低轨卫星大容量物联接入仿真测试数据集
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针对资源受限下天基物联网接入容量难以满足物联接入需求问题,本课题引入非正交多址技术进行随机接入,支撑单波束不小10万物联网用户接入需求。为了解决完整模拟10万用户的数据量过大、短时间内难以得到仿真结果的问题,本课题通过模拟90个子载波180个用户的接入情况,来验证信噪比为-4.22dB时的接入成功率。单次传输的有效子载波数为90个,采用SCMA超载率200%的传输方案,可以承载180个用户发起接入。在过顶时间内,10万用户按照时分复用的方式分别发起接入。本数据集给出了低轨卫星大容量物联接入研究过程中产生的数据。该数据集中,用户接入成功性能数据通过模拟SCMA卫星通信系统,将接收端信号检测得到的结果与发送端的发送比特一一比对,用户比特完全检测正确则视为接入成功,计算得到不同信噪比条件下接入成功率以及成功接入用户数性能,将上述实验数据保存为文件。利用Matlab对原始数据进行处理,采用蒙特卡洛模拟法,计算多次仿真结果中对应接收信噪比条件下接入成功率和成功接入用户数的平均性能,并在前端展示出来。上述数据集于2024年12月在北京市石景山区静洋大厦520生成。
Aiming at the problem that the access capacity of space-based IoT under resource constraints fails to meet the IoT access requirements, this project introduces Non-Orthogonal Multiple Access (NOMA) technology for random access to support the access demand of no fewer than 100,000 IoT users per beam. To address the issues of excessive data volume when fully simulating 100,000 users and the difficulty of obtaining simulation results within a short period, this project simulates the access scenario of 180 users over 90 subcarriers to verify the access success rate at a signal-to-noise ratio (SNR) of -4.22 dB. The effective number of subcarriers for a single transmission is 90. Adopting a SCMA transmission scheme with an overload ratio of 200%, the system can accommodate access requests initiated by 180 users. During the satellite overhead pass duration, 100,000 users initiate access sequentially via Time Division Multiplexing (TDM). This dataset contains data generated during the research on high-capacity IoT access for Low Earth Orbit (LEO) satellites. In this dataset, user access success performance data is obtained by simulating the SCMA satellite communication system: comparing the results of received signal detection at the receiver one-to-one with the transmitted bits from the transmitter, where access is considered successful if all user bits are fully correctly detected. The access success rate and the number of successfully accessed users under different SNR conditions are calculated, and the above experimental data are saved as files. Raw data is processed using Matlab, and the Monte Carlo simulation method is employed to calculate the average performance of the access success rate and the number of successfully accessed users corresponding to the received SNR conditions across multiple simulation results, which are then displayed on the frontend. This dataset was generated in December 2024 at Room 520, Jingyang Building, Shijingshan District, Beijing, China.




