Math Models

Constants 

In our RF-Math Module there are some constants defined:

T0 = np.float64(290)
N0 = np.float64(10*np.log10(constants.k * 290 * 1 * 1000))

Parameter 

Gain 

The gain gets calculated from the given parameter from each device. The values are linear interpolated between the given frequency points.

Noise Temperature / Noise Figure 

The noise parameter are internally calculated as noise-temperature. All other noise parameter are converter from the noise-temperature to their scale.

RFMath.convert_T_n(B=1)[source]

Convert Noise Temperature to Noise Power

Parameters:: T (float) – Noise Temperature in [°K]
Returns:: n – noise power in [dBm/Hz] / [dBm/(B*Hz)]
Return type:: numpy.float

RFMath.convert_T_NF(Gain)[source]

Convert Noise Temperature to NoiseFigure

Parameters:

T (float) – Noise Temperature in [°K]
Gain (float) – Accumulated Gain in [dB]

Returns:

NF – Noisefigure in [dB]

Return type:

numpy.float

Notes

Output Power 

The output power is simply the input power multiplied by the gain

P1dB 

The output P1 parameter is simplistic modeled. It’s just a saturating upper bound.

OIP3 

The OIP3 resp. IP3 in general is calculated by the famous equation:

IP3 = P - (2*(IP3) - P + 6)

It is assumed a single CW signal (thus the +6dB)

SNR 

The signal-to-noise ratio is exactly the signal - noise in dB.

SFDR 

The spurious-dynamic range uses a simple approach and is the distance between output power and intermodulation 3 product.

Dynamic Range 

The dynamic range is defined as the smaller of the SNR or SFDR.