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. Monte Carlo numerical results demonstrate that the proposed channel estimation algorithms can satisfactorily acquire all the relevant parameters in different operative conditions.

翻译：将无人机（UAV）集成到第五代（5G）新无线电（NR）蜂窝网络中用于监视或监测应用，是近期在学术界和工业界引起广泛关注的一个有趣问题。为高效利用频谱，我们采用了一种近期提出的空地非正交多址接入（NOMA）方案，其中作为空中用户（AU）的蜂窝连接无人机与静态地面用户（TU）配对，在相同时间-频率资源块上同时向基站（BS）发送上行信号。在此场景中，由于无人机高度动态的特性，AU发射的信号同时经历多径传播引起的时间色散和多普勒频移造成的频率色散。而静态地面网络中，TU发射信号的频率色散可忽略不计，仅需考虑多径效应。为通过连续干扰消除在BS处解码叠加信号，需要精确估计AU和TU的信道参数。本文提出了一种信道估计方法，通过采用宽线性时变处理，合理利用AU和TU不同的循环/非循环调制格式（调制分集）以及不同的近似循环平稳特征（多普勒分集）。我们的估计方法属于半盲估计，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和TU信道的其余相关参数则同时利用可用训练符号获取。蒙特卡洛数值结果表明，所提信道估计算法能在不同工作条件下有效获取所有相关参数。