5G Toolbox


5G Toolbox

Simulate, analyze, and test 5G communications systems

Waveform Generation

Generate standard-compliant 5G NR waveforms. Configure and generate custom waveforms and NR test models and fixed reference channels. Interactively create, add RF impairments, visualize, and export waveforms with the Wireless Waveform Generator app.

Link-Level Simulation

Simulate 5G NR end-to-end wireless communication links. Incorporate transmitter, channel modeling, and receiver operations. Apply cluster delay line (CDL) and tapped delay line (TDL) channel models. Analyze link performance by computing block error rate and throughput metrics.

Test and Measurement

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 metrics.

MIMO and Beamforming

Use channel state information (CSI) feedback to adjust transmission parameters, including code rate, modulation, number of layers, and MIMO precoding matrix. Estimate uplink channels using sounding reference signals exploiting channel reciprocity in a time division duplex (TDD) scenario. Use CSI reference signal and select the optimal transmit beam based on reference signal received power measurements.

Propagation and Channel Models

Generate TDL and CDL channel models. Configure the CDL channel model with the result of ray tracing analyses. Explore channel information, including antenna element, element pattern, number of rays, angles, delays, attenuations, and cluster paths. 

Cell Search Procedures

Perform cell search and selection procedures to extract initial system information, including Master Information Block and First System Information Block. Model the physical random-access channel. Use synchronization signal blocks to perform beam management procedures consisting of beam sweeping, measurement, determination, reporting, and recovery steps.

System-Level Simulation

Simulate frequency-time resource sharing among multiple UEs in a 5G NR network. Evaluate the performance of medium access control scheduling strategies in both TDD and frequency division duplexing modes.

AI for Wireless

Apply AI for wireless techniques to optimize 5G NR operations. Use an autoencoder neural network to compress downlink CSI. Train a deep Q-network (DQN) reinforcement learning agent for beam selection. Train a convolutional neural network for channel estimation.

“We started with a working example from MathWorks that included 5G new radio cell search and master information block recovery and modified the design to match customer requirements. This helped simplify our work and saved us a lot of time.”

Vinoth Thuruvas, Capgemini