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1. Cosmic Backscatter: New Ways to Communicate via Modulated Noise

   https://doi.org/10.1145/3626111.3628203  

   Kapetanovic, Zerina; Garman, Shanti; Stotland, Dara; Smith, Joshua R. (November 2023, HotNets '23: Proceedings of the 22nd ACM Workshop on Hot Topics in Networks)

   New methods of passive wireless communication are presented where no RF carrier is needed. Instead, data is wirelessly transmitted by modulating noise sources, from those found in electronic components to extraterrestrial noise sources. Any pair of noise sources with a difference in noise temperature can be used to enable communication. We discuss using the Earth, the Moon, the Sun, the coldness of space, and Active Cold Load circuits as sources of thermal contrast. We present Cosmic Backscatter and demonstrate that wireless connectivity can be enabled by switching an antenna connection between the "cold" Sky and a "hot" 50Ω resistor. Furthermore, we present Noise Suppression Communication, where data is transmitted by controlling an Active Cold Load to selectively reduce emitted noise below ambient temperature levels. 

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2. Adaptive RF Fingerprint Decomposition in Micro UAV Detection based on Machine Learning

   https://doi.org/10.1109/ICASSP39728.2021.9414985  

   Xu, Chengtao; He, Fengyu; Chen, Bowen; Jiang, Yushan; Song, Houbing (June 2021, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

   null (Ed.)

   Radio frequency (RF) signal classification has significantly been used for detecting and identifying the features of unknown unmanned aerial vehicles (UAVs). This paper proposes a method using empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) on extracting the communication channel characteristics of intruding UAVs. The decomposed intrinsic mode functions (IMFs) except noise components are selected for RF signal pattern recognition based on machine learning (ML). The classification results show that the denoising effects introduced by EMD and EEMD could both fit in improving the detection accuracy with different features of RF communication channel, especially on identifying time-varying RF signal sources. 

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3. Input-Reflectionless Out-of-Phase 3-dB Bandpass Filtering Couplers

   Gomez-Garcia, Roberto; Munoz-Ferreras, Jose-Maria; Psychogiou, Dimitra (May 2019, 2019 IEEE Radio and Wireless Symposium (RWS))

   A class of out-of-phase 3-dB bandpass-filtering couplers with input-reflectionless capabilities is presented. To obtain the bandpass-filter (BPF) functionality, identical BPF sections are respectively co-integrated in the coupler signal paths from the input to the direct and coupled ports. Furthermore, a resistivelyterminated bandstop-filter (BSF) section with complementary transfer function with regard to the one of the BPF section is loaded at the coupler input access. In this manner, the RF inputsignal energy that is not transmitted to the direct and coupled terminals is dissipated by the loading resistor of the BSF section. Hence, the input-reflectionless behavior is realized. Optimizationbased first-to-third-order design examples are shown. Moreover, for practical-validation purposes of this RF tri-functional device, a 2-GHz second-order microstrip prototype is built and tested. 

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4. Backscatter Communications with Passive Receivers: From Fundamentals to Applications

   Stanacevic, M.; Athalye, A.; Haas, Z.J.; Das, S.R; Djuric, P.M. (December 2020, ITU Journal)

   null (Ed.)

   The principle of backscattering has the potential to enable a full realization of the Internet of Things. This paradigm subsumes massively deployed things that have the capability to communicate directly with each other. Based on the types of excitation and receivers, we discriminate four types of backscattering systems: (i) Dedicated Exciter Active Receiver systems, (ii) Ambient Exciter Active Receiver systems, (iii) Dedicated Exciter Passive Receiver systems, and (iv) Ambient Exciter Passive Receiver systems. In this paper, we present an overview of bacskscattering systems with passive receivers which form the foundation for Backscattering Tag-to-Tag Networks (BTTNs). This is a technology that allows tiny batteryless RF tags attached to various objects to communicate directly with each other and to perform RF-based sensing of the communication link. We present an overview of recent innovations in hardware architectures for backscatter modulation, passive demodulation, and energy harvesting that overcome design challenges for passive tag-to-tag communication. We further describe the challenges in scaling up the architecture from a single link to a distributed network. We provide some examples of application scenarios enabled by BTTNs involving object-to-object communication and inter-object or human-object dynamic interactions. Finally, we discuss key challenges in present-day BTTN technology and future research directions. 

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5. Receiver Selectivity Limits on Bistatic Backscatter Range

   https://doi.org/10.1109/RFID49298.2020.9244826  

   Katanbaf, Mohamad; Saffari, Ali; Smith, Joshua R. (September 2020, 2020 IEEE International Conference on RFID (RFID))

   null (Ed.)

   Backscatter communication has been a popular choice in low-power/battery-free sensor nodes development. However, the effect of RF source to receiver distance on the operating range of this communication system has not been modeled accurately. In this paper, we propose a model for a bistatic backscatter system coverage map based on the receiver selectivity, receiver sensitivity, and geometric placement of the receiver, RF source, and the tag. To verify our proposed model and simulations, we perform an experiment using a low-cost commercial BLE receiver and a custom-designed BLE backscatter tag. We also show that the receiver selectivity might depend on the interference level, and present measurement results to signify how this dependence relates the system bit error rate to the RF excitation power. 

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