Hi everybody,
I want to simulate an elastic tooth mesh within my simscape multibody model. The model is very simple and consists only two gears. The model works fine with the "Common Gear Constraint" block, but I want to simulate spring and damping effects. Therefore I added a Torsional Spring-Damper which I connected to the model via a Rotational Multibody Interface.

Error:An error occurred during simulation and the simulation was terminated
Caused by:
[‘Test_ElastischerZahneingriff_1/Solver Configuration’]: Initial conditions solve failed to converge.
Nonlinear solver: Linear Algebra error. Failed to solve using iteration matrix.
The model may not give enough information to make it possible to solve for values of some of its variables. Specific advice is given below.

all components and nodal across variables involved
Tie variable ‘Torsional_Spring_Damper.R.w’ (Rotational velocity) to a definite value, for example by connecting an appropriate domain reference block.

Dependency found among topology equations. Check for missing reference node.

Is anybody out there who can help me fixing this error?
You can see the model in the pictures.

Thank’s everybody for any helpful comment….

Kind regards,
ThomasHi everybody,
I want to simulate an elastic tooth mesh within my simscape multibody model. The model is very simple and consists only two gears. The model works fine with the "Common Gear Constraint" block, but I want to simulate spring and damping effects. Therefore I added a Torsional Spring-Damper which I connected to the model via a Rotational Multibody Interface.

Error:An error occurred during simulation and the simulation was terminated
Caused by:
[‘Test_ElastischerZahneingriff_1/Solver Configuration’]: Initial conditions solve failed to converge.
Nonlinear solver: Linear Algebra error. Failed to solve using iteration matrix.
The model may not give enough information to make it possible to solve for values of some of its variables. Specific advice is given below.

all components and nodal across variables involved
Tie variable ‘Torsional_Spring_Damper.R.w’ (Rotational velocity) to a definite value, for example by connecting an appropriate domain reference block.

Dependency found among topology equations. Check for missing reference node.

Is anybody out there who can help me fixing this error?
You can see the model in the pictures.

Thank’s everybody for any helpful comment….

Kind regards,
Thomas Hi everybody,
I want to simulate an elastic tooth mesh within my simscape multibody model. The model is very simple and consists only two gears. The model works fine with the "Common Gear Constraint" block, but I want to simulate spring and damping effects. Therefore I added a Torsional Spring-Damper which I connected to the model via a Rotational Multibody Interface.

Error:An error occurred during simulation and the simulation was terminated
Caused by:
[‘Test_ElastischerZahneingriff_1/Solver Configuration’]: Initial conditions solve failed to converge.
Nonlinear solver: Linear Algebra error. Failed to solve using iteration matrix.
The model may not give enough information to make it possible to solve for values of some of its variables. Specific advice is given below.

all components and nodal across variables involved
Tie variable ‘Torsional_Spring_Damper.R.w’ (Rotational velocity) to a definite value, for example by connecting an appropriate domain reference block.

Dependency found among topology equations. Check for missing reference node.

Is anybody out there who can help me fixing this error?
You can see the model in the pictures.

Thank’s everybody for any helpful comment….

Kind regards,
Thomas simscape, multibody, gear, elastic tooth mesh MATLAB Answers — New Questions

​

Post Content Post Content ccs ti MATLAB Answers — New Questions

​

clear all
close all
Data_Seq=[1,1,1,0,0,0,1,0]

stem(Data_Seq)
bitrate= 1000000;
SR=1/bitrate;
ylim([-1 1])
t=0:0.01:length(Data_Seq)
Data_Seq2=[1,1,1,-1,-1,-1,1,-1]
stem(Data_Seq2)
A = Data_Seq2(1:2:end,:) %
B = Data_Seq2(2:2:end,:)
stem(A)
even = Data_Seq(2:2:end);
odd = Data_Seq(1:2:end);

stem(even)
// i Want to seperate even and odd signal but not undrstanding how to plotclear all
close all
Data_Seq=[1,1,1,0,0,0,1,0]

stem(Data_Seq)
bitrate= 1000000;
SR=1/bitrate;
ylim([-1 1])
t=0:0.01:length(Data_Seq)
Data_Seq2=[1,1,1,-1,-1,-1,1,-1]
stem(Data_Seq2)
A = Data_Seq2(1:2:end,:) %
B = Data_Seq2(2:2:end,:)
stem(A)
even = Data_Seq(2:2:end);
odd = Data_Seq(1:2:end);

