Multi-Stage Symbol Block Detection for Lower Search Complexity
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Solution Overview
Problem
Current symbol block detection methods in DS-CDMA and LTE systems face high computational complexity due to intersymbol interference (ISI) and large number of possible symbol combinations, especially at higher data rates, making existing approaches like MLSE and AMLD with SSA impractical for large symbol blocks.
Innovation Solution
A demodulator is configured with multiple stages of detection assistance to progressively reduce the number of candidate symbol combinations by assisting detectors and a final assisting detector, limiting the detector's processing to a reduced set of combinations, thereby reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If maximum likelihood sequence estimation (MLSE) is used to detect all possible symbol combinations, then detection accuracy is improved, but computational complexity becomes prohibitively large
Solution Approach 1:
The patent segments the detection process into multiple stages: first detecting individual symbols within each symbol block to identify candidate symbol values, then detecting symbol blocks to identify candidate symbol combinations, and finally detecting sequences of symbol blocks. This segmentation reduces the computational complexity from considering all MN possible combinations to considering only the most likely candidates at each stage, while maintaining detection accuracy.
Solution Approach 2:
The patent performs preliminary detection of individual symbols within symbol blocks before detecting the symbol blocks themselves. By first identifying the most likely candidate symbol values for each symbol position, the system prepares a reduced set of candidates that significantly decreases the computational burden of subsequent symbol block detection, while ensuring that the most likely combinations are considered.
2Productivity
If the number of symbols in each symbol block is increased to achieve higher data rates, then productivity is improved, but the number of possible symbol combinations increases exponentially
Solution Approach 1:
The patent divides the detection into hierarchical stages: individual symbol detection within blocks, symbol block detection, and sequence detection. At each stage, only the most likely candidate values or combinations are considered, not all possible combinations. This allows the system to handle symbol blocks with many symbols (e.g., 15 symbols in HSPA downlink) without experiencing exponential complexity growth.
Solution Approach 2:
The patent applies partial action by detecting only the most likely candidate symbols and symbol combinations rather than all possible combinations. By identifying K most likely candidate symbol values for each symbol position and considering only combinations formed from these candidates, the system performs sufficient detection to achieve high data rates while avoiding the excessive computational burden of exhaustive search.
3Device complexity
If single-stage detection assistance is used to reduce complexity, then computational complexity is reduced, but it remains overly complicated for higher data rates
Solution Approach 1:
The patent extends single-stage detection assistance to multi-stage detection assistance by segmenting the detection process into multiple hierarchical stages. Each stage performs detection assistance for a specific level (individual symbols, symbol blocks, sequences), progressively reducing the search space. This multi-stage approach maintains low computational complexity while enabling the system to handle higher data rates with more symbols per block.
Solution Approach 2:
The patent adds the dimension of hierarchical staging to the detection process. Instead of a single flat stage considering all combinations, the system introduces multiple layers of detection (symbol-level, block-level, sequence-level), each reducing the problem dimensionality. This dimensional transformation allows the system to handle higher data rates without proportional increases in complexity.
Data Source
AI summary
Teachings presented herein offer reduced computational complexity for detecting a plurality of symbol blocks, even for symbol blocks that comprise the combination of a relatively large number of symbols. The teachings perform two or more stages of detection assistance to successively reduce the number of candidate combinations of symbols to be considered for a symbol block when detecting the plurality of symbol blocks. In particular, the teachings identify a reduced set of candidate symbol combinations for at least one symbol block in the plurality of symbol blocks, and then jointly detect each of one or more distinct groups of symbols in the symbol block to determine from that reduced set a final reduced set of candidate symbol combinations. Detection of the plurality of symbol blocks limits the candidate combinations of symbols considered for a symbol block to the final reduced set of candidate symbol combinations identified for that symbol block.