Integration of unmanned aerial vehicles (UAVs) for surveillance or monitoring applications into fifth generation (5G) New Radio (NR) cellular networks is an intriguing problem that has recently tackled a lot of interest in both academia and industry. For an efficient spectrum usage, we consider a recently-proposed sky-ground nonorthogonal multiple access (NOMA) scheme, where a cellular-connected UAV acting as aerial user (AU) and a static terrestrial user (TU) are paired to simultaneously transmit their uplink signals to a base station (BS) in the same time-frequency resource blocks. In such a case, due to the highly dynamic nature of the UAV, the signal transmitted by the AU experiences both time dispersion due to multipath propagation effects and frequency dispersion caused by Doppler shifts. On the other hand, for a static ground network, frequency dispersion of the signal transmitted by the TU is negligible and only multipath effects have to be taken into account. To decode the superposed signals at the BS through successive interference cancellation, accurate estimates of both the AU and TU channels are needed. In this paper, we propose channel estimation procedures that suitably exploit the different circular/noncircular modulation formats (modulation diversity) and the different almost-cyclostationarity features (Doppler diversity) of the AU and TU by means of widely-linear time-varying processing. Our estimation approach is semi-blind since Doppler shifts and time delays of the AU are estimated based on the received data only, whereas the remaining relevant parameters of the AU and TU channels are acquired relying also on the available training symbols, which are transmitted by the AU and TU in a nonorthogonal manner.

翻译：将无人机（UAV）整合至第五代（5G）新空口（NR）蜂窝网络以执行监视或监控应用，是一个近年来在学术界与工业界均引发广泛关注的重要课题。为实现高效的频谱利用，我们研究了一种新近提出的空地非正交多址（NOMA）方案，其中作为空中用户（AU）的蜂窝连接无人机与一个静态地面用户（TU）配对，在同一时频资源块中同时向基站（BS）发送上行链路信号。在此场景下，由于无人机的高度动态特性，AU发送的信号既经历由多径传播效应引起的时间弥散，也遭受由多普勒频移导致的频率弥散。另一方面，对于静态地面网络，TU发送信号的频率弥散可忽略不计，仅需考虑多径效应。为了在基站端通过连续干扰消除解码叠加信号，需要准确估计AU和TU的信道状态。本文提出了一种信道估计方法，该方法通过广义线性时变处理，恰当地利用了AU与TU之间不同的圆形/非圆形调制格式（调制分集）以及不同的近似循环平稳特性（多普勒分集）。我们的估计方法是半盲的，因为AU的多普勒频移和时延仅基于接收数据进行估计，而AU与TU信道其余相关参数的获取则同时依赖于可用的训练符号，这些符号由AU和TU以非正交方式发送。