论文标题
通道相关性对基于子空间的活动检测的影响
Impact of Channel Correlation on Subspace-Based Activity Detection in Grant-Free NOMA
论文作者
论文摘要
在本文中，我们考虑了无授予代码域非正交多访问（NOMA）中的活动检测问题。我们专注于在设置下通过子空间方法执行活动检测，其中数据和飞行员扩展签名的长度不同，并考虑与现有移动网络相似的现实帧结构。我们研究了通道相关性对活动检测性能的影响；首先，我们考虑了该通道在时间和频率上表现出较高相关性并显示其如何严重恶化性能的情况。为了解决这个问题，我们建议将特定于用户的掩蔽序列叠加在飞行员签名之上。其次，我们考虑了另一个极端，频道具有很高的选择性，并表明它也会对性能产生负面影响。我们调查了可能的飞行员的重新分配策略，以帮助减少其影响。
In this paper, we consider the problem of activity detection in grant-free code-domain non-orthogonal multiple access (NOMA). We focus on performing activity detection via subspace methods under a setup where the data and pilot spreading signatures are of different lengths, and consider a realistic frame-structure similar to existing mobile networks. We investigate the impact of channel correlation on the activity detection performance; first, we consider the case where the channel exhibits high correlation in time and frequency and show how it can heavily deteriorate the performance. To tackle that, we propose to apply user-specific masking sequences overlaid on top of the pilot signatures. Second, we consider the other extreme with the channel being highly selective, and show that it can also negatively impact the performance. We investigate possible pilots' reallocation strategies that can help reduce its impact.
