Goal
Estimate the limits of a transceiver caused by noise, strong signals and nonlinearity.
Prerequisites: Parts 03–10.
Future lessons
- Gaussian noise and white noise: different properties. Power spectral density (PSD), bandwidth and signal-to-noise ratio (SNR).
- Thermal noise kTB, noise figure and sensitivity; the link to Amateurfunk Klasse A.
- A weak signal near a strong one: ADC overload, quantization and spurious-free dynamic range (SFDR).
- The model a1·x + a2·x² + a3·x³: harmonics, third-order intermodulation (IMD3), compression and the third-order intercept point IP3.
Lab plan
- Add noise with a given SNR and a fixed generator seed; compare the power with the integral of the PSD.
- Receive a weak signal near a strong one and check that a filter after the ADC does not remove an overload that already occurred.
- Do a two-tone test and measure how the intermodulation components grow.
We do the experiments in Python with NumPy, SciPy and Matplotlib. We keep the parameters, units and plots. The base models use float64 and complex128; we add bit-depth limits in separate experiments.
Completion criteria
- I can tell the difference between dB, dBFS and dBm; to go to physical power, I set a calibration.
- I can explain the effect of bandwidth on noise, and I can tell overload from insufficient filtering.
Result for the main project
An interference model and a set of quality measurements for the receiver and the transmitter.