LMS Filter

Minimize error between observed and desired signals

Since R2023a

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Libraries:
DSP HDL Toolbox / Filtering

Description

The LMS Filter block estimates the filter weights and minimizes the error between a desired signal and an observed signal using the mean square error (MSE) criteria. The block adapts its weights until the error between the data and the desired data is minimal. The block accepts scalar and vector inputs of type real and complex.

You can use this block for various applications, such as noise cancellation, system identification, and inverse system modeling. The block provides an architecture suitable for HDL code generation and hardware deployment.

Examples

Calculate Mean Square Error Performance Using LMS Filter

How use the LMS Filter block to calculate the mean square error performance in additive white Gaussian noise (AWGN). The block supports scalar and vector inputs of type real or complex. You can generate the HDL code from the LMSFilter subsystem in this Simulink® model.

Open Script

Ports

Input

data — Input data

scalar or column vector of real or complex values

Input data, specified as a scalar or column vector of real or complex values. The input vector size must be in the range [2, 64] and it must be less than the filter length.

Use double and single data types for simulation, but not for HDL code generation. For HDL code generation, the input date type must be signed fixed point.

Data Types: single | double | fixed point
Complex Number Support: Yes

desired — Desired input data

scalar or column vector of real or complex values

Desired input data, specified as a scalar or column vector of real or complex values. The input vector size must be in the range [2, 64] and it must be less than filter length. The desired port must have the same data type, complexity, and dimensions as the input data port.

Data Types: single | double | fixed point
Complex Number Support: Yes

valid — Indicates valid input data

scalar

Control signal that indicates if the input data is valid. When valid is 1 (true), the block captures the values from the input ports data and desired. When valid is 0 (false), the block ignores the values from the input ports data and desired.

Data Types: Boolean

stepSize — Convergence and stability of filter

real scalar

Specify the step size to control convergence and stability of the filter. The recommended step size must be less than 1/(2 x Input power x Filter length).

You can use double and single data types for simulation, but not for HDL code generation. For HDL code generation, the input date type must be unsigned fixed point.

Dependencies

To enable this port, on the Main tab, set the Step-size source parameter to Input port.

Data Types: double | single | fixed point

adapt — Update filter weights

scalar

When adapt is 1 (true) and input valid is 1 (true), the block adapts to the new filter weights for every input valid. When the adapt is 0 (false) and the input valid is 1 (true) or 0 (false), the block remains at its previous filter weights.

Dependencies

To enable this port, on the Main tab, select the Enable adapt input port parameter.

Data Types: Boolean

reset — Clears internal states

scalar

Control signal that clears internal states. When reset is 1 (true), the block stops the current calculation, clears internal states, and sets to the initial values of filter weights specified on the block mask. When the reset is 0 (false) and the input valid is 1 (true), the block captures data for processing.

For more reset considerations, see the Reset Signal section on the Hardware Control Signals page.

Dependencies

To enable this port, on the Control Ports tab, select the Enable reset input port parameter.

Data Types: Boolean

Output

data — Filtered output data

scalar or column vector of real or complex values

Filtered output data, returned as a scalar or column vector of real or complex values. The dimensions of the output match the dimensions of the input.

When the input data is a floating-point type, the output data inherits the data type of the input data. When the input data is a fixed-point type, the output data is a fixed point with word length and fractional length incremented by 1.

Data Types: single | double | fixed point
Complex Number Support: Yes

valid — Indicates valid output data

scalar

Control signal that indicates if the data from the output data port is valid. When valid is 1 (true), the block returns valid data from the output data port. When valid is 0 (false), the values from the output data port are not valid.

Data Types: Boolean

error — Error between output and desired signals

real or complex scalar or column vector

Outputs the result of subtracting the filtered output signal from the desired signal when the output valid is 1 (true).

If the input data and desired is a scalar or a column vector, the error signal has the same dimension and complexity as the input data. When the input data is a floating-point type, the output data inherits the data type of the input data. When the input data is a fixed-point type, the output data is fixed point with word length incremented by 2 and fractional length incremented by 1.

Dependencies

To enable this port, on the Main tab, select the Enable filter error output port parameter.

Data Types: Boolean

weights — Updated filter weights

row vector

For each iteration, the block outputs the current updated filter weights from this port when the output valid is 1 (true).

For floating-point inputs, the weights data type must match the input type. For fixed-point signals, the input must match the data type set to the Weights parameter.

Dependencies

To enable this port, on the Main tab, select the Enable filter weights output port parameter.

Data Types: Boolean

Parameters

Main

Filter length — Filter length

32 (default) | positive integer less than or equal to 2048

Specify the length of the LMS filter as a positive integer less than or equal to 2048.

Step-size source — Step size source

Property (default) | Input port

Select whether the block specifies the step size through property or input port.

• Property — Specify step size by using the Step size (mu) parameter.

• Input port — Specify step size by using the stepSize port.

Step size (mu) — Step size

0.01 (default) | positive scalar numeric value

Specify the step size as a positive scalar numeric value. The recommended step size must be less than 1/(2 x Input power x Filter length).

Dependencies

To enable this parameter, set the Step-size source parameter to Property.

Initial value of filter weights — Initial value of filter weights

0 (default) | scalar | row vector

Enter the initial filter weights as a scalar or row vector of size equal to filter length.

When you specify a scalar value, the block considers it as a row vector equivalent to the specified scalar value.

Enable adapt input port — Option to enable adapt input port

off (default) | on

Select this parameter to enable the adapt input port.

Enable filter weights output port — Option to enable weights output port

on (default) | off

Select this parameter to enable the weights output port.

