Systolic Array MIMO Decoder Complex Matrix Processing
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Solution Overview
Problem
Current systolic arrays face complexity and overhead in processing complex matrices due to the need for complex-to-real data reduction and phase handling, which is not efficiently addressed in existing technologies for MIMO decoding applications.
Innovation Solution
A method for decoding that involves generating an extended channel matrix, triangularizing it, inverting the triangularized matrix, and using the results to perform weighted estimation for MIMO decoding, which is implemented using a systolic array capable of handling complex numbers without the need for complex-to-real data reduction, leveraging modified Givens rotations and dual-mode operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If complex-to-real data reduction is performed to process complex matrices in systolic arrays, then the processing can be done using existing real-number systolic array architectures, but additional complexity and overhead are introduced
Solution Approach 1:
The patent changes the fundamental parameter of the systolic array from processing only real numbers to processing complex numbers directly. This is achieved by modifying the data type handling in the systolic array cells to accommodate complex arithmetic operations (addition, multiplication, conjugation) while maintaining the same architectural framework, thereby eliminating the need for complex-to-real data reduction and associated overhead
Solution Approach 2:
Instead of converting complex data to real data before processing (the conventional approach), the patent inverts the approach by enabling the systolic array to process complex data natively. This inversion eliminates the conversion step and its associated complexity, allowing direct complex matrix operations including QR decomposition and phase tracking
2Adaptability or versatility
If phase factoring is applied to handle complex numbers in systolic arrays, then complex matrix processing becomes possible, but overhead is added to the processing pipeline
Solution Approach 1:
The patent enables continuous complex number processing through the systolic array pipeline without interruption for phase extraction and separate handling. Complex arithmetic operations are performed continuously as data flows through the array, eliminating the discontinuous phase factoring steps that would otherwise break the pipeline and add overhead
Solution Approach 2:
The patent merges the handling of magnitude and phase information into a single complex number processing stream. Instead of separating these operations through phase factoring, the systolic array cells perform combined complex arithmetic operations that process both magnitude and phase simultaneously, reducing the number of processing stages and overhead
3Ease of manufacture
If conventional Givens rotation matrices are used for complex matrices, then standard algorithms can be applied, but the processing becomes less efficient due to complex-to-real reduction requirements
Solution Approach 1:
The patent modifies the Givens rotation matrix operations to work directly with complex numbers by changing the arithmetic parameter from real to complex. The rotation matrices are constructed using complex cosine and sine values, and the rotation operations perform complex arithmetic directly, eliminating the need for complex-to-real reduction while maintaining algorithmic simplicity
Solution Approach 2:
The patent creates a universal systolic array cell design that can handle both real and complex number operations. The same hardware structure and algorithmic framework that process real numbers is extended to handle complex numbers, allowing the system to process complex matrices efficiently without requiring separate specialized processing paths
Data Source
AI summary
A decoder, such as for example an MMSE MIMO decoder, and a method for decoding are described. An input channel matrix is obtained, and an extended channel matrix of the input channel matrix is generated. The extended channel matrix is triangularized to provide a triangularized matrix, and the triangularized matrix is inverted to provide an inverted triangular matrix. A left matrix multiplication result matrix associated with multiplication of the input channel matrix and the inverted triangular matrix is generated, and a weight matrix from the left matrix multiplication result matrix and the inverted triangular matrix is generated. A received symbols matrix is obtained, and a weighted estimation is generated and output using the weight matrix and the received symbols matrix to provide an estimate of a transmit symbols matrix for output of estimated data symbols.