How to Simulate AI-Driven Air Interface PHY for 6G Research

+ System Design for 6G Research

Optimizing 6G designs with real-time channel behavior analysis

Designing next-generation 6G systems requires reacting to real-time dynamic channel and network behavior changes. The software must simulate complex interactions in channel behavior, such as advanced modulation, user equipment mobility, and channel state information from ultra-massive multiple-input / multiple-output. Simulating this behavior with integrated artificial intelligence (AI) and machine learning (ML) techniques is crucial to modeling the complex higher-level modulation formats needed to reach data-rate-throughput goals.

Communication system engineers need design automation software that generates training datasets for neural network algorithms. They also need to incorporate trained models into advanced 5G and future 6G communication system simulations. Researchers must model the behavior of various components and systems, analyze performance metrics, and optimize designs for better functionality and cost-effectiveness. The software must generate datasets and train models.

6G PHY Research Simulation Solution

Building 6G systems requires adapting to real-time changes in channel and network behavior. The Keysight System Design W4812B bundle includes AI and ML example workspaces for 6G research. System Design demonstrates the link-level simulation of channel state information (CSI) feedback to enhance throughput. It saves CSI feedback information without compression for AI-based CSI feedback training. For AI channel estimation, System Design evaluates the performance of a New Radio (NR) receiver model using a pre-trained neural network model, comparing it with conventional minimum mean-squared error (MMSE) channel estimation. System Design generates training data for a neural network model to learn from MMSE channel estimation patterns, aiming to replace conventional MMSE channel estimation in the NR receive model.

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