For each iteration, the block outputs the current updated filter weights from this port.

Enable filter error output port — Option to enable error output port

off (default) | on

Select this parameter to enable the error output port.

Data Types

Rounding mode — Rounding mode for type-casting the output

Floor (default) | Ceiling | Convergent | Nearest | Round | Zero

Rounding mode for type-casting the output to the data type specified by the Output parameter. When the input data type is floating point, the block ignores this parameter. For more details, see Rounding Modes.

Saturate on integer overflow — Overflow handling for type-casting the output

off (default) | on

Overflow handling for type-casting the output to the data type specified by the Output parameter. When the input data type is floating point, the block ignores this parameter. For more details, see Overflow Handling.

Step size — Step size type

Inherit: Same word length as input (default) | <data type expression>

Specify the step size data type.

Dependencies

To enable this parameter, set the Step-size source parameter to Property.

Weights — Filter weights type

fixdt(1,16,15) (default) | Inherit: Same as input | <data type expression>

Specify the data type for the filter weights.

Step-size error product — Step size error product type

Inherit: Same word length as input (default) | Inherit: Inherit via internal rule | <data type expression>

Specify the step size error product.

Control Ports

Enable reset input port — Option to enable reset input port

off (default) | on

Select this check box to enable the reset input port. The reset signal implements a local synchronous reset of the data path registers.

For more reset considerations, see the Reset Signal section on the Hardware Control Signals page.

Use HDL global reset — Option to connect data path registers to generated HDL global reset signal

off (default) | on

Select this check box to connect the generated HDL global reset signal to the data path registers. This parameter does not change the appearance of the block or modify simulation behavior in Simulink^®. When you clear this check box, the generated HDL global reset clears only the control path registers. The generated HDL global reset can be synchronous or asynchronous depending on the HDL Code Generation > Global Settings > Reset type parameter in the model Configuration Parameters.

For more reset considerations, see the Reset Signal section on the Hardware Control Signals page.

Algorithms

The following figure shows the high-level architecture block diagram of the LMS Filter block for a scalar input. An observed signal is provided as input to the Input Memory block and also to the Transpose Filter block, which is configured with a Discrete FIR Filter block. The output of the Transpose Filter block is subtracted from the Desired input signal to output the Filter Error signal. The Filter Error signal is multiplied with the Step-Size input signal and the resulted step-error product is converted to the required data type. The step-error product is multiplied with the corresponding tap delay line conjugate from the Input Memory block. In the Integrator block, the multiplied output is added to the previous weight, Unit Delay, to output new filter weights. These filter weights are routed back to the Transpose Filter block for the next weight updates.

The following figure shows the high-level architecture block diagram of the LMS Filter block for vector input. The vector architecture is almost similar to the scalar architecture. In the vector architecture, step-error product vectors are multiplied with the corresponding tap delay line conjugates from the Input Memory block and then summed up in the Summation block. In the Integrator block, the summed output is added to the previous weight vector Unit Delay to output new filter weights. These filter weights are routed back to the Transpose Filter block for the next weight updates.

For hardware implementation, all these operations are pipelined, which takes around D number of delays to update the filter weights, where D is the latency of the block.

Latency

The latency of the block varies with the input size and complexity.

This figure shows the output of the block for a real scalar input when you set the Filter length parameter to 64 and all other parameters to their default values. The latency of the block is 13 clock cycles.

This figure shows the output of the block for a 4-by-1 real vector input when you set the Filter length parameter to 64 and all other parameters to their default values. The latency of the block is 18 clock cycles.

Performance

The performance of the synthesized HDL code varies with your target and synthesis options. These resource and performance data are the place-and-route results from the generated HDL targeted to a Xilinx^® Zynq^®Ultrascale+™ MPSoC evaluation board. The two examples in the following tables use this common configuration.

• Filter length — 64

• 16-bit real input data

• Step size (mu) — 0.006

• Initial value of filter weights — 0

For a scalar input, the design achieves a clock frequency of 523.01 MHz. The design uses these resources.

Resource	Number Used
LUT	8402
FFS	18819
Xilinx LogiCORE® DSP48	129

For a 4-by-1 vector input, the design achieves a clock frequency of 512.82 MHz. The design uses these resources.

Resource	Number Used
LUT	27712
FFS	30322
Xilinx LogiCORE DSP48	516

Using a vector input can increase the throughput, however, doing so also increases the number of hardware resources that the block uses.

References

[1] Hayes, M.H. Statistical Digital Signal Processing and Modeling. New York: John Wiley & Sons, 1996.

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using Simulink® Coder™.

This block supports C/C++ code generation for Simulink accelerator and rapid accelerator modes and for DPI component generation.

HDL Code Generation

Generate VHDL, Verilog and SystemVerilog code for FPGA and ASIC designs using HDL Coder™.

HDL Coder™ provides additional configuration options that affect HDL implementation and synthesized logic.

HDL Architecture

This block has one default HDL architecture.

HDL Block Properties

ConstrainedOutputPipeline	Number of registers to place at the outputs by moving existing delays within your design. Distributed pipelining does not redistribute these registers. The default is 0. For more details, see ConstrainedOutputPipeline (HDL Coder).
InputPipeline	Number of input pipeline stages to insert in the generated code. Distributed pipelining and constrained output pipelining can move these registers. The default is 0. For more details, see InputPipeline (HDL Coder).
OutputPipeline	Number of output pipeline stages to insert in the generated code. Distributed pipelining and constrained output pipelining can move these registers. The default is 0. For more details, see OutputPipeline (HDL Coder).

Version History

Introduced in R2023a