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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™).

Get Started

Learn the basics of Communications Toolbox

PHY Components

Physical layer features including waveform generation, source coding, error control coding, modulation, MIMO, space-time coding, filtering, equalization, and synchronization

RF Component Modeling

Behavioral RF radio modeling and impairment correction

Propagation and Channel Models

Site and terrain visualization, propagation model specification (including Longley-Rice), signal strength, signal coverage maps, and static and fading channel models

Link-Level Simulation

Link-level communications systems simulation and analysis examples

System-Level Simulation

DLL, MAC sublayer, LLC sublayer, and multinode communications

Standards-Compliant Systems

System models compliant with various standards

Test and Measurement

Waveform generation, file IO formats, visualization, and performance analysis

AI for Wireless

Machine learning, deep learning, and reinforcement learning in wireless communications systems

Code Generation and Deployment

Generate standalone applications for desktop computers and embedded targets

Supported Hardware – Software-Defined Radio

Support for third-party software-defined radio hardware, such as RTL-SDR, ADALM-PLUTO, and USRP™ radios