Author: MS.c Jose Edilson Silva Filho Email: edilsonfilho@lesc.ufc.br
Note: This repository collects some code that I created. It does not have details of the blockchain usage nor the simulation with all the subsystems, because this code is restricted. The intent of the current repository is to show academically some simulations to colleagues.
Our proposal with this project is to provide mission control with greater communication capacity with its satellites through a decentralized infrastructure of ground stations. We thus provide an environment in which each ground station participating in the consortium communicates with the satellites in the network.A consortium is a group of institutions and/or ground stations that own a node running the blockchain. In this activity, we promote a firmware update routine, where the satellite is a client of the blockchain that runs on the ground station servers.
Using the resources of Matlab 2 we create a space mission composed of a nanosatellite that we call SACODE BR-1 and two ground stations, being one called UFC-PICI with longitude and latitude of the university campus of the Federal University of Ceará at the PICI 3 campus. The objective was to estimate the average communication time between the nanosatellite and the ground station at this location point and thus use it as a basis for the other experiments. We chose a mission time of 90 days to collect a list of communication windows between the communication windows between the nanosatellite and the ground station. o Source code 1 Matlab simulation nanosatellite mission presents an example of how to create a space mission using space mission using Matlab resources. The orbit chosen was the LEO orbit.
startTime = datetime(2022,5,1,5,36,0);
stopTime = startTime + days(90);
sampleTime = 60;
% configuration orbit
%% configuration orbit
%semiMajorAxis (semi-major axis) is half the maximum axis length of an ellipse
%describing the satellites orbit around the Earth, measured in meters
semiMajorAxis = 7155000;
%eccentricity is a measure of how the satellites orbit deviates
% of a perfect circular orbit. It is a number between 0 and 1. The closer to zero,
% more circular is the orbit; the closer to 1, the more elliptical the orbit.
eccentricity = 0.0001030;
%inclination is the angle between the satellites orbital plane and the Earths equator,
% measured in degrees. It is the inclination of the orbit relative to the Earths equator.
inclination = 98.5;
%rightAscensionOfAscendingNode (right ascension of ascending node) is the angle measured from the
% Earths equator to the point where the satellites orbit crosses the equator going north, measured in degrees.
% Is the longitude of the point where the satellites orbit crosses the equator going north.
rightAscensionOfAscendingNode = 161.5;
%argumentOfPeriapsis (perihelion argument) is the angle measured from the ascending node to the point where the satellite
% is closest to Earth, measured in degrees. It is the measure of the axis orientation of the ellipse of the orbit.
argumentOfPeriapsis = 0;
%trueAnomaly is the angle measured from perihelion to the satellites current position along
% its orbit, measured in degrees. It is the position of the satellite along the ellipse of its orbit at the time it is observed.
trueAnomaly = 263.19;
%configuration GS
lat = -3.7484767;
lon = -38.5788343;
%% Calculating DirectPatchDooplerShift
%To calculate the v_sat_cubesat/c ratio for a satellite in LEO orbit and a ground station, it is necessary to know the altitude of the satellite and the distance between the satellite and the ground station.
%Using the equation shown earlier, we can calculate v_sat_cubesat/c as follows:
%First, we need to calculate the orbital speed of the satellite using the following formula:
%V = sqrt(mu / r)
%onde:
%mu is the Earths gravitational constant (398600.4418 km^3/s^2)
%r is the distance between the center of the Earth and the satellite (Earth radius + satellite altitude)
%Substituting the values, we have:
V = sqrt(398600.4418 / (6378.137 + (semiMajorAxis))); %Km/s
%Speed of light is approximately 299792.458 km/s.
c = 299792.458;
%Finally, we can calculate the v_sat_cubesat/c ratio:
rate_v_sat = V/c;
DirectPathDopplerShift = -2*rate_v_sat * fc;
rainChannel = comm.RicianChannel(...
'SampleRate', fs, ...
'KFactor', 1.45, ...
'DirectPathDopplerShift',DirectPathDopplerShift, ...
'MaximumDopplerShift', 5, ...
'PathDelays', [0 1.5e-5 3.5e-5], ...
'AveragePathGains', [0 -2 -10], ...
'NormalizePathGains', true, ...
'DopplerSpectrum', doppler('Gaussian', 0.1));
This code snippet creates a rainChannel object of the comm.RicianChannel class to model the communication channel that the signal of interest passes through. The comm.RicianChannel function is used to create a communication channel that simulates the conditions of propagation of the signal of interest over a wireless medium.
The input parameters specified for creating the rainChannel object are:
- SampleRate: specifies the sampling rate in Hz;
- KFactor: specifies the ratio between the direct signal energy and the scattered signal energy. This parameter is used to model the relationship between the direct signal and the reflected or scattered signals in the propagation environment; DirectPathDopplerShift: specifies the Doppler frequency shift in the direct signal. This is used to model the Doppler effect caused by motion of the transmitter, receiver, or both;
- MaximumDopplerShift: specifies the maximum Doppler frequency shift in the scattered signal. This is used to model the Doppler effect caused by relative motion between the transmitter, receiver, or both;
- PathDelays: specifies a vector of propagation delays in seconds for the different propagation paths of the signal. Each value in the vector corresponds to a different path;
- AveragePathGains: specifies a vector of average path gains (in dB) for the different signal propagation paths. Each value in the vector corresponds to a different path;
- NormalizePathGains: specifies whether the average path gains should be normalized so that the direct path has an average path gain of 0 dB;
- DopplerSpectrum: specifies the Doppler spectrum model used to model the frequency fluctuations caused by signal propagation through the communication channel. In this case the model is "Gaussian" with a bandwidth of 0.1 Hz. Some of the simulation results are:
Each communication window between the satellite and the ground station, in the code and in the graph were called interactions.
The Y-axis of the graph represents the bit error rate (BER) on a linear scale. In other words, it represents the percentage of bits received incorrectly in relation to the total number of bits transmitted. For example, if the bit error rate is 0.01, this means that 1% of the transmitted bits were received incorrectly. The scale is linear because it is a direct representation of the bit error rate. If the value on the y-axis is 1, this means that all of the transmitted bits were received incorrectly, (I'm still improving the code and soon I'll post more interesting results for the simulation). The scale used on the y-axis is from 0 to 1, representing the percentage of bits received correctly, i.e., a scale from 0 to 100%. What does this graph mean? It simply shows us in which iterations, or in which overpass, the errors were greater. Investigating why this happened is another step.
References for the codes:
[1] https://www.mathworks.com/help/comm/ref/rfprop.rain.html
[2] https://www.mathworks.com/help/aerotbx/ug/matlabshared.satellitescenario.satellite.html
[3] https://www.mathworks.com/help/aerotbx/ug/model-visualize-and-analyze-satellite-scenario.html
[4] https://www.mathworks.com/help/aerotbx/ug/satellite-constellation-access-to-a-ground-station.html
[5] https://www.mathworks.com/help/aerotbx/satellite-scenario.html?s_tid=CRUX_lftnav