Communications Toolbox

Design, simulate, and analyze the physical layer of communications systems

Communications Toolbox provides algorithms and apps for the design, end-to-end simulation, analysis, and verification of communications systems. The toolbox includes a graphically-based app that lets you generate custom- or standard-based waveforms. You can create test vectors to verify receiver performance or to create datasets for artificial intelligence (AI) applications by adding RF impairments to waveforms. The toolbox lets you model propagation channels statistically or with ray-tracing solutions that include terrain and buildings. You can compensate for the effects of channel degradations and use SDRs to verify your designs with over-the-air (OTA) testing.

Communications Toolbox facilitates modeling communications links from antenna to RF chain to bit processing (with Antenna Toolbox and RF Blockset). You can accelerate BER simulations using the cloud or your local cluster (with Parallel Computing Toolbox). The toolbox helps you solve communications problems using AI techniques (with Deep Learning Toolbox).

What Is Communications Toolbox?

Waveform Generation

Generate a variety of customizable or standard-based physical layer waveforms. Use the Wireless Waveform Generator app to create test signals. Use waveforms as golden references for your designs.

Documentation | Examples

RF Propagation and Channel Modeling

Perform ray tracing analyses on indoor and outdoor scenarios. Characterize effects of noise and fading. Account for path loss due to free space and atmospheric effects.

5G Communications Link Analysis with Ray Tracing Video (50:00)

Documentation | Examples

Satellite link with RF front end impairments and digital predistortion.

End-to-End Simulation

Simulate link-level models of communications systems. Explore what-if scenarios and evaluate system parameter tradeoffs. Obtain expected measures of performance such as BER, PER, BLER, and throughput.

Documentation | Examples

Augmented real-valued time-delay neural network achieves better ACLR than a memory polynomial DPD.

AI for Wireless

Use AI for wireless challenges such as modulation scheme identification, RF fingerprinting, spectrum monitoring, and signal classification. Create synthetic signals with RF impairments to train AI models. Capture over-the-air signals with SDR hardware for training or testing purposes.

Deep Learning for Wireless Communications Video (42:29)

Documentation | Examples

RF, Antenna, and MIMO

Model RF front end effects, antenna designs, and digital baseband systems in one environment. Include high-fidelity models of RF components, antennas, and phased array systems. Boost system performance with MIMO and massive MIMO multiple antenna techniques. Characterize MIMO receivers and channels.

Documentation | Examples

Measure peak and RMS error vector magnitude and modulation error ratio.

Test and Measurement

Compute standard measurements, like EVM, ACPR, ACLR, MER, and CCDF, to quantitatively characterize system performance. Transmit over the air with hardware signal generators.

Documentation | Examples