QR Decomposition Processing Unit for Wireless Receiver Signal
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
MIMO receivers face challenges in achieving high-throughput and low-complexity signal processing due to the high computational complexity of QR decomposition for large-dimensional channel matrices, particularly in mobile communication systems with fast-varying channels, where low processing latency is required.
Innovation Solution
A QR decomposition processing unit that concurrently performs multi-dimensional Givens Rotations, Householder Reflections, and 2D Givens Rotations to generate a QR decomposition of the input matrix, utilizing a combination of multi-dimensional CORDIC algorithms and a modified Real Value Decomposition model to reduce the number of rotation operations and enable parallel implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional QR decomposition algorithms are used for large-dimensional channel matrices, then processing accuracy is maintained, but computational complexity and processing latency increase significantly
Solution Approach 1:
The patent segments the QR decomposition process into distinct functional units: Givens rotation units for individual element elimination, Householder reflection units for multi-element elimination, and a control unit that coordinates their operation. This segmentation allows each unit to be optimized independently and enables parallel execution of multiple operations simultaneously, reducing overall computational complexity while maintaining processing accuracy for large-dimensional channel matrices
Solution Approach 2:
The patent transitions from sequential 2D Givens rotations to multi-dimensional Householder reflections that can eliminate multiple elements simultaneously. By operating in higher dimensions, the Householder reflection units can process multiple channel matrix elements in parallel, significantly reducing the number of rotation operations required and thereby lowering computational complexity while preserving decomposition accuracy
2Measurement precision
If more rotation operations are performed to ensure accurate QR decomposition, then processing precision is improved, but processing latency increases
Solution Approach 1:
The patent merges multiple 2D Givens rotation operations into single multi-dimensional Householder reflection operations. By combining the functionality of multiple sequential rotations into one parallel operation, the patent reduces the total number of rotation steps required for accurate QR decomposition, thereby decreasing processing latency while maintaining the precision needed for reliable signal detection in MIMO systems
Solution Approach 2:
The control unit performs preliminary planning of the rotation operation sequence, identifying which Householder reflections can be executed in parallel. By pre-organizing the decomposition steps and preparing multiple rotation operations in advance, the system can immediately execute them in parallel when resources are available, reducing overall processing latency while ensuring all necessary rotations are performed for accurate decomposition
3Productivity
If high-throughput QR decomposition is achieved through parallel processing, then productivity is improved, but hardware requirements and power consumption increase
Solution Approach 1:
The patent designs multi-dimensional Householder reflection units that can handle various matrix dimensions and configurations through a single unified architecture. These universal units can be configured to process different channel matrix sizes by adjusting control parameters rather than requiring separate dedicated hardware for each dimension, thereby achieving high throughput through parallel processing while minimizing the overall hardware footprint and power consumption
Data Source
AI summary
A QRD processor for computing input signals in a receiver for wireless communication relies upon a combination of multi-dimensional Givens Rotations, Householder Reflections and conventional two-dimensional (2D) Givens Rotations, for computing the QRD of matrices. The proposed technique integrates the benefits of multi-dimensional annihilation capability of Householder reflections plus the low-complexity nature of the conventional 2D Givens rotations. Such integration increases throughput and reduces the hardware complexity, by first decreasing the number of rotation operations required and then by enabling their parallel execution. A pipelined architecture is presented (290) that uses un-rolled pipelined CORDIC processors (245a to 245d) iteratively to improve throughput and resource utilization, while reducing the gate count.