Extensible QR decomposition method based on pipeline working mode

Abstract

The invention provides an extensible QR decomposition method based on a pipeline working mode. A layered and cascaded structure is adopted, wherein pipeline operation is realized among layers; two lines of matrix data of each layer are subject to Givens rotation; the Givens rotation is conducted by using a coordinate rotation digital computer (CORDIC) algorithm and realized by using operations of addition and displacement; the layer number is determined by the line number or the row number of matrices; a whole QR decomposition module consists of a controller and a data processing module; a line rotation module is arranged on each layer of the data processing module; output ports of upper layers of line rotation modules are connected with input ports of lower layers of line rotation modules; the matrix data to be composed are input from an input port of the first layer of line rotation module, and sequentially flow through all the line rotation modules; and all the line rotation modules work in parallel. The scalable QR decomposition method has the advantages that hardware resources are saved; and as the iterative process of the CORDIC algorithm is split at the inner part of each layer of the data processing module, a pipeline structure is formed, the rotation operation of multiple vector groups is processed in a time sharing manner, the throughput rate is increased, and the extension is flexible.

Description

technical field [0001] The invention belongs to the technical field of mobile wireless data transmission, and relates to an expandable QR decomposition method, structure and application based on a pipeline working mode. Background technique [0002] QR decomposition is a basic form of matrix decomposition, which is widely used in various signal processing fields. For example, in MIMO detection, after performing QR decomposition on the channel matrix, not only the orthogonality of data can be maintained, but also the data detection process can be simplified. After performing QR decomposition on the channel matrix, it can be transformed into an orthogonal matrix Q and an upper triangular matrix R. In this way, mutual interference of received data will be simplified. Because QR decomposition can greatly simplify the complexity of the algorithm, the scale of the entire algorithm will not increase due to the introduction of QR decomposition. [0003] At present, there are thre...

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[0009] This is actually a huge computational waste

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