论文标题
摇摆和硬件障碍的基本原理 - 意识到地面频道模型
Fundamentals of Wobbling and Hardware Impairments-Aware Air-to-Ground Channel Model
论文作者
论文摘要
In this paper, we develop an impairments-aware air-to-ground unified channel model that incorporates the effect of both wobbling and hardware impairments, where the former is caused by random physical fluctuations of unmanned aerial vehicles (UAVs), and the latter by intrinsic radio frequency (RF) nonidealities at both the transmitter and receiver, such as phase noise, in-phase/quadrature (I/Q) imbalance, and power放大器（PA）非线性。无人机摇摆的影响是由两个随机过程（即规范的维也纳过程和更现实的正弦过程）建模的。另一方面，所有硬件障碍的总影响都被建模为两个乘法和加性失真噪声过程，这是一个良好接受的模型。为了普遍性，我们考虑了变形噪声的宽宽静止（WSS）和非组织过程。然后，我们严格地描述了无线通道的自相关函数（ACF），使用该函数对四个关键通道相关指标进行了全面分析：（i）功率延迟曲线（PDP），（ii）相干时间，（iii）相干带宽和（III）相干性带宽和（IV）功率频谱密度（iv）扭曲密度（psd）。此外，我们通过合理的无人机摆动和硬件障碍模型来评估这些指标，以获得有用的见解。我们很值得注意的是，我们证明，即使对于小无人机摇摆，连贯的时间也会在高频下严重降解，这在这些频率下使空对接地通道的估计非常困难。据我们所知，这是第一项表征无人机摇摆和硬件障碍在空中无线通道上的关节影响的作品。
In this paper, we develop an impairments-aware air-to-ground unified channel model that incorporates the effect of both wobbling and hardware impairments, where the former is caused by random physical fluctuations of unmanned aerial vehicles (UAVs), and the latter by intrinsic radio frequency (RF) nonidealities at both the transmitter and receiver, such as phase noise, in-phase/quadrature (I/Q) imbalance, and power amplifier (PA) nonlinearity. The impact of UAV wobbling is modeled by two stochastic processes, i.e., the canonical Wiener process and the more realistic sinusoidal process. On the other hand, the aggregate impact of all hardware impairments is modeled as two multiplicative and additive distortion noise processes, which is a well-accepted model. For the sake of generality, we consider both wide-sense stationary (WSS) and nonstationary processes for the distortion noises. We then rigorously characterize the autocorrelation function (ACF) of the wireless channel, using which we provide a comprehensive analysis of four key channel-related metrics: (i) power delay profile (PDP), (ii) coherence time, (iii) coherence bandwidth, and (iv) power spectral density (PSD) of the distortion-plus-noise process. Furthermore, we evaluate these metrics with reasonable UAV wobbling and hardware impairment models to obtain useful insights. Quite noticeably, we demonstrate that even for small UAV wobbling, the coherence time severely degrades at high frequencies, which renders air-to-ground channel estimation very difficult at these frequencies. To the best of our understanding, this is the first work that characterizes the joint impact of UAV wobbling and hardware impairments on the air-to-ground wireless channel.