stem(even)
// i Want to seperate even and odd signal but not undrstanding how to plot clear all
close all
Data_Seq=[1,1,1,0,0,0,1,0]

stem(Data_Seq)
bitrate= 1000000;
SR=1/bitrate;
ylim([-1 1])
t=0:0.01:length(Data_Seq)
Data_Seq2=[1,1,1,-1,-1,-1,1,-1]
stem(Data_Seq2)
A = Data_Seq2(1:2:end,:) %
B = Data_Seq2(2:2:end,:)
stem(A)
even = Data_Seq(2:2:end);
odd = Data_Seq(1:2:end);

stem(even)
// i Want to seperate even and odd signal but not undrstanding how to plot qpsk modulation MATLAB Answers — New Questions

​

% Parameters
M = 4; % MQAM order (e.g., 16-QAM)
N = 1e5; % Number of transmit symbols

% Define SNR range
SNR_dB_range = 0:10;
num_SNR_points = length(SNR_dB_range);

% Initialize SER array
SER_values = zeros(num_SNR_points, 1);

% Create MQAM constellation
constellation = createMQAM(M);
% Loop over each SNR value
for idx = 1:num_SNR_points
SNR_dB = SNR_dB_range(idx);

% Generate transmit symbols
transmitSymbols = generateSymbols(constellation, N);

% Transmit through AWGN channel
receivedSymbols = transmitSymbolsThroughAWGN(transmitSymbols, SNR_dB);

% Demodulate symbols
decodedSymbols = demodulateSymbols(receivedSymbols, constellation);

% Compute Symbol Error Rate
SER = computeSER(transmitSymbols, decodedSymbols);

% Store SER value
SER_values(idx) = SER;

% Compute Theoretical SER
SER_theoretical = computeTheoreticalSER(M, SNR_dB_range);
SER_theoretical_values = SER_theoretical;
end

% Plot SER vs SNR
figure;
semilogy(SNR_dB_range, SER_values, ‘o-‘, ‘DisplayName’, ‘Simulated SER’);
hold on;
semilogy(SNR_dB_range, SER_theoretical_values, ‘r–‘, ‘DisplayName’, ‘Theoretical SER’);
xlabel(‘SNR (dB)’);
ylabel(‘Symbol Error Rate (SER)’);
title(‘SER vs. SNR for MQAM’);
legend;
grid on;

function constellation = createMQAM(M)
% Create an MQAM constellation manually
% M: Number of symbols (e.g., 16, 64, etc.)

% Check that M is a perfect square
k = sqrt(M);
if mod(k,1) ~= 0
error(‘M must be a perfect square’);
end

% Generate constellation points
k = round(k); % Number of points per side
[X, Y] = meshgrid(linspace(-k+1, k-1, k), linspace(-k+1, k-1, k));
constellation = X(:) + 1i*Y(:);
end

function symbols = generateSymbols(constellation, N)
% Generate N random transmit symbols
% constellation: The MQAM constellation points
% N: Number of symbols to generate

numPoints = length(constellation);
indices = randi([1 numPoints], [N, 1]); % Randomly choose indices
symbols = constellation(indices); % Select symbols from the constellation
end

function received = transmitSymbolsThroughAWGN(symbols, SNR_dB)
% Transmit symbols through a SISO AWGN channel
% symbols: Transmit symbols
% SNR_dB: Signal-to-noise ratio in dB

% Convert SNR from dB to linear scale
SNR_linear = 10^(SNR_dB / 10);

% Calculate noise variance
noise_variance = 1 / SNR_linear;

% Generate noise
noise = sqrt(noise_variance / 2) * (randn(size(symbols)) + 1i*randn(size(symbols)));

% Received symbols
received = symbols + noise;
end

function decodedSymbols = demodulateSymbols(received, constellation)
% Demodulate symbols using minimum distance decoding
% received: Received symbols
% constellation: MQAM constellation points

numSymbols = length(received);
numPoints = length(constellation);

% Initialize decoded symbols
decodedSymbols = zeros(size(received));

for i = 1:numSymbols
% Compute distances to all constellation points
distances = abs(received(i) – constellation).^2;
% Find the closest constellation point
[~, idx] = min(distances);
decodedSymbols(i) = constellation(idx);
end
end

function SER = computeSER(originalSymbols, decodedSymbols)
% Compute symbol error rate
% originalSymbols: Transmit symbols
% decodedSymbols: Received and decoded symbols

numErrors = sum(originalSymbols ~= decodedSymbols);
totalSymbols = length(originalSymbols);

