achieved through simple selection logic rather than complex real-time computation, thus avoiding significant increases in power consumption.
Solution Approach 2:
The patent incorporates feedback mechanisms that monitor buffer states and transmission rates to select appropriate distribution matching functions. This feedback-driven adaptation improves reliability under varying load conditions while maintaining low power consumption by using pre-computed functions and simple selection criteria.
3Speed
If a fixed distribution matching process is used, then the processing speed is simplified, but the streaming capability under high data loads deteriorates
Solution Approach 1:
The patent implements dynamic adaptation to varying data rates and distributions by selecting from multiple pre-defined distribution matching functions based on current buffer states and transmission rates. This dynamic approach enables efficient streaming capabilities under high data loads while maintaining manageable processing complexity through pre-computed functions.
Data Source
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AI summary
The present invention refers to a method (C) for converting a data signal (U), comprising processes of (i) providing an input symbol stream (IB) of input symbols (IBj), the input symbol stream (IB) being representative for the underlying data signal (U) to be converted and (ii) applying to consecutive partial input symbol sequences (IBk) of a number of k consecutive input symbols (IBj) covering said input bit stream (IB), with k being a - not necessarily fixed - natural number, a distribution matching process (DM) in order to generate and output a final output symbol stream (OB) of consecutive output symbols (OBj) or a preform thereof, wherein the distribution matching process (DM) is based on and/or comprises a family ((fi)i∈{0,1,...,n-1}) of a number (n) of distribution matching functions (fi), the action of the distribution matching process (DM) is achieved by acting with one of said distribution matching functions (fi) selected from said family ((fi)i∈{0,1,...,n-1}) on the input symbol stream (IB) and wherein selecting a distribution matching function (fi) from of said family ((fi)i∈{0,1,...,n-1}) is based on and in particular controlled by a set of rules comprising at least one selection rule.