sdr

The sdr library is a Python 3 package for software-defined radio (SDR).

The goal of sdr is to provide tools to design, analyze, build, and test digital communication systems in Python. The library relies on and is designed to be interoperable with NumPy, SciPy, and Matplotlib. Performance is also very important. So, where possible, Numba is used to accelerate computationally intensive functions.

Additionally, the library aims to replicate relevant functionality from MATLAB’s Communications and DSP Toolboxes.

We are progressively adding functionality to the library. If there is something you’d like to see included in sdr, please open an issue on GitHub.

Installation

The latest version of sdr can be installed from PyPI using pip.

python3 -m pip install sdr

Features

View all available classes and functions in the API Reference.

  • Digital signal processing: Finite impulse response (FIR) filters, FIR filter design, infinite impulse response (IIR) filters, polyphase interpolators, polyphase decimators, polyphase resamplers, polyphase channelizers, Farrow arbitrary resamplers, fractional delay FIR filters, FIR moving averagers, FIR differentiators, IIR integrators, IIR leaky integrators, complex mixing, real/complex conversion.

  • Sequences: Binary, Gray, Barker, Hadamard, Walsh, Gold, Kasami, Zadoff-Chu, \(m\)-sequences, preferred pairs, Fibonacci LFSRs, Galois LFSRs, LFSR synthesis.

  • Coding: Block interleavers, additive scramblers.

  • Modulation: Phase-shift keying (PSK), \(\pi/M\) PSK, offset QPSK, continuous-phase modulation (CPM), minimum-shift keying (MSK), rectangular pulse shapes, half-sine pulse shapes, Gaussian pulse shapes, raised cosine pulse shapes, root raised cosine pulse shapes, differential encoding.

  • Estimation: TOA, TDOA, FOA, FDOA Cramér-Rao lower bounds.

  • Detection: Detector probability density functions (PDFs). Theoretical probability of detection, probability of false alarm, and thresholds. Detection performance approximations. Coherent gain, coherent gain loss (CGL), and non-coherent gain. Maximum-allowable integration time and frequency offset.

  • Synchronization: Numerically controlled oscillators (NCO), loop filters, closed-loop phase-locked loop (PLL) analysis, phase error detectors (PEDs), automatic gain control (AGC).

  • Measurement: Energy, power, voltage, Euclidean distance, Hamming distance, bit/symbol error rate, error vector magnitude (EVM), RMS integration time, and RMS bandwidth.

  • Conversions: Between linear units and decibels. Between \(E_b/N_0\), \(E_s/N_0\), and \(S/N\).

  • Simulation: Binary symmetric channels (BSC), binary erasure channels (BEC), discrete memoryless channels (DMC). Apply additive white Gaussian noise (AWGN), frequency offset, sample rate offset, IQ imbalance.

  • Link budgets: BSC, BEC, AWGN, and BI-AWGN channel capacity, Shannon’s limit on \(E_b/N_0\) and \(S/N\), free-space path loss, antenna gain.

  • Miscellaneous: Numerical calculation of PDF of sum and product of random variables. Packing and unpacking binary data, hexdump of binary data.

  • Plotting: Time-domain, raster, correlation, stem, discrete Fourier transform (DFT), discrete-time Fourier transform (DTFT), periodogram, spectrogram, constellation, symbol map, eye diagram, phase tree, bit error rate (BER), symbol error rate (SER), probability of detection, receiver operating characteristic (ROC), detection PDFs, impulse response, step response, zeros/poles, magnitude response, phase response, phase delay, and group delay.

Citation

If this library was useful to you in your research, please cite us. Following the GitHub citation standards, here is the recommended citation.

@software{Hostetter_SDR_2023,
   title = {{sdr: A software-defined radio package for Python}},
   author = {Hostetter, Matt},
   month = {7},
   year = {2023},
   url = {https://github.com/mhostetter/sdr},
}

Hostetter, M. (2023). sdr: A software-defined radio package for Python [Computer software]. https://github.com/mhostetter/sdr
Last update: Jul 28, 2024