Interchannel Interference and Mitigation in Distributed MIMO RF Sensing

In this paper, we analyze and mitigate the cross-channel interference, which is found in multiple-input multiple-output (MIMO) radio frequency (RF) sensing systems. For a millimeter wave (mm-Wave) MIMO system, we present a geometrical three-dimensional (3D) channel model to simulate the time-variant (TV) trajectories of a moving scatterer. We collected RF data using a state-of-the-art radar known as Ancortek SDR-KIT 2400T2R4, which is a frequency-modulated continuous wave (FMCW) MIMO radar system operating in the K-band. The Ancortek radar is currently the only K-band MIMO commercial radar system that offers customized antenna configurations. It is shown that this radar system encounters the problem of interference between the various subchannels. We propose an optimal approach to mitigate the problem of cross-channel interference by inducing a propagation delay in one of the channels and apply range gating. The measurement results prove the effectiveness of the proposed approach by demonstrating a complete elimination of the interference problem. The application of the proposed solution on Ancortek’s SDR-KIT 2400T2R4 allows resolving all subchannel links in a distributed MIMO configuration. This allows using MIMO RF sensing techniques to track a moving scatterer (target) regardless of its direction of motion.

Metamaterial-Integrated High-Gain Rectenna for RF Sensing and Energy Harvesting Applications

This paper presents a metamaterial (MTM)-integrated high-gain rectenna for RF sensing and energy harvesting applications that operates at 2.45 GHz, an industry, science, medicine (ISM) band. The novel MTM superstrate approach with a three-layered integration method is firstly introduced for rectenna applications. The integrated rectenna consists of three layers, where the first layer is an MTM superstrate consisting of four-by-four MTM unit cell arrays, the second layer a patch antenna, and the third layer a rectifier circuit. By integrating the MTM superstrate on top of the patch antenna, the gain of the antenna is enhanced, owing to its beam focusing capability of the MTM superstrate. This induces the increase of the captured RF power at the rectifier input, resulting in high-output DC power and high entire end-to-end efficiency. A parametric analysis is performed in order to optimize the near-zero property of the MTM unit cell. In addition, the effects of the number of MTM unit cells on the performance of the integrated rectenna are studied. A prototype MTM-integrated rectenna, which is designed on an RO5880 substrate, is fabricated and characterized. The measured gain of the MTM-integrated rectenna is 11.87 dB. It shows a gain improvement of 6.12 dB compared to a counterpart patch antenna without an MTM superstrate and a maximum RF–DC conversion efficiency of 78.9% at an input RF power of 9 dBm. This results in the improvement of the RF–DC efficiency from 39.2% to 78.9% and the increase of the output DC power from 0.7 mW to 6.27 mW (a factor of 8.96 improvements). The demonstrated MTM-integrated rectenna has shown outstanding performance compared to other previously reported work. We emphasize that the demonstrated MTM-integrated rectenna has a low design complexity compared with other work, as the MTM superstrate layer is integrated on top of the simple patch antenna and rectifier circuit. In addition, the number of MTM units can be determined depending on applications. It is highly envisioned that the demonstrated MTM-integrated rectenna will provide new possibilities for practical energy harvesting applications with improved antenna gain and efficiency in various IoT environments.

Towards the Optimal RF Sensing Strategy for IoT Applications: An Ice Sensing Case Study

Remote ice detection has recently emerged as an application of Radio Frequency (RF) sensors. While RF sensing is a feasible approach used for detecting various stimuli, the optimal system architecture and design strategy for RF-based sensing in future Internet of Things (IoT) systems remains unclear. In this paper, we propose a systematic methodology for designing an RF-based sensing system, applicable to a plethora of IoT applications. The proposed methodology is used to design printable antennas as highly-sensitive sensors for detecting and measuring the thickness of ice, demonstrating best-in-class sensory response. Antenna design is investigated systematically for wireless interrogation in the 2.4 GHz band, to support a variety of IoT protocols. Following the proposed methodology, the antenna's realized gain was identified as the optimum parameter-under-test. The developed loop antenna sensor exhibits a high linearity, resilience to interference, and applicability to different real-world deployment environments, demonstrated through over 90% average ice thickness measurement accuracy and at least 5 dB real-time sensitivity to ice deposition.

Towards the Optimal RF Sensing Strategy for IoT Applications: An Ice Sensing Case Study

Remote ice detection has recently emerged as an application of Radio Frequency (RF) sensors. While RF sensing is a feasible approach used for detecting various stimuli, the optimal system architecture and design strategy for RF-based sensing in future Internet of Things (IoT) systems remains unclear. In this paper, we propose a systematic methodology for designing an RF-based sensing system, applicable to a plethora of IoT applications. The proposed methodology is used to design printable antennas as highly-sensitive sensors for detecting and measuring the thickness of ice, demonstrating best-in-class sensory response. Antenna design is investigated systematically for wireless interrogation in the 2.4 GHz band, to support a variety of IoT protocols. Following the proposed methodology, the antenna's realized gain was identified as the optimum parameter-under-test. The developed loop antenna sensor exhibits a high linearity, resilience to interference, and applicability to different real-world deployment environments, demonstrated through over 90% average ice thickness measurement accuracy and at least 5 dB real-time sensitivity to ice deposition.

RF Sensing Based Breathing Patterns Detection Leveraging USRP Devices

Non-contact detection of the breathing patterns in a remote and unobtrusive manner has significant value to healthcare applications and disease diagnosis, such as in COVID-19 infection prediction. During the epidemic prevention and control period of COVID-19, non-contact approaches have great significance because they minimize the physical burden on the patient and have the least requirement of active cooperation of the infected individual. During the pandemic, these non-contact approaches also reduce environmental constraints and remove the need for extra preparations. According to the latest medical research, the breathing pattern of a person infected with COVID-19 is unlike the breathing associated with flu and the common cold. One noteworthy symptom that occurs in COVID-19 is an abnormal breathing rate; individuals infected with COVID-19 have more rapid breathing. This requires continuous real-time detection of breathing patterns, which can be helpful in the prediction, diagnosis, and screening for people infected with COVID-19. In this research work, software-defined radio (SDR)-based radio frequency (RF) sensing techniques and machine learning (ML) algorithms are exploited to develop a platform for the detection and classification of different abnormal breathing patterns. ML algorithms are used for classification purposes, and their performance is evaluated on the basis of accuracy, prediction speed, and training time. The results show that this platform can detect and classify breathing patterns with a maximum accuracy of 99.4% through a complex tree algorithm. This research has a significant clinical impact because this platform can also be deployed for practical use in pandemic and non-pandemic situations.