W8301B WirelessPro 5G Advanced Bundle

• Flexible, Modern Development Environment: Python-based simulation stack - easier integration, and broader developer adoption.
• Comprehensive Wireless Scenario Testing: Validate both traditional and AI/ML-driven innovations across diverse link and system-level simulations.
• Realistic Channel & Network Modeling: Employ high-fidelity models to accurately evaluate performance in varied wireless environments.
• Supports Evolving 3GPP Standards: Accelerate research impact for both established wireless technologies and emerging AI/ML applications (e.g., TR 38.843).
• Hybrid Simulation for Realistic End-to-End Modeling: Combines detailed PHY signal processing with event-driven system-level logic.

Supported 3GPP Specifications

• 3GPP TS 38.211 – NR; Physical channels and modulation
• 3GPP TS 38.212 – NR; Multiplexing and channel coding
• 3GPP TS 38.213 – NR; Physical layer procedures for control
• 3GPP TS 38.214 – NR; Physical layer procedures for data
• 3GPP TS 38.215 – NR; Physical layer measurements

Channel Modeling

• 3GPP TR 38.901 – Study on channel models for frequencies from 0.5 to 100 GHz
• 3GPP TR 38.811 – Study on New Radio support for non-terrestrial networks

AI/ML Use Cases

• 3GPP TR 38.843 – Study on Artificial Intelligence (AI)/Machine Learning (ML) for NR air interface

Implementation Notes

WirelessPro 2026 offers partial yet robust implementations of the above specifications, optimized for both link-level and system-level simulations. This focused approach enables users to explore the most impactful aspects of the standards, with ongoing development planned to expand feature coverage in future updates.

Key Features

Reference Tutorials

Step-by-step guides to help users quickly get started with

• 5G NR configuration and waveform generation
• Wireless channel modeling using TR 38.901 and TR 38.811
• Site-specific channel modeling using RaySim-generated ray tracing data
• Link-level and system-level simulation workflows

Simulation Applications

• Simulation workflows aligned with 3GPP for evaluating wireless system performance
• AI/ML use cases including synthetic data generation, model training, and inference within simulation loops

Developer APIs

• Modular Python and C++ APIs for 5G NR downlink/uplink signal processing
• Channel modeling APIs supporting TR 38.901 and TR 38.811
• AI/ML integration hooks for custom model deployment and evaluation