论文标题
用于反向散射传感器网络的移动性辅助无线计算
Mobility-assisted Over-the-Air Computation for Backscatter Sensor Networks
论文作者
论文摘要
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和准确的聚合至关重要。无线计算（AIRCOMP）是一项有前途的技术，传感器同时通过无线通道传输其测量值，并且读取器由于叠加属性而获得了测量功能的嘈杂版本。 AirComp中的一个关键挑战是单个传输的准确功率对准，这是通过使用常规的预码方法来解决的。在本文中，我们研究了一个无人机的反向散射通信框架，其中无人机既是电源发射器又是读者。利用读取器的移动性来代替传感器上复杂的预编码，在该传感器上，无人机首先收集第一个天桥中的总和通道的增长，然后使用这些收集这些量，然后使用它们来估算第二个天桥中实际的聚集传感器数据。我们的结果表明，与无人机对总信道增益的基准案例相比，MSE中最多10 dB的改善。
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.
