Communications Toolbox

Design and simulate the physical layer of communications systems

Communications Toolbox™ provides algorithms and apps for the analysis, design, end-to-end simulation, and verification of communications systems. Toolbox algorithms including channel coding, modulation, MIMO, and OFDM enable you to compose and simulate a physical layer model of your standard-based or custom-designed wireless communications system.

The toolbox provides a waveform generator app, constellation and eye diagrams, bit-error-rate, and other analysis tools and scopes for validating your designs. These tools enable you to generate and analyze signals, visualize channel characteristics, and obtain performance metrics such as error vector magnitude (EVM). The toolbox includes SISO and MIMO statistical and spatial channel models. Channel profile options include Rayleigh, Rician, and WINNER II models. It also includes RF impairments, including RF nonlinearity and carrier offset and compensation algorithms, including carrier and symbol timing synchronizers. These algorithms enable you to realistically model link-level specifications and compensate for the effects of channel degradations.

Using Communications Toolbox with RF instruments or hardware support packages, you can connect your transmitter and receiver models to radio devices and verify your designs with over-the-air testing.

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.

RF Propagation, Ray Tracing and Wireless Scenario Modeling in MATLAB (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 (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