AI-Powered Data Synthesis for Advanced Simulation in 5G/6G mmWave Integrated Access and Backhaul Networks

Integrated Access and Backhaul (IAB) is a cost-effective and adaptable solution for the deployment of ultra-dense next-generation (5G and 6G) cellular networks to increase the likelihood of Line-of-Sight (LOS) coverage. This technology allows wireless backhaul connections to be established using the same technology and specifications as available in the access links. However, the absence of a physical testbed or a dataset that can be used for simulation in the millimeter wave (mmWave) band prevents researchers’ validation of the proposed algorithms in the IAB scenario. In this paper, we propose a novel data generator based on Generative Adversial Network (GAN), trained on a real dataset from a mobile network that operates in Europe, and maintains a significant market share that returns accurate traffic data for an IAB network. Furthermore, we integrate this data generator with the SeBaSi simulator (an IAB simulator based on Sionna) which permits to obtain accurate, data-consistent, realistic, and end-to-end IAB simulation results. The performance results indicate that the data generator successfully passes the Kolmogorov–Smirnov (KS) criterion, so it could operate as a verified data generator. Furthermore, we use the SeBaSi simulator, integrated with the data generator, to evaluate the performance of an IAB network in the London City scenario.