Explore the relationship of signals in the time and frequency domain. Adjust time parameters and see how the spectrum changes! Take a look at selected Fourier transform pairs of waveforms and pulses.
Control the settings of an FFT spectrum analyzer and get familiar with measurements in the frequency domain. Sampling time and capture time determine the spectrum's highest available frequency and resolution bandwidth.
Calculate the FFT of signals. The FFT takes a time-discrete signal and computes the spectrum. Examples of time and frequency signals are shown, DC component, Dirac pulse, cosine, pulse, complex rotating phasor.
Calculate the Inverse Fourier Transform (IFFT). Enter a frequency-based signal and transform it to a time-based Signal.
How does music or speech sound in different signal qualities? Recognize that it's the signal-to-noise ratio (SNR) that matters. Observe the visual and auditive impact of added noise.
Analyze audio signals in the time and frequency domain. Play any music wave file, microphone line-in or signal generator waveform. This audio software visualizes the signal in an oscilloscope and in a spectrum analyzer.
Measure amplitude and phase of the output sine wave signal using an oscilloscope. Compare the results with the transfer function representing the linear time-invariant (LTI) system.
In this scenario, a pulse is sent and shall be detected at the receiver. It might represent a symbol for digital transmission or a radar pulse. The received pulse might be almost hidden in noise...
Frequency modulation signal spectra show sidebands for sinewave modulation. In this experiment we take a look how they change with the modulating signal amplitude.
By the invention of amplitude modulation radio broadcasting was made possible. Analyze an AM-signal in time and frequency domain by changing the parameters of the modulation.
Explore hands-on how a QPSK signal is generated and demodulated. Check the transmission spectrum for different pulse shapers. Vary further parameters like carrier frequency etc.
Explore the bit error rate for QPSK. Adjust Eb/N0 and measure the corresponding BER. Compare it to the analytical bit error probability. Vary the pulse shape - does it have effect on the BER?
Analyse the bit error rate of M-QAM over an AWGN channel. Adjust Eb/N0 and the constellation size. Compare the measured BER to the analytical bit error probability.
Measure the bit error rate for QPSK in high accuracy. Use this experiment to hands-on verify the analytical bit error probability as a function of Eb/N0. This app is optimized for high simulation speed.
Understand OFDM and process each step using example values. A pilot sequence initializes the equalizer. Transmit an OFDM symbol and detect ISI-free.
Explore how OFDM implements an ISI-free transmission and verify the required guard interval length for a multipath channel. Modify the delay spread of a fading channel and check the received signal points for ISI.
Verify the theoretical BER of IEEE 802.11ac OFDM setups with simulation results! Why does OFDM show an increased BER compared to single carrier systems in AWGN channel?
Why do OFDM systems show a high peak-to-average power ratio? How does the PAPR depend on the number of subcarriers? Has the underlying M-QAM constellation size any impact?
See how even small movements strongly impact frequency-selective channel characteristics. Fast fading describes mobile radio channels.
In channels where the receiver moves the signal frequency is shifted depending on the velocity. In practice the Doppler effect occurs in wireless communications.
This simulation implements a Wi-Fi transmission (80 MHz, VHT-MCS 6, 400 ns guard interval). Analyze the signals, e.g. the transmit spectrum.
Analyze the bit error rate (BER) over an AWGN channel for QPSK and QAM. A calculator and a table help you finding out the values of Q(x) and erfc(x) depending on x.
Calculate the M-ary QAM bit error probability in AWGN depending on energy per bit and the constellation size M.
This little tool helps you calculating logarithmic decibels into a linear power value and vice versa.
The power of the FFT output signal differs from the input signal power due to an asymmetry in the FFT / IFFT definitions. This FFT variant keeps input and output signal powers equal.
How does music or speech sound for different quantization resolutions? Explore how the quantization error degenerates the audio signal and each bit per sample improves the Signal-to-quantization-noise ratio by 6 dB.
Explore how the use of compressors and expanders (companding) improves the signal quality compared to uniform quantization - especially for quiet voice. Logarithmic Laws - piecewise linear approximation.
Start with your first steps to use the labAlive environment. Adjust some scope settings and see how the scope displays the signal.
Start with your first steps to use the labAlive environment. Adjust some spectrum analyzer settings and see how the spectrum is displayed.
This demonstration shows example signals interfered by noise with variable bandwidth. The signal-to-noise ratio (SNR) is a measure for the signal quality. Visually evaluate the signal quality and noise bandwidth!
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