Project Description
We are developing RF tags that operate on harvested RF energy, have a passive envelope detector based receiver and a modest computing ability. They communicate directly with each other as they can resolve a reflected external RF excitation signal from a transmitting tag. This eliminates the need for the centralized device like RFID reader or on-tag high power radio. Prior to this work, the passive receiver based on envelope detection was not able to perform the wireless channel estimation as the receiver was not capable of IQ demodulation. Our novel technique to overcome this limitation is based on multiphase probing of the backscatter channel between a pair of tags, wherein the transmitting tag sends out the systematically designed probing signal which in turn enables the receiving tag to estimate channel parameters. This empowers an autonomous network of radio-less RF-powered backscattering tags with the ability to recognize and localize activities in the surrounding environments. Compared to centralized RFID reader based systems, our approach has (i) an order of magnitude lower cost due to the elimination of the high cost reader (ii) much greater scalability due to the ability of the tags to communicate with each other and form multihop networks and (iii) much higher precision, granularity and diversity in the measured RF fingerprint of the space, since for N tags our system establishes O(N2) distributed sensing channels as opposed to O(N) channels centralized around a single reader. By providing RF-powered passive RF tags the ability to ’fingerprint’ the surrounding wireless channels without any need for external active elements, we bring about a paradigm shift in passive RF tag networking. They are no longer limited to just traditional IoT like systems communicating ID information between each other, but can also sense and learn activities and interactions in their vicinity and propagate this knowledge all over the passive network.
Results
Channel Sensing. The core of this technology is estimation of amplitude and phase of wireless channel between two RF tags that integrate passive ultra-low power transceiver: multimodal reflector for signal transmission in presence of ambient RF signal and an envelope detector as receiver to extract the amplitude of the incident signal. We derive and demonstrate the limits in the estimation of the channel parameters as a function of the distance between the tags, incident ambient power and data rate in different environments. The phase estimator bias defines the performance of the channel estimation for tag localization. The bias is dominated by the multi-path reflections and in indoor environment with moderate reflections the phase error is limited to 25 degrees up to 2.3 m distance between the tags. The variance of the phase estimator determines the performance of RF fingerprinting of the environment in the vicinity of the tags, that is quantification of the dynamic processes, like activity or gesture recognition. For monitoring slow processes, with the period of phase sampling on the order of millisecond, the phase noise in the excitation RF signal dominates the ripple noise and inherent electronics noise. The variance is a function of the incident power and in the power range of interest, the measured variance is limited to 1 degree.
RF Tag IC Implementation. The designed SoC implementation integrates energy harvester and channel sensing/communication building blocks, that include wake-up detector and demodulator. Energy harvesting circuitry comprises a dual-channel rectifier and a power management unit that integrates a novel adaptive capacitor charging circuit and a voltage regulator. We achieve 64% energy conversion efficiency at -25dBm incident power. The proposed voltage regulator demonstrates a competitive figure-of-merit (FOM) with respect to other reported state-of-art designs with improved line regulation at the expense of lower load regulation. The designed wake-up detector demonstrates -68 dBm sensitivity while consuming 2 nW at 10 kbps data rate. The demodulator circuit integrates an ultra-low-power low-resolution successive-approximation analog-to-digital (ADC) converter with a clock oscillator. The proposed clock oscillator demonstrates, to the best of our knowledge, the highest energy-efficiency per nominal line regulation, with the one of the lowest reported power consumptions.
Collaborative Backscatter. As distance increases in a tag-to-tag link, a weaker backscatter signal becomes difficult to resolve for the receiving tag. We proposed two schemes based on conventional beamforming to boost the backscatter signal, that is to increase the modulation depth for easier resolution of transmitted data. In the first proposed technique, neighboring tags select their reflection coefficients to maximize the incident RF power at the location of the transmitting tag and do not participate in the data transmission. In the second technique, the neighboring tags are synchronized with the transmitting tag and participate in the data transmission. The selection of the optimal reflection coefficient in both techniques can be based on the pair-wise estimated phase of the tag-to-tag channel, or on a data-driven heuristic approach. We demonstrate that in a network comprising 8 RF tags, the gain in modulation depth in the first scheme ranges from 15% to 70%, while in the second scheme it is much higher, from 100% to 260%.
Sensing Applications. The large-scale network of intelligent RF tags based on passive channel estimation technique, can be deployed in applications like activity or gesture recognition and structural health monitoring. We show that for gesture recognition, we can achieve higher than 95% recognition with 10 gestures at different locations. We demonstrate that with tags embedded in a structural material, we can indirectly measure material integrity (presence of cracks and their propagation over time) and internal humidity (water content in pores and its variability over time).
Open-source RF tag platform
As a part of the project, we have developed an open-source RF tag. RF tag is an instrumented tag prototype that can be used for the data collection in experiments with tag-to-tag link in a presence of a dedicated exciter (RF source generator and antenna). RF tag implements a multiphase modulator and input baseband signal recording after the envelope detection. The multi-phase modulator integrates a 8-port RF switch. Switch is terminated with six impedances, preselected to provide the diversity in the reflection coefficient, along with an open-circuit and 50 Ohms. The envelope detector is followed by a low-pass filter and a 16-bit 1 MSample/s analog-to-digital (ADC) converter. The digitized amplitude of the input RF signal is collected by a microcontroller
unit (MCU) from the ADC via SPI communication. Data can be written into a on-board micro SD card or transferred to PC using USB connector.
Schematic of the RF tag
BOM of the RF tag
The gerber files and software are available upon request.
