Sionna Simulation Dataset

该数据集包含了来自Sionna平台的模拟结果，模拟了一个500米道路场景，其中央基站服务于在时相关阻塞条件下5个用户设备。数据集采用了5个组件状态用于深度强化学习（DRL）代理，并融入了瑞利衰落、路径损耗以及多种性能指标，如延迟、吞吐量、能量消耗、准确性和信噪比。当前规模为单小区设置，服务五个用户设备，未来计划扩展到多小区。该数据集的任务是优化6G网络中的波束切换。

This dataset contains simulation results from the Sionna platform, which models a 500-meter road scenario where a central base station serves five user equipments (UEs) under time-correlated blocking conditions. The dataset adopts 5 component states for deep reinforcement learning (DRL) agents, and incorporates Rayleigh fading, path loss, and multiple performance metrics including latency, throughput, energy consumption, accuracy, and signal-to-noise ratio (SNR). Currently, it has a single-cell setup serving five UEs, with plans to expand to multi-cell scenarios in the future. The task of this dataset is to optimize beam switching in 6G networks.