Spectrum Sensing for the SALSAT Nanosatellite

This repository is part of my bachelor thesis: Spectrum Sensing for the SALSAT Nanosatellite. It provides algorithms and simulations for blind wideband spectrum sensing with special emphasis on space applications. More background information on the SALSAT project can be found here. The code is a constant work in progress and ever-evolving. So stay with me if something is not working or behaving "weird". You can always contact me (details at the bottom).

This repository is split up into three main directories:

• specsens/ the python package that contains all relevant code and algorithms.
• notebooks/ contains usage examples for all parts of the specsens package.
• graphs/ contains the code used to create the graphs in my thesis.

Usage

The code is written in python 3.6 using the packages listed in requirements.txt. Consider using a virtual environment. Ensure that you have matplotlib 3.1.x installed. Other versions might give weird artifacts. I am still looking into the problem at this point in time.

Step by step installation guide

Starting with a clean Ubuntu 18.04 system (should also work with other Linux distributions) you will need to follow these steps:

• Clone the repository using git, run git clone git@git.tu-berlin.de:salsat/spectrum-sensing.git (you will need to have ssh with GitLab setup for this to work)
• Navigate into the repository, run cd spectrum-sensing
• Ensure you have Python 3, run python3 --version
• Install Python Virtual Environments, run sudo apt install python3-venv
• Install pip3, run sudo apt install python3-pip
• Create a new virtual environment, run python3 -m venv specsens-env
• To activate the virtual environment, run source specsens-env/bin/activate
• (To deactivate the virtual environment, run deactivate)
• Use the requirements.txt to install required python packages, run pip3 install -r requirements.txt
• Finally, start Jupyter Notebook, run jupyter notebook

If you already have some of the parts installed, just skip the corresponding steps. Feel free to open an issue or contact me if you run into any problems.

Specsens Package

In order to organize and structure my work, I created the specsens package. It contains most of the algorithms and simulations that I developed. It behaves like a regular python package (one can just import specsens). It consists of the following directories:

• eigenvalue_detect/ Eigenvalue detection algorithms based on signal covariance matrices.
• energy_detect/ Energy detector and functions to calculate performance statistics (prob. of false alarm, prob. of detection, etc.).
• noise_estimation/ Noise estimation methods and related utilities. (Currently still part of eigenvalue_detect, work in progress.)
• plot/ Plotting functions to visualize signals and detection results.
• signal/ Signal and noise generators used mainly for simulations, as well as some utilities that simplify working with signals.
• simulation/ Simulations to evaluate performance statistics.
• wideband_detect/ Wideband detection algorithms (wideband energy detector, wavelet edge detector, etc.).

When using the specsens package locally you need to add the following lines before you import the package. This tells the python interpreter where to find the package:

import sys
sys.path.insert(0, '..')

Replace .. with the path to the specsens package, when moving things around.

Notebooks

The notebooks provide a visual presentation of the specsens package and its functionalities. They allow you to get an overview of what the algorithms are doing in order to better understand them. Feel free to play around with the code and observe the behavior, especially in the simulations.

• 01_signal_and_noise.ipynb basic overview over complex signal and noise generation + time-domain visualization.
• 02_simple_energy_detector.ipynb simple narrowband time-domain energy detector.
• 03_energy_detector_statistics.ipynb analytical performance statistics overview and comparison (prob. of false alarm, prob. of detection, etc.) ( comparison between Chi-square based statistics and central limit theorem based statistics).
• 04_energy_detector_simulation.ipynb simulation using simple energy detector and comparison between analytical and numerical statistics.
• 05_short_time_fourier_transform.ipynb short-time Fourier transform + frequency domain visualization.
• 06_wideband_signal.ipynb wideband signal generation using signal matrix.
• 06a_doppler_signal.ipynb wideband doppler signal generation that aims to realistically reproduce doppler shifts.
• 07_wideband_detect.ipynb short-time Fourier transform-based wideband energy detection.
• 07a_wideband_detect_simulation.ipynb simulation for wideband energy detection.
• 07b_wideband_detect_doppler.ipynb wideband detection of doppler signal.
• 08_edge_detect.ipynb spectrum edge detection using wavelet transforms.
• 09_variable_band_detect.ipynb variable band wideband energy detection using edge detection.
• 09a_variable_band_detect_doppler.ipynb variable band wideband energy detection of doppler signal.
• 10_noise_estimation_simulation.ipynb energy detection using noise estimation.
• 11_eigenvalue_detector.ipynb eigenvalue detector based on covariance matrix.
• 12_eigenvalue_detector_simulation.ipynb simulation of eigenvalue detector.
• 13_eigenvalue_detector_filter.ipynb eigenvalue detector with bandpass filter.
• 14_eigenvalue_detector_whitening.ipynb eigenvalue detector with bandpass filter using noise whitening.
• 15_eigenvalue_simulation_whitening.ipynb simulation of eigenvalue detector with bandpass filter and noise whitening.
• 16_eigenvalue_noise_estimation.ipynb noise power estimation using covariance matrix eigenvalues.
• 17_eigenvalue_noise_estimation_simulation.ipynb simulation of wideband energy detection using eigenvalue noise power estimation.
• 18_noise_estimation_comparison.ipynb comparison simulation of noise estimation techniques.

TODO

General

• Upload first version to gitlab
• Notebook documentation in readme
• Upload papers and create basic paper index that helps to connect paper and python implementation
• Documentation for every function using sphinx
• Upload specsens to PyPi

Functional

• Time space plotting functions. (time_plot.py)
• Implement sigmoid detection output
• Cleanup variable band spectrum sensing
• Add pfa and pd eigenvalue stats
• Create eigenvalue simulation with filter and whitening matrix
• Combine eigenvalue detection and noise estimation based energy detection, use energy detection as heuristic and binary search pattern to find noise only band to use for noise estimation
• Use small eigenvalues for noise estimation

Author

Fabian Peddinghaus peddinghaus@tu-berlin.de