论文标题
利用基于张量的贝叶斯学习在Leo卫星物联网中进行大规模无赠款随机访问
Exploiting Tensor-based Bayesian Learning for Massive Grant-Free Random Access in LEO Satellite Internet of Things
论文作者
论文摘要
随着物联网（IoT）的快速发展，低地球轨道（LEO）卫星物联网有望提供低功率，大量连通性和广泛的覆盖物IoT应用。在这种情况下，本文为LEO卫星IoT提供了大量的无赠款随机访问（GF-RA）方案。该方案不需要更改收发器，而是将接收的信号转换为张量分解形式。通过利用张量结构的特性，设计了用于关节活动设备检测的贝叶斯学习算法，并设计了大量GF-RA期间的通道估计。理论分析表明，所提出的算法具有快速的收敛性和低复杂性。最后，广泛的仿真结果证实了其在LEO卫星IoT中基线算法上的频道估计的误差概率方面的表现更好。尤其是，发现所提出的算法需要简短的序列序列，并支持低功率的大规模连通性，这对Leo卫星IoT有吸引力。
With the rapid development of Internet of Things (IoT), low earth orbit (LEO) satellite IoT is expected to provide low power, massive connectivity and wide coverage IoT applications. In this context, this paper provides a massive grant-free random access (GF-RA) scheme for LEO satellite IoT. This scheme does not need to change the transceiver, but transforms the received signal to a tensor decomposition form. By exploiting the characteristics of the tensor structure, a Bayesian learning algorithm for joint active device detection and channel estimation during massive GF-RA is designed. Theoretical analysis shows that the proposed algorithm has fast convergence and low complexity. Finally, extensive simulation results confirm its better performance in terms of error probability for active device detection and normalized mean square error for channel estimation over baseline algorithms in LEO satellite IoT. Especially, it is found that the proposed algorithm requires short preamble sequences and support massive connectivity with a low power, which is appealing to LEO satellite IoT.
