Future intelligent systems will consist of a massive number of battery-less sensors, where quick and accurate aggregation of sensor data will be of paramount importance. Over-the-air computation (AirComp) is a promising technology wherein sensors concurrently transmit their measurements over the wireless channel, and a reader receives the noisy version of a function of measurements due to the superposition property. A key challenge in AirComp is the accurate power alignment of individual transmissions, addressed previously by using conventional precoding methods. In this paper, we investigate a UAVenabled backscatter communication framework, wherein UAV acts both as a power emitter and reader. The mobility of the reader is leveraged to replace the complicated precoding at sensors, where UAV first collects sum channel gains in the first flyover, and then, use these to estimate the actual aggregated sensor data in the second flyover. Our results demonstrate improvements of up to 10 dB in MSE compared to that of a benchmark case where UAV is incognizant of sum channel gains.

中文翻译：

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反向散射传感器网络的移动辅助空中计算

未来的智能系统将由大量无电池传感器组成，其中传感器数据的快速准确聚合将至关重要。空中计算 (AirComp) 是一项很有前途的技术，其中传感器通过无线信道同时传输它们的测量值，并且由于叠加特性，读取器接收测量函数的噪声版本。AirComp 中的一个关键挑战是单个传输的准确功率对齐，以前使用传统的预编码方法解决了这个问题。在本文中，我们研究了一种支持无人机的反向散射通信框架，其中无人机既充当电源发射器又充当读取器。利用阅读器的移动性来代替传感器处复杂的预编码，其中无人机首先在第一次飞越中收集总信道增益，然后，使用这些来估计第二次天桥中的实际聚合传感器数据。我们的结果表明，与 UAV 不知道总信道增益的基准情况相比，MSE 的改进高达 10 dB。