General QAM Demodulator Baseband

A real or complex vector that lists the constellation points.

Determines whether the block produces integers or binary representations of integers.

If you set this parameter to Integer, the block produces integers.

If you set this parameter to Bit, the block produces a group of K bits, called a binary word, for each symbol, when Decision type is set to Hard decision. If Decision type is set to Log-likelihood ratio or Approximate log-likelihood ratio, the block outputs bitwise LLR and approximate LLR, respectively.

Specifies the use of hard decision, LLR, or approximate LLR during demodulation. For more information, see Hard- vs. Soft-Decision Demodulation.

To enable this parameter, set Output type to Bit.

When you set this parameter to Dialog, you can then specify the noise variance in the Variance parameter. When you set this option to Port, a port appears on the block through which the noise variance can be input.

To enable this parameter, set Decision type to Approximate log-likelihood ratio or Log-likelihood ratio.

This parameter appears when the Variance source is set to Dialog and specifies the noise variance in the input signal. This parameter is tunable in normal mode, Accelerator mode, and Rapid Accelerator mode. This parameter is nontunable for fixed-point inputs.

If you use the Simulink^® Coder™ rapid simulation (RSIM) target to build an RSIM executable, then you can tune the parameter without recompiling the model. This is useful for Monte Carlo simulations in which you run the simulation multiple times (perhaps on multiple computers) with different amounts of noise.

The exact LLR algorithm computes exponentials using finite precision arithmetic. For computations involving very large positive or negative magnitudes, the exact LLR algorithm yields:

The approximate LLR algorithm does not compute exponentials. You can avoid Inf, -Inf, and NaN results by using the approximate LLR algorithm.

The block supports the following Output options:

When you set the parameter to Inherit via internal rule (default setting), the block inherits the output data type from the input port. The output data type is the same as the input data type if the input is of type single or double.

For integer outputs, you can set this block's output to Inherit via internal rule (default setting), Smallest unsigned integer, int8, uint8, int16, uint16, int32, uint32, single, and double.

For bit outputs, when you set Decision type to Hard decision, you can set the output to Inherit via internal rule, Smallest unsigned integer, int8, uint8, int16, uint16, int32, uint32, boolean, single, or double.

When you set Decision type to Hard decision or Approximate log-likelihood ratio and the input is a floating point data type, then the output inherits its data type from the input. For example, if the input is of data type double, the output is also of data type double. When you set Decision type to Hard decision or Approximate log-likelihood ratio, and the input is a fixed-point signal, the Output parameter, located in the Fixed-Point algorithm parameters region of the Data-Type tab, specifies the output data type.

When you set the parameter to Smallest unsigned integer, the output data type is selected based on the settings used in the Hardware Implementation pane of the Configuration Parameters dialog box. If you select ASIC/FPGA in the Hardware Implementation pane, the output data type is the ideal minimum size, i.e., ufix(1) for bit outputs, and $$ufix\left(\lceil {\log }_{2}M\rceil \right)$$ for integer outputs. For all other choices, the Output data type is an unsigned integer with the smallest available word length large enough to fit the ideal minimum size, usually corresponding to the size of a char (e.g., uint8).

Use this parameter to specify the rounding method to be used when the result of a fixed-point calculation does not map exactly to a number representable by the data type and scaling storing the result.

For more information, see Rounding Modes or Rounding Mode: Simplest (Fixed-Point Designer).

Use this parameter to specify the method to be used if the magnitude of a fixed-point calculation result does not fit into the range of the data type and scaling that stores the result:

For more information, see the Saturate on integer overflow parameter subsection of Specify Fixed-Point Attributes for Blocks.

Use this parameter to define the data type of the Signal constellation parameter.

Use this parameter to specify the data type for Accumulator 1:

Use this parameter to specify the data type for Product input.

Use this parameter to select the data type for Product output.

Use this parameter to specify the data type for Accumulator 2:

When you select Inherit via internal rule, the block automatically calculates the accumulator data type. The internal rule first calculates ideal, full-precision word length and fraction length as follows:

WL_{ideal accumulator 3} = WL_{input to accumulator 3} + 1

FL _{ideal accumulator 3} = FL _{input to accumulator 3}.

After the full-precision result is calculated, your particular hardware may still affect the final word and fraction lengths set by the internal rule. For more information, see The Effect of the Hardware Implementation Pane on the Internal Rule.

The internal rule always sets the sign of data-type to Signed.

To enable this parameter, set Variance source to Dialog. This parameter appears in the Data Types tab.

When you select Inherit via internal rule, the Output data type is automatically set for you.

If you set the Variance source parameter to Dialog, the output is a result of product operation as shown in the Noise Variance Operation Modes Signal Flow Diagram Fixed-Point Signal Flow Diagram for Approximate LLR Mode: Noise Variance Operation Modes. In this case, it follows the internal rule for Product data types specified in the Inherit via Internal Rule topic.

If the Variance source parameter is set to Port, the output is a result of division operation as shown in the signal flow diagram. In this case, the internal rule calculates the ideal, full-precision word length and fraction length as follows:

WL _output = max(WL _{Noise scaling input}, WL _{Noise variance})

FL _output = FL _{Noise scaling input (dividend)}– FL _{Noise variance (divisor)}.

After the full-precision result is calculated, your particular hardware may still affect the final word and fraction lengths set by the internal rule. For more information, see The Effect of the Hardware Implementation Pane on the Internal Rule.

The internal rule for Output always sets the sign of data-type to Signed.