SER = numErrors / totalSymbols;
end

function SER_theoretical = computeTheoreticalSER(M, SNR_dB)
% Compute the theoretical SER for MQAM in AWGN
% M: Number of symbols (e.g., 16, 64, etc.)
% SNR_dB: SNR in dB

% Convert SNR from dB to linear scale
SNR_linear = 10.^(SNR_dB / 10);

% Compute theoretical SER
k = sqrt(M); % Number of points per side
if mod(k,1) ~= 0
error(‘M must be a perfect square’);
end
% Calculate SER using the Q-function
SER_theoretical = 4*(1-1./sqrt(M))*qfunc(sqrt(3./(M-1)*SNR_linear));
end

In the above code when I plot the SER v/s SNR and compare the simulation and analytical values don’t match. Can someone tell me what is the error in my code and how can I match the curves ?% Parameters
M = 4; % MQAM order (e.g., 16-QAM)
N = 1e5; % Number of transmit symbols

% Define SNR range
SNR_dB_range = 0:10;
num_SNR_points = length(SNR_dB_range);

% Initialize SER array
SER_values = zeros(num_SNR_points, 1);

% Create MQAM constellation
constellation = createMQAM(M);
% Loop over each SNR value
for idx = 1:num_SNR_points
SNR_dB = SNR_dB_range(idx);

% Generate transmit symbols
transmitSymbols = generateSymbols(constellation, N);

% Transmit through AWGN channel
receivedSymbols = transmitSymbolsThroughAWGN(transmitSymbols, SNR_dB);

% Demodulate symbols
decodedSymbols = demodulateSymbols(receivedSymbols, constellation);

% Compute Symbol Error Rate
SER = computeSER(transmitSymbols, decodedSymbols);

% Store SER value
SER_values(idx) = SER;

% Compute Theoretical SER
SER_theoretical = computeTheoreticalSER(M, SNR_dB_range);
SER_theoretical_values = SER_theoretical;
end

% Plot SER vs SNR
figure;
semilogy(SNR_dB_range, SER_values, ‘o-‘, ‘DisplayName’, ‘Simulated SER’);
hold on;
semilogy(SNR_dB_range, SER_theoretical_values, ‘r–‘, ‘DisplayName’, ‘Theoretical SER’);
xlabel(‘SNR (dB)’);
ylabel(‘Symbol Error Rate (SER)’);
title(‘SER vs. SNR for MQAM’);
legend;
grid on;

function constellation = createMQAM(M)
% Create an MQAM constellation manually
% M: Number of symbols (e.g., 16, 64, etc.)

% Check that M is a perfect square
k = sqrt(M);
if mod(k,1) ~= 0
error(‘M must be a perfect square’);
end

% Generate constellation points
k = round(k); % Number of points per side
[X, Y] = meshgrid(linspace(-k+1, k-1, k), linspace(-k+1, k-1, k));
constellation = X(:) + 1i*Y(:);
end

function symbols = generateSymbols(constellation, N)
% Generate N random transmit symbols
% constellation: The MQAM constellation points
% N: Number of symbols to generate

numPoints = length(constellation);
indices = randi([1 numPoints], [N, 1]); % Randomly choose indices
symbols = constellation(indices); % Select symbols from the constellation
end

function received = transmitSymbolsThroughAWGN(symbols, SNR_dB)
% Transmit symbols through a SISO AWGN channel
% symbols: Transmit symbols
% SNR_dB: Signal-to-noise ratio in dB

% Convert SNR from dB to linear scale
SNR_linear = 10^(SNR_dB / 10);

% Calculate noise variance
noise_variance = 1 / SNR_linear;

% Generate noise
noise = sqrt(noise_variance / 2) * (randn(size(symbols)) + 1i*randn(size(symbols)));

% Received symbols
received = symbols + noise;
end

function decodedSymbols = demodulateSymbols(received, constellation)
% Demodulate symbols using minimum distance decoding
% received: Received symbols
% constellation: MQAM constellation points

numSymbols = length(received);
numPoints = length(constellation);

% Initialize decoded symbols
decodedSymbols = zeros(size(received));

for i = 1:numSymbols
% Compute distances to all constellation points
distances = abs(received(i) – constellation).^2;
% Find the closest constellation point
[~, idx] = min(distances);
decodedSymbols(i) = constellation(idx);
end
end

function SER = computeSER(originalSymbols, decodedSymbols)
% Compute symbol error rate
% originalSymbols: Transmit symbols
% decodedSymbols: Received and decoded symbols

numErrors = sum(originalSymbols ~= decodedSymbols);
totalSymbols = length(originalSymbols);

