Max-Log MIMO Detection for Low-Complexity LLR Computation
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Solution Overview
Problem
Existing MIMO detection algorithms face challenges in achieving a favorable performance-complexity trade-off, with many methods either being too complex or sacrificing performance, especially in MIMO orthogonal frequency-division multiplexing systems with multiple transmit antennas.
Innovation Solution
The implementation of a modified Max-Log detector that reduces complexity by using a Jacobian logarithm expansion and constraining the set of possible channel inputs, eliminating the need for QR decomposition, and applying it to various bit-to-symbol mappings, including Gray coding and dual-carrier modulation, to compute log-likelihood ratios with reduced computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of transmit and receive antennas is increased to increase system capacity and transmission reliability, then the capacity increases linearly and fading probability decreases exponentially, but the complexity of recovering transmitted information increases significantly
Solution Approach 1:
The patent changes the mathematical parameters of the detection algorithm by using Max-Log approximation instead of exact logarithm calculation, and by applying specific matrix decomposition techniques that reduce the computational parameters from exponential to polynomial complexity while maintaining detection accuracy
Solution Approach 2:
The patent replaces the computationally intensive exact detection mechanism with an approximate Max-Log mechanism that uses simplified mathematical operations, substituting the complex exponential-time computation with a more efficient polynomial-time algorithm that achieves near-optimal performance
2Measurement precision
If optimal MIMO detection algorithms are used to achieve best performance, then detection accuracy is maximized, but computational complexity increases exponentially with the number of channel inputs and alphabet size
Solution Approach 1:
The patent modifies the detection algorithm parameters by using Max-Log approximation which changes the computational complexity from exponential to polynomial while maintaining near-optimal detection accuracy through carefully selected approximation thresholds and calculation methods
Solution Approach 2:
The patent segments the detection process into distinct stages including QR decomposition, symbol-by-symbol detection, and Log-likelihood ratio calculation, allowing each segment to be optimized independently to reduce overall complexity while maintaining accuracy
3Measurement precision
If list-sphere detectors are used to compute log-likelihood ratio, then near-optimal performance can be achieved, but significant processing resources are required making implementation complex
Solution Approach 1:
The patent extracts and eliminates the most computationally intensive components from the list-sphere detector algorithm, retaining only the essential Max-Log approximation and simplified detection steps that provide near-optimal performance without requiring full list-sphere detection resources
Solution Approach 2:
The patent uses computationally inexpensive approximations and simplified calculations that can be performed quickly with minimal processing resources, sacrificing the exhaustive search of list-sphere detectors for efficient polynomial-time computation that achieves sufficient accuracy for practical applications
Data Source
AI summary
Embodiments provide novel systems and methods for multiple-input multiple-output (MIMO) Max-Log detection. These systems and methods enable near-optimal performance with low complexity for a two-input two-output channel. Some embodiments comprise using a Max-Log detector to compute a set of log-likelihood ratio (LLR) values for a channel input by minimizing cost function while computing only one instance of the cost function for each value of each bit in a symbol. Other embodiments comprise using a Max-Log detector to compute a set of log-likelihood ratio (LLR) values for a channel input by computing all instances of a cost function for each value of each bit in a symbol and selecting the minimum cost from all computed instances of the cost function for each value of each bit.