MIMO Detection via Segmented ML Path Search
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Solution Overview
Problem
Existing MIMO detection methods, such as Soft-output Fixed-complexity Sphere Decoding (SFSD), face challenges in achieving optimal Maximum Likelihood (ML) performance while maintaining acceptable hardware implementation complexity, especially with increased MIMO layers, leading to high computational complexity and suboptimal detection performance.
Innovation Solution
The method involves QR decomposition of the channel response matrix, followed by Maximum Likelihood path detection and ML complementary set path detection, with reserved nodes decreased layer by layer to acquire ML paths and complementary set paths, and Likelihood Ratio (LLR) information calculation for each bit of each symbol, allowing for detection performance approaching ML performance while meeting hardware complexity requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If SFSD detection is used to achieve acceptable hardware implementation complexity, then device complexity is reduced, but detection performance deteriorates and cannot reach optimal ML performance
Solution Approach 1:
The patent segments the detection process into two distinct phases: ML path detection and ML complementary set path detection. This segmentation allows the system to first obtain a baseline ML path with controlled complexity, then enhance performance by calculating complementary paths for soft output generation, thereby resolving the contradiction between hardware complexity and detection performance.
Solution Approach 2:
The patent performs preliminary ML path detection before complementary set path detection. By first establishing the ML path through QR decomposition and controlled search, the system prepares a foundation that reduces subsequent computational burden while ensuring optimal performance is achieved through the preliminary structured approach.
2Device complexity
If bit-negating method is used to reduce complexity of SFSD detection, then device complexity is reduced, but information loss increases and detection performance deteriorates
Solution Approach 1:
The patent divides the path calculation into ML path detection (structured search) and complementary set path detection (bit-negating approach). This segmentation allows information loss to be confined to the complementary set calculation phase only, while the ML path detection phase preserves complete information through systematic search, thereby reducing overall information loss while maintaining complexity reduction benefits.
Solution Approach 2:
The patent applies different quality levels to different parts of the detection process: full-precision ML path detection for critical path finding, and reduced-precision complementary set detection for soft output enhancement. This local quality differentiation optimizes the balance between information preservation and complexity reduction in different operational phases.
Data Source
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AI summary
Provided are an SFSD detection method and apparatus, and the method includes: QR decomposition is performed on a channel response matrix to acquire a Q matrix and an R matrix; the conjugate transpose of the Q matrix is multiplied by a received signal to acquire an equalized signal of the received signal; ML path detection is performed on the equalized signal, reserved nodes in respective layers are decreased layer by layer to acquire an ML path, and branches as many as iterations are reserved; ML complementary set path detection is performed on the branches, and all nodes of an acquired complementary set layer are reserved and reserved nodes in other layers are decreased layer by layer to acquire an ML complementary set path; and LLR information of each bit of each symbol of each layer is acquired according to the ML path and the ML complementary set path. For more-than-two-layer MIMO, the disclosure can acquire a detection performance approaching the ML performance and meet requirements on acceptable hardware implementation complexity.