# Implement Hardware-Efficient Complex Partial-Systolic QR Decomposition

This example shows how to implement a hardware-efficient QR decomposition using the Complex Partial-Systolic QR Decomposition block.

### Economy Size QR Decomposition

The Complex Partial-Systolic QR Decomposition block performs the first step of solving the least-squares matrix equation AX = B which transforms A in-place to R and B in-place to C = Q'B, then solves the transformed system RX = C, where QR is the orthogonal-triangular decomposition of A.

To compute the stand-alone QR decomposition, this example sets B to be the identity matrix so that the output of the Complex Partial-Systolic QR Decomposition block is the upper-triangular R and C = Q'.

### Define Matrix Dimensions

Specify the number of rows in matrices A and B, the number of columns in matrix A, and the number of columns in matrix B. This example sets B to be the identity matrix the same size as the number of rows of A.

m = 10; % Number of rows in matrices A and B n = 3; % Number of columns in matrix A p = m; % Number of columns in matrix B

### Generate Matrices A and B

Use the helper function `complexUniformRandomArray`

to generate a random matrix A such that the real and imaginary parts of the elements of A are between -1 and +1, and A is full rank. Matrix B is the identity matrix.

```
rng('default')
A = fixed.example.complexUniformRandomArray(-1,1,m,n);
B = eye(m);
```

### Select Fixed-Point Data Types

Use the helper function `qrFixedpointTypes`

to select fixed-point data types for input matrices A and B that guarantee no overflow will occur in the transformation of A in-place to R and B in-place to C = Q'B.

The real and imaginary parts of the elements of A are between -1 and 1, so the maximum possible absolute value of any element is sqrt(2).

max_abs_A = sqrt(2); % Upper bound on max(abs(A(:)) max_abs_B = 1; % Upper bound on max(abs(B(:)) precisionBits = 24; % Number of bits of precision T = fixed.qrFixedpointTypes(m,max_abs_A,max_abs_B,precisionBits); A = cast(A,'like',T.A); B = complex(cast(B,'like',T.B));

### Open the Model

```
model = 'ComplexPartialSystolicQRModel';
open_system(model);
```

The Data Handler subsystem in this model takes complex matrices A and B as inputs. It sends rows of A and B to QR block using the AMBA AXI handshake protocol. The `validIn`

signal indicates when data is available. The `ready`

signal indicates that the block can accept the data. Transfer of data occurs only when both the `validIn`

and `ready`

signals are high. You can set a delay between the feeding in rows of A and B in the Data Handler to emulate the processing time of the upstream block. `validIn`

remains high when `rowDelay`

is set to 0 because this indicates the Data Handler always has data available.

### Set Variables in the Model Workspace

Use the helper function `setModelWorkspace`

to add the variables defined above to the model workspace. These variables correspond to the block parameters for the Complex Partial-Systolic QR Decomposition block.

numSamples = 1; % Number of sample matrices rowDelay = 1; % Delay of clock cycles between feeding in rows of A and B fixed.example.setModelWorkspace(model,'A',A,'B',B,'m',m,'n',n,'p',p,... 'numSamples',numSamples,'rowDelay',rowDelay);

### Simulate the Model

out = sim(model);

### Construct the Solution from the Output Data

The Complex Partial-Systolic QR Decomposition block outputs matrices R and C at each time step. When valid result matrices are output, the block sets `validOut`

to true.

R = out.R; C = out.C;

### Extract the Economy-Size Q

The block computes C = Q'B. In this example, B is the identity matrix, so Q = C' is the economy-size orthogonal factor of the QR decomposition.

Q = C';

### Verify That Q is Orthogonal and R is Upper-Triangular

Q is orthogonal, so Q'Q is the identity matrix within roundoff.

I = Q'*Q

I = 1.0000 + 0.0000i -0.0000 - 0.0000i -0.0000 + 0.0000i -0.0000 + 0.0000i 1.0000 + 0.0000i -0.0000 - 0.0000i -0.0000 - 0.0000i -0.0000 + 0.0000i 1.0000 + 0.0000i DataTypeMode: Fixed-point: binary point scaling Signedness: Signed WordLength: 62 FractionLength: 48

R is an upper-triangular matrix.

R

R = 3.1655 + 0.0000i 0.4870 + 1.1980i 0.1466 - 0.9092i 0.0000 + 0.0000i 2.2184 + 0.0000i -0.2159 - 0.0972i 0.0000 + 0.0000i 0.0000 + 0.0000i 2.2903 + 0.0000i DataTypeMode: Fixed-point: binary point scaling Signedness: Signed WordLength: 29 FractionLength: 24

isequal(R,triu(R))

ans = logical 1

### Verify the Accuracy of the Output

To evaluate the accuracy of the Complex Partial-Systolic QR Decomposition block, compute the relative error.

relative_error = norm(double(Q*R - A))/norm(double(A))

relative_error = 1.4032e-06

Suppress mlint warnings.

```
%#ok<*NOPTS>
```