A Low-cost and In-field Antenna Characterizing Method Based on Statistics Measurement

With the development of the Internet of Things (IoT) technology, the antenna becomes increasingly integrated and miniaturized. Vector Network Analyzer (VNA) is a standard instrument for characterizing antennas. However, IoT devices are small and scattered installed, which makes it costly to carry out in-field characterizing on IoT devices. Integrating an antenna characterizing system into IoT devices can release the difficulty significantly, but characterizing antennas usually requires a high-performance Analog to Digital Conversion system, which is expensive and power-consuming. However, the antennas are not required to be tested frequently, which means this is not a real-time application. Therefore, this paper proposes a method that can realize time-domain reflectometer based VNA with just a comparator or differential receiver. In this system, impulses are sent into the antenna, and the frequency response is captured by analyzing the time-domain reflection. Analog to Probability Conversion (APC), Probability Density Modulation (PDM), and Equivalent time sampling (ETS) concepts are used to reduce the real-time performance requirements of the ADC system, so that the cost and power consumption can be tolerable on a tiny IoT device. With this technology, the antennas could be characterized in-field and remotely, making the maintenance easier. This technology is implemented with Xilinx ZYNQ Ultrascale+ series FPGA. Two antennas are tested, and the experimental results show that the system can successfully measure the radio-frequency. The sampling rate is set to 56Ghz, and a micro-volt level voltage resolution is achieved. Since the essence of technology is a trade-off between time and performance, the sampling rate and resolution can be further increased theoretically according to specific applications.