Iterative MIMO Detection with Stochastic Sampling for Lower Complexity
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Solution Overview
Problem
Existing MIMO detection systems face exponential complexity in decoding multiple streams due to the exhaustive search of the full solution space, which becomes infeasible as the constellation size and MIMO size increase, leading to unsustainable system latencies and suboptimal solutions.
Innovation Solution
A stochastic sampling method is employed to identify a subset of likely solutions near the minimum mean square error, generating both independent and dependent samples to approximate log-likelihood ratios, reducing the search space to a manageable list for iterative MAP detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive search over full solution space is used for MIMO detection, then detection accuracy is improved, but computational complexity grows exponentially
Solution Approach 1:
The patent segments the full solution space into multiple subspaces by identifying a sampling center and dividing the search space into regions. This segmentation allows the detector to focus computational efforts on promising subspaces rather than exhaustively searching the entire solution space, thereby reducing complexity while maintaining detection accuracy.
Solution Approach 2:
The patent applies local quality by concentrating search efforts in specific regions of the solution space near the sampling center where the minimum mean square error occurs. By allocating more computational resources to high-probability regions and less to low-probability regions, the system achieves accurate detection with reduced overall complexity.
2Measurement precision
If exhaustive search over full solution space is used for MIMO detection, then detection accuracy is improved, but system latency increases
Solution Approach 1:
The patent segments the full solution space into multiple subspaces by identifying a sampling center and dividing the search space into regions. This segmentation allows the detector to focus computational efforts on promising subspaces rather than exhaustively searching the entire solution space, thereby reducing complexity while maintaining detection accuracy.
Solution Approach 2:
The patent performs preliminary action by first identifying the sampling center and determining which subspaces are worth exploring before conducting the actual detection search. This preliminary analysis guides subsequent search efforts, avoiding wasted computations in low-probability regions and reducing overall system latency.
3Device complexity
If bounded search methods are used to reduce complexity, then computational complexity is reduced, but optimal solution cannot be guaranteed
Solution Approach 1:
The patent applies local quality by concentrating search efforts in specific regions of the solution space near the sampling center where the minimum mean square error occurs. By allocating more computational resources to high-probability regions and less to low-probability regions, the system achieves accurate detection with reduced overall complexity.
Solution Approach 2:
The patent employs feedback by using the identified sampling center and subspace information to guide subsequent search iterations. The detector uses results from initial searches to refine its understanding of the solution space structure, enabling it to make more informed search decisions in subsequent iterations and improve accuracy without proportionally increasing complexity.
Data Source
AI summary
A method of recovering transmitted symbols. The method receiving a signal comprising a codeword, the signal having been affected by channel effects including distortion and noise. A solution space for recovering the transmitted symbols is identified, including finding a sampling center using a full matrix W for minimum mean square error. A vector v′ is generated to generate noise. The vector has a predefined variance. The vector v′ is applied to the solution space. Original samples are gathered from the solution space, which includes noise from the vector v′ and symbol information from the received signal to find probabilities for symbols. Using the probabilities, the symbols are recovered.