Use a convolutional neural network (CNN) for modulation classification. You generate synthetic, channel-impaired waveforms. Using the generated waveforms as training data, you train a CNN for modulation classification. You then test the CNN with software-defined radio (SDR) hardware and over-the-air signals.

Construct an orthogonal frequency division modulation (OFDM) modulator/demodulator pair and to specify their pilot indices. The OFDM modulator System object enables you to specify pilot subcarrier indices consistent with the constraints described in comm.OFDMModulator.info. In this example, for OFDM transmission over a 3x2 channel, pilot indices are created for each of the three transmit antennas. Additionally, the pilot indices differ between odd and even symbols.

Compares Filter Bank Multi-Carrier (FBMC) with Orthogonal Frequency Division Multiplexing (OFDM) and highlights the merits of the candidate modulation scheme for Fifth Generation (5G) communication systems.

Compares Orthogonal Frequency Division Multiplexing (OFDM) with Filtered-OFDM (F-OFDM) and highlights the merits of the candidate modulation scheme for Fifth Generation (5G) communication systems.

Compares Universal Filtered Multi-Carrier (UFMC) with Orthogonal Frequency Division Multiplexing (OFDM) and highlights the merits of the candidate modulation scheme for Fifth Generation (5G) communication systems.

A straightforward way to perform passband modulation, by multiplying a modulated complex signal with a complex sine wave to perform frequency upconversion. In general, it is preferable to model a system at complex baseband. However, there are some circumstances where it is necessary to model the system at real passband. An example of this is when an adjacent band signal is processed with a nonlinearity, and causes interference in the desired band. This model also illustrates the effect of such interference.

Generate signals and channel impairments to train a neural network, called LLRNet, to estimate exact log likelihood ratios (LLR).

The BER performance improvement for QPSK modulation when using log-likelihood ratio (LLR) instead of hard-decision demodulation in a convolutionally coded communication link. With LLR demodulation, one can use the Viterbi decoder either in the unquantized decoding mode or the soft-decision decoding mode. Unquantized decoding, where the decoder inputs are real values, though better in terms of BER, is not practically viable. In the more practical soft-decision decoding, the demodulator output is quantized before being fed to the decoder. It is generally observed that this does not incur a significant cost in BER while significantly reducing the decoder complexity. We validate this experimentally through this example.

An orthogonal space-time block code (OSTBC) concatenated with trellis-coded modulation (TCM) for information transmission over a multiple-input multiple-output (MIMO) channel with 2 transmit antennas and 1 receive antenna. The example uses communications System objects™ to simulate this system.

An orthogonal space-time block code (OSTBC) concatenated with trellis-coded modulation (TCM) for information transmission over a multiple-input multiple-output (MIMO) channel with 2 transmit antennas and 1 receive antenna.

The improvement in BER performance when using log-likelihood ratio (LLR) instead of hard decision demodulation in a convolutionally coded communication link.