SER = numErrors / totalSymbols;
end

function SER_theoretical = computeTheoreticalSER(M, SNR_dB)
% Compute the theoretical SER for MQAM in AWGN
% M: Number of symbols (e.g., 16, 64, etc.)
% SNR_dB: SNR in dB

% Convert SNR from dB to linear scale
SNR_linear = 10.^(SNR_dB / 10);

% Compute theoretical SER
k = sqrt(M); % Number of points per side
if mod(k,1) ~= 0
error(‘M must be a perfect square’);
end
% Calculate SER using the Q-function
SER_theoretical = 4*(1-1./sqrt(M))*qfunc(sqrt(3./(M-1)*SNR_linear));
end

In the above code when I plot the SER v/s SNR and compare the simulation and analytical values don’t match. Can someone tell me what is the error in my code and how can I match the curves ? % Parameters
M = 4; % MQAM order (e.g., 16-QAM)
N = 1e5; % Number of transmit symbols

% Define SNR range
SNR_dB_range = 0:10;
num_SNR_points = length(SNR_dB_range);

% Initialize SER array
SER_values = zeros(num_SNR_points, 1);

% Create MQAM constellation
constellation = createMQAM(M);
% Loop over each SNR value
for idx = 1:num_SNR_points
SNR_dB = SNR_dB_range(idx);

% Generate transmit symbols
transmitSymbols = generateSymbols(constellation, N);

% Transmit through AWGN channel
receivedSymbols = transmitSymbolsThroughAWGN(transmitSymbols, SNR_dB);

% Demodulate symbols
decodedSymbols = demodulateSymbols(receivedSymbols, constellation);

% Compute Symbol Error Rate
SER = computeSER(transmitSymbols, decodedSymbols);

% Store SER value
SER_values(idx) = SER;

% Compute Theoretical SER
SER_theoretical = computeTheoreticalSER(M, SNR_dB_range);
SER_theoretical_values = SER_theoretical;
end

% Plot SER vs SNR
figure;
semilogy(SNR_dB_range, SER_values, ‘o-‘, ‘DisplayName’, ‘Simulated SER’);
hold on;
semilogy(SNR_dB_range, SER_theoretical_values, ‘r–‘, ‘DisplayName’, ‘Theoretical SER’);
xlabel(‘SNR (dB)’);
ylabel(‘Symbol Error Rate (SER)’);
title(‘SER vs. SNR for MQAM’);
legend;
grid on;

function constellation = createMQAM(M)
% Create an MQAM constellation manually
% M: Number of symbols (e.g., 16, 64, etc.)

% Check that M is a perfect square
k = sqrt(M);
if mod(k,1) ~= 0
error(‘M must be a perfect square’);
end

% Generate constellation points
k = round(k); % Number of points per side
[X, Y] = meshgrid(linspace(-k+1, k-1, k), linspace(-k+1, k-1, k));
constellation = X(:) + 1i*Y(:);
end

function symbols = generateSymbols(constellation, N)
% Generate N random transmit symbols
% constellation: The MQAM constellation points
% N: Number of symbols to generate

numPoints = length(constellation);
indices = randi([1 numPoints], [N, 1]); % Randomly choose indices
symbols = constellation(indices); % Select symbols from the constellation
end

function received = transmitSymbolsThroughAWGN(symbols, SNR_dB)
% Transmit symbols through a SISO AWGN channel
% symbols: Transmit symbols
% SNR_dB: Signal-to-noise ratio in dB

% Convert SNR from dB to linear scale
SNR_linear = 10^(SNR_dB / 10);

% Calculate noise variance
noise_variance = 1 / SNR_linear;

% Generate noise
noise = sqrt(noise_variance / 2) * (randn(size(symbols)) + 1i*randn(size(symbols)));

% Received symbols
received = symbols + noise;
end

function decodedSymbols = demodulateSymbols(received, constellation)
% Demodulate symbols using minimum distance decoding
% received: Received symbols
% constellation: MQAM constellation points

numSymbols = length(received);
numPoints = length(constellation);

