Sphere Decoding MIMO Signal Estimation Real Domain Transformation
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Solution Overview
Problem
The sphere decoding method in MIMO systems faces high estimation complexity, making it difficult to implement in hardware while maintaining signal receiving performance close to maximum likelihood methods.
Innovation Solution
The method involves transferring a triangular matrix from the complex domain to the real domain, performing Q-R decomposition, and using preferred points to reduce estimation complexity by calculating partial Euclidean distances in the real domain, thereby simplifying the process and maintaining signal receiving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the maximum likelihood rule is used for signal estimation, then the receiving performance is optimal, but the system complexity becomes too high for practical hardware implementation
Solution Approach 1:
The patent transforms the channel matrix from complex domain to real domain by separating real and imaginary parts, changing the mathematical representation parameters. This transformation reduces the computational complexity of sphere decoding while maintaining the optimal receiving performance of maximum likelihood detection, as the real-domain operations require fewer complex arithmetic operations
Solution Approach 2:
The patent segments the complex-valued signal processing into separate real and imaginary component processing. By dividing the complex domain operations into independent real domain sub-operations, the computational burden is reduced and can be more efficiently implemented in hardware while preserving the overall detection performance
2Device complexity
If the sphere decoding method is used to reduce system complexity, then the hardware implementation becomes feasible, but the estimation complexity remains huge
Solution Approach 1:
The patent applies parameter transformation by converting the channel matrix and signal vectors from complex domain to real domain. This change in mathematical representation reduces the number of required computations in the sphere decoding algorithm, significantly lowering the estimation complexity while keeping the system structure implementable in hardware
Solution Approach 2:
The patent substitutes complex arithmetic operations with equivalent real-domain operations. By replacing complex multiplication and addition with real-valued computations, the patent reduces the computational load and estimation complexity, making the sphere decoding method more efficient while maintaining hardware feasibility
3Measurement precision
If the complex domain processing is used for sphere decoding, then the signal estimation accuracy is maintained, but the computational complexity increases significantly
Solution Approach 1:
The patent changes the domain parameter from complex to real by transforming the channel matrix H into a real-valued matrix through QR decomposition in the complex domain followed by real-domain processing. This parameter transformation maintains the geometric relationships necessary for accurate signal estimation while reducing computational complexity
Data Source
AI summary
A sphere decoding method applied to a MIMO channel is provided. T signals transmitted via the MIMO channel are received. A first triangular matrix corresponding to a channel matrix is generated and mapped from the complex domain into the real domain to obtain a second triangular matrix. A first zero-forcing soft-output solution corresponding to a first estimation layer is found, and multiple preferred points P(1) are obtained. Multiple n-th zero-forcing soft-output solutions corresponding to an n-th estimation layer are obtained according to multiple preferred points P(n−1), and multiple preferred points P(n) are obtained according to PEDs of multiple n-th constellation points. Multiple 2T-th zero-forcing soft-output solutions are obtained according to the preferred points P(2T−1) and multiple preferred points P(2T) are obtained correspondingly. The preferred point P(2T) corresponding to the least PED is mapped from the real domain into the complex domain to generate an optimal solution of the T signals.