ML-APP MIMO Detection Using Segmented Symbol Candidate Processing
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Solution Overview
Problem
The existing Maximum Likelihood (ML) detector for MIMO systems becomes impractical for high information rate wireless communication systems, such as those complying with the 3GPP standard, due to its software implementation and the exponential increase in the number of candidates to be compared, leading to processing speed issues.
Innovation Solution
The ML-APP detector is designed with an Hx unit generating a special portion of all possible symbol candidates, which are then processed by LLR units performing cost function analysis and conversion operations, allowing for efficient comparison and decoding within a symbol period, thereby increasing processing speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a traditional ML detector is used to detect MIMO transmitted symbols, then detection accuracy is improved, but processing speed deteriorates due to exponential increase in candidate comparisons
Solution Approach 1:
The patent segments the set of all possible symbol candidates into multiple subsets. The Hx unit generates candidates in portions rather than all at once, and the LLR unit processes comparisons across multiple symbol periods. This segmentation allows the system to maintain detection accuracy while distributing the computational load over time, thereby improving processing speed.
Solution Approach 2:
The Hx unit performs preliminary generation of symbol candidate subsets before the LLR unit begins its comparison operations. By preparing the candidate subsets in advance and organizing them for systematic processing across multiple symbol periods, the system reduces the critical calculation period and improves overall processing throughput without sacrificing detection accuracy.
2Adaptability or versatility
If software implementation of ML detector is used, then flexibility is improved, but processing speed deteriorates for high information rate systems
Solution Approach 1:
The patent structures the detection process into distinct functional units (Hx unit for candidate generation, LLR unit for comparison) that operate across multiple symbol periods. This segmented architecture enables the system to achieve software-level flexibility while organizing computations to improve processing speed for high information rate systems.
3Measurement precision
If all possible symbol candidates are generated and compared, then detection accuracy is improved, but calculation period increases beyond symbol period
Solution Approach 1:
The patent divides the complete set of symbol candidates into multiple subsets that are processed sequentially across different symbol periods. The Hx unit generates these subsets in an organized manner, and the LLR unit performs comparisons for each subset within its designated time window. This segmentation ensures that the critical calculation period for each subset remains within the symbol period, preventing time loss while maintaining the ability to evaluate all candidates for accurate detection.
Solution Approach 2:
The system performs preliminary organization of candidate subsets and prepares the comparison framework before the actual detection process begins. This preliminary structuring enables efficient processing during the symbol period, ensuring that the calculation period does not exceed the symbol period while still allowing comprehensive candidate evaluation for accurate detection.
Data Source
AI summary
A communication device comprising an ML-APP detector coupled to at least two antennas. The ML-APP detector comprises at least one Hx unit coupled to at least one LLR unit. The Hx unit generates a portion of all possible symbols that can be received and transfers each generated symbol candidate to the LLR unit which performs a conversion operation on the transferred symbol candidate to generate another symbol candidate that is not part of the special portion. In this manner all of the possible symbol candidates are obtained by the LLR unit. The LLR unit compares the symbol candidates to a received symbol to perform a cost calculation. The symbol candidate yielding the lowest cost from the cost calculations of all possible symbol candidates is selected as the best candidate. APP decoding is then performed on the selected candidate using soft information associated with the selected candidate which soft information is generated by the LLR unit.