Simulate, analyze, and test 5G communications systems
5G Toolbox™ provides standard-compliant functions and reference examples for the modeling, simulation, and verification of 5G New Radio (NR) communications systems. The toolbox supports link-level simulation, golden reference verification, conformance testing, and test waveform generation.
With the toolbox you can configure, simulate, measure, and analyze end-to-end 5G NR communications links. You can modify or customize the toolbox functions and use them as reference models for implementing 5G systems and devices.
The toolbox provides functions and reference examples to help you characterize uplink and downlink baseband specifications and simulate the effects of RF designs and interference sources on system performance. You can generate waveforms and customize test benches, either programmatically or interactively using the Wireless Waveform Generator app. With these waveforms, you can verify that your designs, prototypes, and implementations comply with the 3GPP 5G NR specifications.
NR Subcarrier and Numerology
Generate 5G NR uplink and downlink carrier waveforms based on flexible NR subcarrier spacings and frame numerologies, including carrier bandwidth parts (CBP).
Wireless Waveform Generation App
Generate 5G NR test models (NR-TM) and NR uplink and downlink fixed reference channel (FRC) waveforms. Add RF impairments such as AWGN , phase offset, frequency offset, DC offset, IQ imbalance, and memoryless cubic nonlinearity. Visualize in constellation diagram, spectrum analyzer, OFDM grid, and time scope plots.
Propagation Channel Models
Perform block error rate (BLER) simulations with TR 38.901 propagation channel models. Characterize and simulate cluster delay line (CDL) and tapped delay line (TDL) channel models.
Characterize 5G NR link-level performance and measure physical downlink shared channel (PDSCH) and physical uplink shared channel (PUSCH) throughputs.
Perform channel estimation and equalization on received 5G NR signals.
RF Modeling and Testing
Evaluate the performance of 5G RF transmitters. Model and test NR RF receivers in the presence of interference.
Characterize RF link performance. Measure adjacent channel leakage ratio (ACLR) and error vector magnitude (EVM) metrics.
Downlink and Uplink Channels
Create downlink and uplink physical channels including shared (PDSCH and PUSCH), control (PDCCH and PUCCH), random access (PRACH), and broadcast (PBCH) channels.
Downlink and Uplink Signals
Generate synchronization signals (PSS, SSS) and channel state information (CSI-RS), demodulation (DM-RS), phase-tracking (PT-RS), and sounding (SRS) reference signals.
Simulate uplink and downlink (UCI, DCI) control information and control resource sets (CORESETs).
Use low-density parity-check (LDPC) coding to encode and decode transport channels, including uplink and downlink shared channels (UL-SCH and DL-SCH).
Construct a waveform containing a synchronization signal (SS) burst, pass waveforms through a fading channel, and blindly synchronize to receive the waveforms.
Selection Procedures and MIB Decoding
Decode the Master Information Block (MIB). Model the physical random access channel (PRACH) missed detection conformance test.
Open MATLAB Code
Use transmitter, channel model, and receiver operations that are expressed as open and customizable MATLAB® code.
C and C++ Code Generation
Generate portable C or C++ source code, standalone executables, or standalone applications from your MATLAB applications that use 5G Toolbox.
5G support in Wireless Waveform Generator App
Generate NR-TM, and uplink and downlink FRC waveforms using the Wireless Waveform Generator app
Support for DM-RS and PT-RS signals
Model demodulation reference signals (DM-RS), and phase-tracking reference signals (PT-RS) for channel estimation and phase tracking
Support for SRS signals
Model sounding reference signals (SRS) for uplink channel sounding
Support for PRACH physical channels
Model physical random access channel (PRACH) used in initial system access
Deep learning data synthesis for 5G channel estimation
Generate deep learning training data for convolutional neural networks (CNN) used in 5G channel estimation