Shell Mapping Distribution Matcher for Power-Efficient Data Transmission
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Solution Overview
Problem
Existing data signal conversion and transmission methods face challenges in achieving increased reliability with reduced power consumption, as they often rely on uncoded transmission scenarios and lack efficient combinations of shell mapping and forward error correction (FEC) processes.
Innovation Solution
The method combines a shell mapping process with probabilistic amplitude shaping (PAS) to generate and index constellation points, allowing for efficient distribution matching and invertible processes that can be combined with FEC, such as LDPC or polar codes, to enhance power efficiency and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional uncoded transmission methods are used, then device complexity is reduced, but reliability deteriorates
Solution Approach 1:
The transmission system is segmented into distinct functional blocks: distribution matcher (with shell mapping and amplitude mapping), FEC encoder, modulator, and corresponding receiver components. This segmentation allows each component to be optimized independently while maintaining overall system reliability without excessive complexity
Solution Approach 2:
The distribution matcher acts as an intermediary component between the FEC encoder and modulator, transforming coded bit sequences into shaped amplitude sequences that optimize power efficiency and reliability without requiring complex joint optimization of all system components
2Use of energy by moving object
If conventional constellation shaping is applied, then power consumption is reduced, but adaptability to different coding schemes deteriorates
Solution Approach 1:
The distribution matcher is designed as a universal component that can work with multiple FEC coding schemes (LDPC, polar codes, convolutional codes) and different modulation formats (QAM, PSK). The shell mapping and amplitude mapping processes are coding-agnostic, allowing the same power-efficient shaping mechanism to be applied across diverse coding schemes without redesign
Solution Approach 2:
The system achieves adaptability through parameter configuration rather than structural changes. The distribution matcher can be configured with different shell mapping functions, amplitude mapping rules, and constellation parameters to optimize performance for specific coding schemes and channel conditions while maintaining the core power-efficient shaping mechanism
3Use of energy by moving object
If probabilistic amplitude shaping is used, then power efficiency is improved, but device complexity increases
Solution Approach 1:
The distribution matching process is segmented into two independent stages: shell mapping (which groups constellation points into shells based on amplitude) and amplitude mapping (which assigns amplitudes to shells). This segmentation simplifies the overall complexity by breaking down the complex PAS process into manageable, independently implementable blocks with well-defined interfaces
Solution Approach 2:
The shell mapping process performs preliminary organization of constellation points into amplitude-based shells before the actual amplitude mapping occurs. This preliminary action creates a structured framework that simplifies the subsequent probabilistic amplitude selection and reduces the computational burden during real-time signal generation
4Reliability
If shell mapping with FEC integration is implemented, then reliability is improved, but manufacturing precision requirements increase
Solution Approach 1:
The system applies different processing qualities to different parts of the signal: shell mapping operates with coarse-grained amplitude grouping, while amplitude mapping applies fine-grained probabilistic selection within each shell. This local quality differentiation allows the system to achieve high reliability through precise amplitude control where needed while using coarser processing elsewhere, reducing overall implementation precision requirements
Data Source
AI summary
Methods (C) for converting a data signal (U). The methods may comprise (i) providing an input symbol stream (IB) of input symbols (Bj), the input symbol stream (IB) being representative for the data signal (U) to be converted and (ii) applying to consecutive disjunct partial input symbol sequences (IBp) of a number of p consecutive input symbols (IBj) covering said input symbol stream (IB), a distribution matching process (DM) to generate and output a final output symbol stream (OB) or a preform thereof, wherein the distribution matching process (DM) may be formed by a preceding shell mapping process (SM) and a succeeding amplitude mapping process (AM), wherein said shell mapping process (SM) may be configured to form and output to said amplitude mapping process (AM) for each of said consecutive partial input symbol sequences (IBp) a sequence (sq) of a number of q shell indices (s), and wherein said amplitude mapping process (AM) may be configured to assign to each shell index (s) a tuple of amplitude values.