Constellation Demapping Using Closest-Point LLR Search
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Solution Overview
Problem
The complexity of high-order constellations in digital communications systems leads to increased hardware implementation costs, memory requirements, and power consumption due to the need for extensive distance metric evaluations in demapping processes.
Innovation Solution
The method reduces the number of distance metric calculations by identifying the closest or local minimum constellation points using an iterative slicing process, parallel comparisons, and a Decision Making Network, which halves the number of evaluations and supports both rotated and non-rotated constellations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive search of all constellation points is performed to calculate LLRs, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The constellation set is segmented into multiple subsets based on bit values. For each bit position, constellation points are divided into two subsets: one where the bit is 0 and another where the bit is 1. This segmentation allows the exhaustive search to be performed on smaller subsets rather than the entire constellation, reducing computational complexity while maintaining LLR calculation accuracy.
Solution Approach 2:
Instead of performing exhaustive search on all constellation points for all bits simultaneously, the method performs partial searches on segmented subsets for each bit position separately. This partial action approach reduces the total number of distance metric calculations required while still obtaining accurate LLR values for all bits.
2Productivity
If high-order constellations are used to increase data rate, then productivity is improved, but device complexity increases
Solution Approach 1:
High-order constellations are handled by segmenting the large constellation into smaller subsets based on bit positions. This allows the demapper to process high-order constellations with reduced complexity by performing exhaustive search on smaller subsets rather than treating the entire large constellation as a single unit.
Solution Approach 2:
The method changes the parameter of constellation processing by introducing bit-based segmentation. Instead of processing the constellation as a whole with fixed complexity, the approach dynamically adjusts the processing granularity based on bit positions, enabling efficient handling of high-order constellations with varying data rates.
3Measurement precision
If exhaustive distance metric evaluations are performed for all constellation points, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The exhaustive distance metric evaluation is segmented across multiple bit positions and constellation subsets. By dividing the total number of evaluations into smaller chunks processed sequentially for each bit, the method reduces peak power consumption while maintaining overall measurement precision through complete evaluation of all necessary subsets.
Solution Approach 2:
The method performs partial distance metric evaluations on segmented subsets rather than evaluating all constellation points simultaneously. This partial action approach distributes the computational load over time, reducing instantaneous power consumption while still achieving accurate LLR calculations through cumulative evaluation of all relevant subsets.
Data Source
AI summary
Methods and apparatus for efficient demapping of constellations are described. In an embodiment, these methods may be implemented within a digital communications receiver, such as a Digital Terrestrial Television receiver. The method reduces the number of distance metric calculations which are required to calculate soft information in the demapper by locating the closest constellation point to the received symbol. This closest constellation point is identified based on a comparison of distance metrics which are calculated parallel to either the I- or Q-axis. The number of distance metric calculations may be reduced still further by identifying a local minimum constellation point for each bit in the received symbol and these constellation points are identified using a similar method to the closest constellation point. Where the system uses rotated constellations, the received symbol may be unrotated before any constellation points are identified.