No abstract

In this paper, we propose an energy-efficient radar beampattern design framework for Millimeter Wave (mmWave) massive multi-input multi-output (mMIMO) systems, equipped with a hybrid analog-digital (HAD) beamforming structure. Aiming to reduce the power consumption and hardware cost of the mMIMO system, we employ a learning approach to synthesize the probing beampattern based on a small number of RF chains and antennas. By leveraging a combination of softmax neural networks, the proposed solution is able to achieve a desirable beampattern with high accuracy while incurring low cost.

A novel dual-function radar communication (DFRC) system is proposed, that achieves high communication rate, and can flexibly trade-off rate for improved sensing performance. The proposed system is a monostatic multiple-input multiple-output (MIMO) radar and transmits wideband, precoded, orthogonal frequency division multiplexing (OFDM) waveforms from its antennas. The system subcarriers are divided into two groups, i.e., shared and private. On a shared subcarrier, all antennas can transmit simultaneously, while on a private one only one antenna can transmit at a time. A novel target estimation approach with low complexity is proposed to overcome the coupling of transmitted symbols and radar target parameters in the target echoes, which arises due to the shared use of subcarriers by the transmit antennas. The proposed method first operates on all (shared and private) subcarriers to obtain coarse angle estimates, and then fine-tunes those estimates based on the signal received on the private subcarriers. The resolution of the coarse angle estimates is limited by the physical receive array, while the finetuning is enabled by effectively constructing a virtual array that has larger aperture than the receive array. The precoding matrix is optimally designed to optimize a weighted combination of the beampattern error with respect to a desirable beampattern, and the signal-to-noise ratio at the communication receiver.

Vital sign monitoring plays a critical role in tracking the physiological state of people and enabling various healthrelated applications (e.g., recommending a change of lifestyle, examining the risk of diseases). Traditional approaches rely on hospitalization or body-attached instruments, which are costly and intrusive. However, in recent years there is an emergence of contactless vital sign monitoring techniques that rely on radio frequency signals. Early studies with continuous wave radars/WiFi devices have shown good success in detecting the vital signs of a single individual, while simultaneous monitoring of the vital signs of multiple, closely spaced subjects remains a challenge. In this paper, using an off-the-shelf Texas Instrument automotive FMCW radar, we design and implement a time-division multiplexing (TDM) phased-MIMO radar sensing system that allows high-precision vital sign monitoring of multiple subjects. The proposed sensing system can steer the beam towards the desired directions with a micro-second delay.The steerable beam enables capturing the vital signs of multiple individuals at the same radial distance to the radar. The proposed system enables the formation of a virtual array with aperture longer than that of the physical array. A Capon beamformer is used at the receiver side to combine the data collected from different transmit and receive antenna pairs corresponding to the virtual array. As all those pairs provide independent information about the targets, their combination significantly boosts the receiver signal-to-noise ratio. Based on the designed TDM phased-MIMO radar, we develop a system to automatically localize multiple human subjects and estimate their vital signs. Extensive evaluations show that under two-subject scenarios, our system can achieve more than 98.06% accuracy for breathing rate (BR) and more than 82.89% accuracy for heartbeat rate (HR) estimation, at a subject-to-radar distance of 1.6 m when the targets are facing the radar. The minimal subjectto-subject angle separation is 30 • at a subject-to-radar distance of 1.6 m, corresponding to a close distance of 0.3 m between two subjects, which outperforms the state-of-the-art.

No abstract