QPSK MIMO-OFDM ML Decoder Pipeline for Parallel LLR Metrics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
QPSK MIMO OFDM receivers face significant computational challenges in determining Log-Likelihood Ratio (LLR) values due to the exponential increase in complexity with the number of signal streams, leading to high processing power consumption and time requirements.
Innovation Solution
A pipelined decoding approach is implemented to compute LLR values by parallel processing metric portions associated with received signal streams, allowing for the computation of multiple permutations in parallel stages, thereby reducing the computational burden and increasing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive permutation computation is used to determine LLR values, then measurement precision is improved, but device complexity increases exponentially
Solution Approach 1:
The patent segments the LLR computation process into distinct pipeline stages: metric computation for first stream, metric computation for second stream, permutation generation, and metric comparison. Each stage processes partial results and passes them to the next stage, transforming the monolithic exponential computation into manageable sequential steps that reduce overall system complexity while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary computation of metrics for each stream independently before combining them through permutation generation. By pre-computing the metric portions for each stream separately and storing them, the system avoids redundant calculations during the final LLR determination, reducing the computational burden from exponential to manageable levels.
2Measurement precision
If exhaustive permutation computation is used to determine LLR values, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent divides the computation into time-sliced pipeline stages where metric computations for different streams occur in parallel within the pipeline, and permutation operations are performed in dedicated time slots. This segmentation allows the system to process multiple operations concurrently rather than sequentially, significantly reducing total computation time while maintaining the accuracy of exhaustive permutation evaluation.
Solution Approach 2:
The patent implements a continuous pipeline where metric computations, permutation generations, and comparisons occur in an unbroken sequence without idle cycles. Each stage continuously processes incoming data and passes results to the next stage, ensuring that computational resources are constantly utilized and no time is wasted on idle operations, thereby reducing overall computation time.
3Measurement precision
If exhaustive permutation computation is used to determine LLR values, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent segments the energy-intensive permutation computation into separate pipeline stages that can be executed efficiently on specialized hardware. By dividing the computation into metric calculation, permutation generation, and comparison stages, the system can optimize each stage for energy efficiency and avoid the exponential energy consumption of monolithic implementation.
Solution Approach 2:
The patent replaces general-purpose sequential computation with a dedicated parallel pipeline architecture specifically designed for LLR computation. This specialized hardware implementation substitutes inefficient software-based exhaustive search with optimized parallel processing, dramatically reducing the processing power consumption required to achieve the same measurement precision.
4Productivity
If parallel pipeline processing is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent segments the receiver into distinct functional blocks corresponding to pipeline stages: first stream metric computation unit, second stream metric computation unit, permutation generation unit, and metric comparison unit. Each block is relatively simple in design and can be independently optimized, reducing the complexity burden compared to a monolithic high-throughput processor while achieving parallel processing productivity.
Data Source
AI summary
Methods and apparatus are provided for performing log-likelihood ratio (LLR) computations in a pipeline. Portions of a metric used to compute LLR values are computed in one pipeline part. The portions correspond to all permutations of some received signal streams. The portions are combined with one permutation x2 of the received signal stream that was not included in the previous pipeline computation in a subsequent pipeline part to produce M values associated with a particular bit position. At each subsequent clock cycle, a different permutation of x2 is combined with the previously computed portions producing different M values. State values corresponding to different values of bit positions of the received stream are computed by finding the minimum among the M values, in each clock cycle, that affect a particular bit position. The state values are combined to compute the LLR values for the bit position in a final pipeline part.