Qinyi Xu

Combating Strong–Weak Spatial–Temporal Resonances in Time-Reversal Uplinks

In a time-reversal (TR) communication system, the signal-to-noise ratio (SNR) is boosted and the inter-user interference (IUI) is suppressed due to the spatial-temporal resonances, commonly known as the focusing effects, of the TR technique when implemented in a rich scattering environment. However, since the spatial-temporal resonances highly depend on the location-specific multipath profile, there exists a strong-weak spatial-temporal resonances effect. In the TR uplink system, different users at different locations enjoy different strengths of spatial-temporal resonances, i.e., the received signal-to-interference-noise ratios (SINRs) for different users vary, and the weak ones can be blocked from correct detection in the presence of strong ones. In this paper, we formulate the strong-weak spatial-temporal resonances in the multiuser TR uplink system as a max-min weighted SINR balancing problem by joint power control and signature design. Then, a novel two-stage adaptive algorithm that can guarantee the convergence is proposed. In stage I, the original nonconvex problem is relaxed into a Perron Frobenius eigenvalue optimization problem and an iterative algorithm is proposed to obtain the optimum efficiently. In stage II, the gradient search method is applied to update the relaxed feasible set until the global optimum for the original optimization problem is obtained. Numerical results show that our algorithm converges quickly, achieves a high energy-efficiency, and provides a performance guarantee to all users.

TRIEDS: Wireless Events Detection Through the Wall

In this paper, we propose a novel wireless indoor events detection system, TRIEDS. By leveraging the time-reversal technique to capture the changes of channel state information (CSI) in the indoor environment, TRIEDS enables low-complexity single-antenna devices that operate in the ISM band to perform through-the-wall indoor multiple events detection. The multipath phenomenon denotes that the electromagnetic signals undergo different reflecting and scattering paths in a rich-scattering environment. In TRIEDS, each indoor event is detected by matching the instantaneous CSI to a multipath profile in a training database. To validate the feasibility of TRIEDS and to evaluate the performance, we build a prototype that works on ISM band with carrier frequency being 5.4 GHz and 125 MHz bandwidth. Experiments are conducted to detect the states of the indoor wooden doors. Experimental results show that with a single receiver access point and transmitter (client), TRIEDS can achieve a detection rate higher than 96.92% and a false alarm rate smaller than 3.08% under either line-of-sight (LOS) or non-LOS transmission.

Radio Biometrics: Human Recognition Through a Wall

In this paper, we show the existence of human radio biometrics and present a human identification system that can discriminate individuals even through the walls in a non-line-of-sight condition. Using commodity Wi-Fi devices, the proposed system captures the channel state information (CSI) and extracts human radio biometric information from Wi-Fi signals using the time-reversal (TR) technique. By leveraging the fact that broadband wireless CSI has a significant number of multipaths, which can be altered by human body interferences, the proposed system can recognize individuals in the TR domain without line-of-sight radio. We built a prototype of the TR human identification system using standard Wi-Fi chipsets with

The Promise of Radio Analytics: A Future Paradigm of Wireless Positioning, Tracking, and Sensing

3 \times 3

Time reversal based wireless events detection

multi-in multi-out (MIMO) transmission. The performance of the proposed system is evaluated and validated through multiple experiments. In general, the TR human identification system achieves an accuracy of 98.78% for identifying about a dozen of individuals using a single transmitter and receiver pair. Thanks to the ubiquitousness of Wi-Fi, the proposed system shows the promise for future low-cost low-complexity reliable human identification applications based on radio biometrics.

Radio shot: Through-the-wall human recognition

With the proliferation of Internet of Things (IoT) applications, billions of household appliances, phones, smart devices, security systems, environment sensors, vehicles, buildings, and other radio-connected devices will transmit data and communicate with each other or people, and it will be possible to constantly measure and track virtually everything. Among the various approaches to measuring what is happening in the surrounding environment, wireless sensing has received increasing attention in recent years because of the ubiquitous deployment of wireless radio devices. In addition, human activities affect wireless signal propagation, so understanding and analyzing how these signals react to human activities can reveal rich information about the activities around us.

Joint power control and signature design for time-reversal uplinks

In this work, we propose a novel wireless time-reversal indoor events detection system (TRIEDS). By leveraging the time-reversal (TR) technique to capture the changes of channel state information (CSI) in the indoor environment, TRIEDS enables low-complexity single-antenna devices that operate in the ISM band to perform through-the-wall multiple events detection. In TRIEDS, each indoor event is detected by matching the instantaneous CSI to a multipath profile in a training database. To validate the feasibility of TRIEDS and to evaluate the performance, we build a prototype that works on ISM band with carrier frequency being 5.4 GHz and a 125 MHZ bandwidth. Experiments are conducted to monitor the states of the indoor wooden doors. Experimental results show that with a single receiver (AP) and transmitter (client), TRIEDS can achieve a detection rate higher than 96:92% and a false alarm rate smaller than 3:08% under either line-of-sight (LOS) or non-LOS transmission.

Optimal pricing for interference control in time-reversal device-to-device uplinks

In this work, we show the existence of human radio biometrics and present a human identification system that can discriminate individuals even through the walls in a non-line-of-sight condition. Using commodity WiFi devices, the proposed system captures the channel state information (CSI) and extracts human radio biometric information from WiFi signals using time-reversal (TR) technique. By leveraging the fact that broadband wireless CSI has significant number of multipaths, which can be altered by human body interferences, the proposed system can recognize individuals in the TR domain without line-of-sight path. We built a prototype of the TR human identification system using standard WiFi chipsets with 3 × 3 MIMO transmission. The performance of the proposed system is evaluated and validated through multiple experiments. In general, the TR human identification system achieves an accuracy of 98.78% for identifying about a dozen of individuals using a single transmitter and receiver pair.

Real-Time Indoor Event Monitoring Using CSI Time Series.

In the time reversal division multiple access (TRDMA) system, the signal-to-noise ratio (SNR) is boosted and the inter-user interference (IUI) is suppressed due to the temporal and spatial focusing effects of the time-reversal (TR) technique. However, since the focusing effects highly depend on the location-specific multipath profile, there exists a strong-weak focusing effect in the TRDMA uplink system where different users at different locations enjoy different level of focusing effects. In this paper, we formulate the strong-weak focusing effect in the multiuser TRDMA uplink system as a max-min signal-to-interference-noise ratio (SINR) balancing problem by means of joint power control and signature filter design. A novel two-stage adaptive algorithm that can guarantee the convergence is proposed. Numerical results show that our algorithm converges quickly, achieves a high energy-efficiency gain, and provides a performance guarantee to all users.

Time Reversal Indoor Tracking with Centimeter Accuracy.

The Device-to-Device (D2D) communication is a promising technique to empower local wireless communications. However, without proper management it may generate interference to the existing network and degrade the overall performance. By treating each multipath as a virtual antenna, time-reversal (TR) signal transmission in a rich-scattering environment produces a spatial-temporal resonance which efficiently suppresses the inter-user interference (IUI) while boosting the signal power at the target receiver. In this work, we design a TR-based D2D hybrid network, where both primary users (PUs) and D2D pairs share the same time-frequency resources and use TR focusing effect to combat interference. With the purpose of enhancing D2D performance while providing a performance protection to PUs, an efficient optimal pricing algorithm is proposed to dynamically control interference through TR focusing strength control.