Spatial-Multiplexed Signal Detection for Low-Complexity Turbo Decoding
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Solution Overview
Problem
Current mobile communication systems face challenges in achieving high-performance signal separation with low complexity in spatial and temporal signal multiplexing, as existing methods either result in error propagation or require high computational complexity, especially when increasing the number of antennas.
Innovation Solution
A spatial-multiplexed signal detection method that uses semi-rings for metric operations to estimate transmission sequences, allowing for accurate likelihood calculation and extrinsic information extraction, thereby reducing complexity and improving decoding performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Maximum Likelihood Decoding (MLD) is used for optimal detection, then detection accuracy is improved, but computational complexity increases exponentially
Solution Approach 1:
The patent segments the detection process into two stages: first performing QR decomposition to transform the received signal into a form with reduced interference, then applying sphere decoding to search for the transmitted signal within a constrained spherical region. This segmentation divides the originally exponential complexity problem into manageable polynomial complexity steps, achieving near-MLD performance with significantly reduced computational burden.
Solution Approach 2:
The patent performs QR decomposition as a preliminary action before the main detection process. By pre-processing the channel matrix H into QR form (where Q is unitary and R is upper triangular), the system transforms the received signal y into Q^H*y, which simplifies the subsequent sphere decoding search. This preliminary transformation reduces the search space and computational complexity of the detection algorithm.
2Productivity
If the number of transmitting antennas nT increases to improve system throughput, then communication capacity is improved, but complexity of signal separation increases
Solution Approach 1:
The patent segments the signal separation process into QR decomposition followed by sphere decoding. The QR decomposition step efficiently handles the increased dimensionality from multiple antennas by transforming the channel matrix into a structured form, while the sphere decoding step operates in the transformed space with reduced complexity. This segmentation allows the system to scale to higher nT without exponential complexity increase.
Solution Approach 2:
The patent replaces the traditional brute-force maximum likelihood search (mechanical exhaustive search) with an intelligent sphere decoding algorithm that uses geometric constraints. By substituting the exhaustive search mechanism with a constrained optimization approach in the transformed signal space, the system achieves efficient signal separation even as the number of antennas increases.
Data Source
AI summary
The present invention is directed to providing a spatial-multiplexed signal detection method that can improve the characteristics of spatial and temporal iterative decoding that is based on turbo principles. According to the method, when implementing factorization of conditional probability referred to as “likelihood” such that the conditional probability can be represented by the product of a plurality of conditional probabilities, the conditional probability being obtained for a received signal sequence in a spatial and temporal iterative decoding configuration based on turbo principles of soft-input soft-output detector 1 and soft-input soft-output decoder 2, the conditional probability for which factorization is possible is divided into a plurality of groups. When calculating this likelihood, the ordering among groups in which probabilities are calculated can be ordered such that groups that contain events that serve as the conditions of conditional probabilities in the groups are processed earlier. When calculating the probabilities in the groups, a metric operation method is used that uses semi-rings for estimating transmission sequences by means of the ratio of likelihoods of two exclusive events.