Methods of converting or reconverting a data signal and method and system for data transmission and/or data reception
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data signal conversion and transmission methods face challenges in achieving high reliability while minimizing power consumption, as they often result in inefficient power usage due to the inherent power consumption associated with processing and transmitting symbol sequences.
Innovation Solution
The method involves decomposing an input symbol stream into partial streams, applying distribution matching processes with lower cardinality alphabets, and then using symbol mapping processes to generate a final output signal, with the option for invertible processes and variable symbol lengths to enhance efficiency. This approach ensures that the empirical distribution of the output signal aligns with a predefined distribution, such as Gaussian, to optimize power efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If conventional distribution matching processes with high cardinality alphabets are used, then the data transmission can achieve higher data rates, but the power consumption increases significantly
Solution Approach 1:
The input symbol stream is demultiplexed into multiple decomposed partial symbol streams, which are then processed by separate distribution matching processes with lower cardinality alphabets. This segmentation allows the system to achieve the required data transmission rate through parallel processing while each individual process consumes less power due to operating with smaller alphabet sizes.
Solution Approach 2:
The invention transitions from a single high-cardinality alphabet process to multiple low-cardinality alphabet processes operating in parallel. By changing the dimensional structure of the processing architecture and using a symbol mapping process to combine the outputs, the system achieves equivalent or superior data transmission rates while reducing the power consumption burden on any single processing element.
2Use of energy by moving object
If the cardinality of the alphabet in distribution matching processes is reduced, then the power consumption decreases, but the processing complexity and device structure become more complex
Solution Approach 1:
The complex task of distribution matching with high cardinality alphabets is segmented into multiple simpler tasks with lower cardinality alphabets. Each distribution matching process handles a subset of the input stream with reduced complexity, and the overall system complexity is managed through the structured demultiplexing and multiplexing framework.
Solution Approach 2:
The system uses a universal symbol mapping process that can handle outputs from multiple distribution matching processes with different alphabet cardinalities. This multi-functional component consolidates the complexity management, allowing each individual distribution matching process to remain simple while the overall system achieves the required performance.
3Loss of energy
If multiple decomposed partial symbol streams are processed separately, then the power efficiency improves, but the device complexity and number of components increase
Solution Approach 1:
The input symbol stream is divided into multiple decomposed partial symbol streams that are processed in parallel by separate distribution matching processes. This segmentation enables power-efficient processing by distributing the computational load across multiple low-power components rather than concentrating it in a single high-power component.
Solution Approach 2:
The outputs from multiple distribution matching processes are combined through a symbol mapping process to generate the final output symbol sequence. This merging operation consolidates the results from multiple parallel processes into a unified output, managing the complexity of having multiple components while maintaining the power efficiency benefits of parallel processing.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The present invention refers to a method (C) for converting a data signal (U). The method comprises processes of (i) providing an input symbol stream (B) being representative for the data signal (U) to be converted, (ii) demultiplexing (DMX) the input symbol stream (B) in order to consecutively decompose the input symbol stream (B) into a number m of decomposed partial symbol streams (B_1, ..., B_m) with m being a natural number, (iii) applying on each of the decomposed partial symbol streams (B_1, ..., B_m) an assigned distribution matching process (DM_1, ..., DM_m) thereby generating and outputting for each decomposed partial symbol stream (B_1, ..., B_m) a respective pre-sequence (bn_1, ..., bn_m) of n_j symbols as an intermediate output symbol sequence, with n_j being a natural number for all j = 1 to m, and (iv) supplying the pre-sequences (bn_1, ..., bn_m) output by the distribution matching processes (DM_1, ..., DM_m) to at least one symbol mapping process (BM) in order to generate and output a signal representative for a final output symbol sequence (S) as a converted data signal, wherein (v) each of the distribution matching processes (DM_1, ..., DM_m) and the symbol mapping process (BM) are based on a respective assigned alphabet (ADM_1, ..., ADM_m; ABM) of symbols and (vi) the cardinality of each of the alphabets (ADM_1, ..., ADM_m) of the distribution matching processes (DM_1, ..., DM_m) is lower than the cardinality of the alphabet (ABM) of the symbol mapping process (BM).