Low-complexity LLR Computation for Nonuniform QAM
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Solution Overview
Problem
Current digital communication systems using regular QAM constellations fall short of achieving Shannon capacity over AWGN channels, and existing demodulation methods are complex and inefficient for non-uniform constellations.
Innovation Solution
A device and method for demodulating modulated signals using a processor to determine a log likelihood ratio by identifying closest complementary constellation points and hard decision points in a constellation diagram, allowing for efficient demodulation with complexity growing logarithmically with constellation size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If regular QAM constellations are used for demodulation, then implementation is simple and easy, but Shannon capacity over AWGN channels is not achieved
Solution Approach 1:
The patent applies local quality by using different amplitude levels for different constellation points in non-uniform QAM. Specifically, the constellation points are assigned amplitudes according to a probability distribution (e.g., Gaussian distribution), where inner points have smaller amplitudes and outer points have larger amplitudes. This local differentiation in amplitude quality optimizes the constellation to achieve Shannon capacity while maintaining manageable implementation complexity through structured amplitude assignment.
2Reliability
If non-uniform QAM constellations are used to achieve Shannon capacity, then capacity performance improves, but demodulation complexity increases
Solution Approach 1:
The patent segments the demodulation process into distinct stages: receiving the modulated signal, determining the LLR value through a systematic algorithm involving comparison and calculation steps, and using the LLR for demodulation. This segmentation of the complex demodulation task into manageable operational steps reduces overall demodulation complexity while maintaining the capacity performance benefits of non-uniform QAM constellations.
Solution Approach 2:
The patent changes the parameter of constellation point amplitudes from uniform to non-uniform distribution, specifically using amplitudes that follow a probability distribution such as Gaussian. This parameter change in the constellation structure enables achieving Shannon capacity. The associated complexity is managed by using efficient LLR calculation algorithms that adapt to these amplitude parameters without requiring exhaustive search methods.
3Measurement precision
If exhaustive search methods are used to determine closest constellation points, then accuracy is high, but computational complexity grows linearly with constellation size
Solution Approach 1:
The patent applies partial action by using the Max2-log MAP algorithm which considers only the two closest constellation points (the hard decision point and the closest complementary constellation point) rather than exhaustively evaluating all constellation points. This partial evaluation approach maintains high accuracy for LLR determination because the log-likelihood ratio is dominated by the closest points, while significantly reducing computational complexity from linear to logarithmic growth with constellation size.
Data Source
AI summary
A device for use in demodulating modulated signals by determining a value for a log likelihood ratio. The device has a storage device to store executable instructions and a processor to execute the instructions stored on the memory device. The processor is configured to, when executing the instructions: receive a modulated signal which is to be demodulated using a constellation diagram comprising a plurality of constellation points which are identified by binary reflected Gray-labelled codes; identify, for a bit of the Gray-labelled codes, a closest complementary constellation point to the signal when considering the signal as a point on a representation of one-dimension of the constellation diagram; identify a hard decision point, wherein the hard decision point is the closest constellation point to the signal when considering the signal as a point on a representation of one-dimension of a constellation diagram; and a complementary constellation point is a constellation point which has a different value for the bit compared to the hard decision point; and determine a value for a log likelihood ratio using the hard decision point and the closest complementary constellation point. Some devices identify a closest complementary constellation point to the signal, a second closest complementary constellation point to the signal, a hard decision point and an auxiliary hard decision point and determine a value for a log likelihood ratio using the hard decision point, an auxiliary hard decision point, the closest complementary constellation point and the second closest complementary constellation point.