A low-cost and in-field antenna characterizing method based on statistics measurement

Document Type

Conference Proceeding

Date of Original Version



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 highperformance 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.

Publication Title

2020 Antenna Measurement Techniques Association Symposium, AMTA 2020

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