# Implement Hardware-Efficient Complex Burst Q-less QR with Forgetting Factor

This example shows how to use the hardware-efficient Complex Burst Q-less QR Decomposition with Forgetting Factor Whole R Output block.

### Q-less QR Decomposition with Forgetting Factor

The Complex Burst Q-less QR Decomposition with Forgetting Factor Whole R Output block implements the following recursion to compute the upper-triangular factor R of continuously streaming n-by-1 row vectors A(k,:) using forgetting factor . It is as if matrix A is infinitely tall. The forgetting factor in the range keeps it from integrating without bound.

### Define System Parameters

`n`

is the length of the row vectors A(k,:) and the number of rows and columns in R.

n = 5;

`m`

is the effective numbers of rows of A to integrate over.

m = 100;

Use the `fixed.forgettingFactor`

function to compute the forgetting factor as a function of the number of rows that you are integrating over.

forgettingFactor = fixed.forgettingFactor(m)

forgettingFactor = 0.9950

`precisionBits`

defines the number of bits of precision required for the QR Decomposition. Set this value according to system requirements.

precisionBits = 24;

In this example, complex-valued matrix A is constructed such that the magnitude of the real and imaginary parts of its elements is less than or equal to one, so the maximum possible absolute value of any element is . Your own system requirements will define what those values are. If you don't know what they are, and A is a fixed-point input to the system, then you can use the `upperbound`

function to determine the upper bounds of the fixed-point types of A.

`max_abs_A`

is an upper bound on the maximum magnitude element of A.

max_abs_A = sqrt(2);

### Select Fixed-Point Types

Use the `fixed.qlessqrFixedpointTypes`

function to compute fixed-point types.

T = fixed.qlessqrFixedpointTypes(m,max_abs_A,precisionBits)

T = struct with fields: A: [0x0 embedded.fi]

`T.A`

is the fixed-point type computed for transforming A to R in-place so that it does not overflow.

T.A

ans = [] DataTypeMode: Fixed-point: binary point scaling Signedness: Signed WordLength: 31 FractionLength: 24

### AMBA AXI Handshaking Process

The Data Handler subsystem in this model takes complex matrix A as inputs. It sends rows of A to QR Decomposition 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 delay for the feeding in rows of A in the Data Handler to emulate the processing time of the upstream block. `validOut`

signal of Data Handler remain high when `delayLen`

is set to `0`

becuase this indicates the Data Handler always has data available.

### Define Simulation Parameters

Create random matrix A to contain a specified number of inputs.

`numInputs`

is the number of input rows A(k,:) for this example.

```
numInputs = 500;
rng('default')
A = fixed.example.complexUniformRandomArray(-1,1,numInputs,n);
```

Cast the inputs to the types determined by `fixed.qlessqrFixedpointTypes`

.

```
A = cast(A,'like',T.A);
```

Cast the forgetting factor to a fixed-point type with the same word length as A and best-precision scaling.

forgettingFactor = fi(forgettingFactor,1,T.A.WordLength);

Set delay for feeding in rows of A

delayLen = 1;

Select a stop time for the simulation that is long enough to process all the inputs from A.

stopTime = 4*numInputs*T.A.WordLength;

### Open the Model

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

### Set Variables in the Model Workspace

Use the helper function `setModelWorkspace`

to add the variables defined above to the model workspace.

fixed.example.setModelWorkspace(model,'A',A,'n',n,... 'forgettingFactor',forgettingFactor,... 'regularizationParameter',0,... 'delayLen',delayLen,... 'stopTime',stopTime);

### Simulate the Model

out = sim(model);

### Verify the Accuracy of the Output

Define matrix as follows

Then using the formula for the computation of the th output , and the fact that , you can show that

So to verify the output, the difference between and should be small.

Choose the last output of the simulation.

R = double(out.R(:,:,end))

R = Columns 1 through 4 7.4030 + 0.0000i 0.2517 - 0.3472i 0.4163 - 0.1448i 0.4088 + 0.5546i 0.0000 + 0.0000i 7.3291 + 0.0000i -0.1239 - 0.3553i -0.8237 + 0.2091i 0.0000 + 0.0000i 0.0000 + 0.0000i 7.3507 + 0.0000i 0.2622 - 0.6994i 0.0000 + 0.0000i 0.0000 + 0.0000i 0.0000 + 0.0000i 7.2422 + 0.0000i 0.0000 + 0.0000i 0.0000 + 0.0000i 0.0000 + 0.0000i 0.0000 + 0.0000i Column 5 0.5450 + 0.7208i 0.1945 + 0.1716i -0.5293 + 0.4192i 0.4574 - 0.1519i 7.0558 + 0.0000i

Verify that R is upper triangular.

isequal(R,triu(R))

ans = logical 1

Verify that the diagonal is greater than or equal to zero.

diag(R)

ans = 7.4030 7.3291 7.3507 7.2422 7.0558

Synchronize the last output R with the input by finding the number of inputs that produced it.

A = double(A); alpha = double(forgettingFactor); relative_errors = nan(1,n); for k = 1:numInputs A_k = alpha.^(k:-1:1)' .* A(1:k,:); relative_errors(k) = norm(A_k'*A_k - R'*R)/norm(A_k'*A_k); end

`k`

is the number of inputs A(k,:) that produced the last R.

```
k = find(relative_errors==min(relative_errors),1,'last')
```

k = 166

Verify that

with a small relative error.

A_k = alpha.^(k:-1:1)' .* A(1:k,:); relative_error = norm(A_k'*A_k - R'*R)/norm(A_k'*A_k)

relative_error = 5.2882e-06

Suppress mlint warnings in this file.

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