% Initialize decoded symbols
decodedSymbols = zeros(size(received));

for i = 1:numSymbols
% Compute distances to all constellation points
distances = abs(received(i) – constellation).^2;
% Find the closest constellation point
[~, idx] = min(distances);
decodedSymbols(i) = constellation(idx);
end
end

function SER = computeSER(originalSymbols, decodedSymbols)
% Compute symbol error rate
% originalSymbols: Transmit symbols
% decodedSymbols: Received and decoded symbols

numErrors = sum(originalSymbols ~= decodedSymbols);
totalSymbols = length(originalSymbols);

SER = numErrors / totalSymbols;
end

function SER_theoretical = computeTheoreticalSER(M, SNR_dB)
% Compute the theoretical SER for MQAM in AWGN
% M: Number of symbols (e.g., 16, 64, etc.)
% SNR_dB: SNR in dB

% Convert SNR from dB to linear scale
SNR_linear = 10.^(SNR_dB / 10);

% Compute theoretical SER
k = sqrt(M); % Number of points per side
if mod(k,1) ~= 0
error(‘M must be a perfect square’);
end
% Calculate SER using the Q-function
SER_theoretical = 4*(1-1./sqrt(M))*qfunc(sqrt(3./(M-1)*SNR_linear));
end

In the above code when I plot the SER v/s SNR and compare the simulation and analytical values don’t match. Can someone tell me what is the error in my code and how can I match the curves ? communication, plot MATLAB Answers — New Questions

​

Hi Everyone,
I am trying to create a 5G environment with USRPs, one as transmitter (PC1 with MATLAB) and other as receiver (PC2 with MATLAB) and they are connect with wired antennas.
I am generating the 5G NR waveform at transmitter side and sending it to receiver over connected antennas.
The waveform signal is sent for 10 seconds and receiver also receiving it for same duration with 1 second provided for transmission delay (through USRP’s USBs and wires). However, the synchronization is failing and the waveform I received are not the same which leads to inability to extract the sent waveform properties like PDCCH and PDSCH, data, etc. The datatypes of sent and received waveform are also different (Complex-double at transmitter, Complex-int16 at receiver).

I used the below link for designing the waveform for transmission.
https://in.mathworks.com/help/5g/ug/5g-nr-waveform-capture-and-analysis-using-software-defined-radio.html
Although there is no details about the receiver side in the above link.

If someone faced the similar issues, kindly suggest some help
If there exist any tutorial for this kind of work Please share?

ThanksHi Everyone,
I am trying to create a 5G environment with USRPs, one as transmitter (PC1 with MATLAB) and other as receiver (PC2 with MATLAB) and they are connect with wired antennas.
I am generating the 5G NR waveform at transmitter side and sending it to receiver over connected antennas.
The waveform signal is sent for 10 seconds and receiver also receiving it for same duration with 1 second provided for transmission delay (through USRP’s USBs and wires). However, the synchronization is failing and the waveform I received are not the same which leads to inability to extract the sent waveform properties like PDCCH and PDSCH, data, etc. The datatypes of sent and received waveform are also different (Complex-double at transmitter, Complex-int16 at receiver).

I used the below link for designing the waveform for transmission.
https://in.mathworks.com/help/5g/ug/5g-nr-waveform-capture-and-analysis-using-software-defined-radio.html
Although there is no details about the receiver side in the above link.

If someone faced the similar issues, kindly suggest some help
If there exist any tutorial for this kind of work Please share?

Thanks Hi Everyone,
I am trying to create a 5G environment with USRPs, one as transmitter (PC1 with MATLAB) and other as receiver (PC2 with MATLAB) and they are connect with wired antennas.
I am generating the 5G NR waveform at transmitter side and sending it to receiver over connected antennas.
The waveform signal is sent for 10 seconds and receiver also receiving it for same duration with 1 second provided for transmission delay (through USRP’s USBs and wires). However, the synchronization is failing and the waveform I received are not the same which leads to inability to extract the sent waveform properties like PDCCH and PDSCH, data, etc. The datatypes of sent and received waveform are also different (Complex-double at transmitter, Complex-int16 at receiver).

I used the below link for designing the waveform for transmission.
https://in.mathworks.com/help/5g/ug/5g-nr-waveform-capture-and-analysis-using-software-defined-radio.html
Although there is no details about the receiver side in the above link.

If someone faced the similar issues, kindly suggest some help
If there exist any tutorial for this kind of work Please share?

Thanks 5g nr waveform synchronization using usrps (b210s) MATLAB Answers — New Questions

​