Project Team
Milutin Stanaćević, Professor, Electrical and Computer Engineering
Samir R. Das, Professor, Computer Science
Petar M. Djurić, Distinguished Professor, Electrical and Computer Engineering
Akshay Athalye, Research Scientist, Electrical and Computer Engineering
Yang Xie, PhD student
Manavjeet Singh, PhD student
Pengxu Chen, PhD student
Dyumaan Arvind, PhD student
Former members:
Yuanfei Huang, now with Qualcomm
Xiao Sha, now with Marvell Inc
Abeer Ahmad, now with Rochester Institute of Technology
Puyang Zheng, now with Texas Instruments
Publications
[1] Y. Xie, Y. Li, M. Singh, S.R. Das, P.M. Djurić, and M. Stanaćević, "Robust and Energy-Efficient Channel Estimation in RF Backscatter Tag-to-Tag Network," IEEE Journal of Radio Frequency Identification, vol. 9, pp. 567-578, 2025.
[2] Y. Xie, Y. Li, A. Ahmed, X. Sha, P.M. Djurić, S.R. Das, and M. Stanaćević, "Channel Sensing Based Distance Estimation in Backscattering RF Tag Networks," Proc. IEEE International Symposium on Circuits and Systems Conference (ISCAS’25), May 2025.
[3] A. Ahmed, M. Singh, Y. Xie, X. Sha, M. Stanaćević, S.R. Das and P.M. Djurić, "Improving Communication Performance of Passive Backscattering Tags Using Collaborative Backscatter," IEEE International Conference on RFID (RFID’25), pp. 1-6., 2025.
[4] PY. Zheng, D. Arvind, and M. Stanaćević, "Design of the Dual-Channel Dickson Rectifier with Native NMOS for RF Energy Harvesting Sensors," IEEE International Conference on RFID (RFID’24), pp. 1-6., 2024.
[5] Y. Xie, Y. Li, P.M. Djurić, S.R. Das, and M. Stanaćević, "Optimized Channel Phase Estimation in Passive RF Tag Network," IEEE International Conference on RFID (RFID’24), pp. 1-6., 2024.
[6] PY. Zheng, D. Arvind, and M. Stanaćević, "A Batteryless Power Management Unit for RF Energy Harvesting Sensors," IEEE 67th International Midwest Symposium on Circuits and Systems (MWSCAS’24), pp. 801-805., 2024.
[7] PY. Zheng, X. Sha, D. Arvind, Y. Xie and M. Stanaćević, ”Ultra-low Iq Fully Integrated NMOS LDO with Enhanced Load Regulation and Startup for RF Energy Harvesting Sensors,” IEEE 66th International Midwest Symposium on Circuits and Systems (MWSCAS’23), Aug. 2023.
[8] A. Ahmad, X. Sha, A. Athalye, S.R. Das, P.M. Djurić and M. Stanaćević, ”Amplitude and Phase Estimation of Backscatter Tag-to-tag Channel,” Proc. IEEE International Symposium on Circuits and Systems Conference (ISCAS’22), May 2022.
[9] X. Sha, P. Zheng and M. Stanaćević, ”High Sensitivity Near-zero Power Wakeup Receiver for Backscattering RF Tags,” Proc. IEEE International Symposium on Circuits and Systems Conference (ISCAS’22), May 2022.
[10] A. Ahmad, X. Sha, M. Stanaćević, A. Athalye, P.M. Djurić and S.R. Das, ”Enabling Passive Backscatter Tag Localization Without Active Receivers,” Proc. 19th ACM Conference on Embedded Networked Sensor Systems (SenSys 2021), November 2021.
[11] A. Ahmad, X. Sha, A. Athalye, S.R. Das, P.M. Djurić and M. Stanaćević, ”Collaborative Backscatter Based on Phase Channel Estimation in Passive RF Tag Networks,” Proc. 11th IEEE International Conference on RFID Technology and Applications (IEEE RFID-TA 2021), October 2021.
[12] M. Stanaćević, A. Ahmad, X. Sha, A. Athalye, S.R. Das, K. Caylor, B. Glisić and P.M. Djurić, ”RF Backscatter-Based Sensors for Structural Health Monitoring,” Forth International Balkan Conference on Communications and Networking (BalkanCom’21), September 2021.
[13] X. Sha, PY. Zheng and M. Stanaćević, ”1.81 kHz Relaxation Oscillator with Forward Bias Comparator and Leakage Current Compensation Based Techniques,” Proc. 34th IEEE International System-on-Chip Conference (SOCC’21), September 2021.
[14] PY. Zheng, X. Sha and M. Stanaćević, ”Analysis of the Sub-uA Fully Integrated NMOS LDO for Backscattering System,” Proc. 34th IEEE International System-on-Chip Conference (SOCC’21), September 2021.
[15] Y. Huang, A. Athalye, S.R. Das, P.M. Djurić and M. Stanaćević, ”RF Energy Harvesting and Management for Near-zero Power Passive Devices,” Proc. IEEE International Symposium on Circuits and Systems Conference (ISCAS’21), May 2021.
[16] M. Stanaćević, A. Athalye, Z.J. Haas, S.R. Das and P.M. Djurić, "Backscatter Communication with Passive Receivers: From Fundamentals to Applications," ITU Journal on Future and Evolving Technologies, vol. 1(1), pp. 1-11, 2020.
[17] A. Ahmad, Y. Huang, X. Sha, A. Athalye, M. Stanaćević, S.R. Das and P.M. Djurić, ”On Measuring Doppler Shifts between Tags in a Backscattering Tag-to-Tag Network with Applications in Tracking,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 9055-9059, May 2020.
