Signal Detection in Ambient Backscatter Systems: Fundamentals, Methods, and Trends

Internet-of-Things (IoT) is rapidly growing in wireless technology, aiming to connect vast numbers of devices to gather and distribute vital information. Despite individual devices having low energy consumption, the cumulative demand results in significant energy usage. Consequently, the concept of ultra-low-power tags gains appeal. Such tags communicate by reflecting rather than generating the radio frequency (RF) signals by themselves. Thus, these backscatter tags can be low-cost and battery-free. The RF signals can be ambient sources such as wireless-fidelity (Wi-Fi), cellular, or television (TV) signals, or the system can generate them externally. Backscatter channel characteristics are different from conventional point-to-point or cooperative relay channels. These systems are also affected by a strong interference link between the RF source and the tag besides the direct and backscattering links, making signal detection challenging. This paper provides an overview of the fundamentals, challenges, and ongoing research in signal detection for AmBC networks. It delves into various detection methods, discussing their advantages and drawbacks. The paper's emphasis on signal detection sets it apart and positions it as a valuable resource for IoT and wireless communication professionals and researchers.Comment: Accepted for publication in the IEEE Acces

Energy-Efficient Wireless Circuits and Systems for Internet of Things

As the demand of ultra-low power (ULP) systems for internet of thing (IoT) applications has been increasing, large efforts on evolving a new computing class is actively ongoing. The evolution of the new computing class, however, faced challenges due to hard constraints on the RF systems. Significant efforts on reducing power of power-hungry wireless radios have been done. The ULP radios, however, are mostly not standard compliant which poses a challenge to wide spread adoption. Being compliant with the WiFi network protocol can maximize an ULP radio’s potential of utilization, however, this standard demands excessive power consumption of over 10mW, that is hardly compatible with in ULP systems even with heavy duty-cycling. Also, lots of efforts to minimize off-chip components in ULP IoT device have been done, however, still not enough for practical usage without a clean external reference, therefore, this limits scaling on cost and form-factor of the new computer class of IoT applications. This research is motivated by those challenges on the RF systems, and each work focuses on radio designs for IoT applications in various aspects. First, the research covers several endeavors for relieving energy constraints on RF systems by utilizing existing network protocols that eventually meets both low-active power, and widespread adoption. This includes novel approaches on 802.11 communication with articulate iterations on low-power RF systems. The research presents three prototypes as power-efficient WiFi wake-up receivers, which bridges the gap between industry standard radios and ULP IoT radios. The proposed WiFi wake-up receivers operate with low power consumption and remain compatible with the WiFi protocol by using back-channel communication. Back-channel communication embeds a signal into a WiFi compliant transmission changing the firmware in the access point, or more specifically just the data in the payload of the WiFi packet. With a specific sequence of data in the packet, the transmitter can output a signal that mimics a modulation that is more conducive for ULP receivers, such as OOK and FSK. In this work, low power mixer-first receivers, and the first fully integrated ultra-low voltage receiver are presented, that are compatible with WiFi through back-channel communication. Another main contribution of this work is in relieving the integration challenge of IoT devices by removing the need for external, or off-chip crystals and antennas. This enables a small form-factor on the order of mm3-scale, useful for medical research and ubiquitous sensing applications. A crystal-less small form factor fully integrated 60GHz transceiver with on-chip 12-channel frequency reference, and good peak gain dual-mode on-chip antenna is presented.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162975/1/jaeim_1.pd

Ultralow noise pre-amplified receiver for free-space optical communications

The demand for high data rate in space communication links is increasing due to the growth of space exploration missions inter-satellite, and satellite-to-Earth data transmission. Optical communication systems capable of handling hundreds of Gigabits per second data transmission with a single light carrier and are suitable for such space links. In addition, light offers smaller beam divergence in space due to the shorter wavelength compared to radio frequency beams (RF), resulting in smaller link loss and smaller size receiver apertures required.The receiver sensitivity is one of the key factors that determines the capacity and reach for such long haul communication links. Currently, there is a search for the optimal modulation format and receiver implementation combination to achieve the best sensitivity for error-free transmission. In this thesis, we discuss and implement the best possible combination of these, both theoretically and experimentally. Phase sensitive parametric optical amplifier (PSA) can amplify optical signals ideally without adding any excess noise, limited only by quantum fluctuations. Employing these as preamplifiers in free-space receivers can thus improve the sensitivity compared to erbium doped fiber amplifiers. We implement a two-mode PSA with a noise figure of 1.2 dB, which can amplify both quadratures of a signal, being used as a pre-amplifier in coherent receiver setup. We experimentally demonstrate a record black-box sensitivity of 1 photon-per-bit using PSA receiver for quadrature phase shift keying (QPSK) modulation format at 10.5 Gbps with 100 % overhead forward error correction code. This sensitivity also includes ultra-low pump power (-72 dBm) which is recovered using pre-amplified injection locking. We also investigate the most power efficient modulation formats, where a combination of m-(pulse-position modulation) PPM+QPSK with higher m-values provides best sensitivity at relatively high received SNR-per-bit, while QPSK outperforms all formats investigated at very low SNR-per-bit, which is ideal for space communications

Shuttle Ku-band and S-band communications implementations study

The interfaces between the Ku-band system and the TDRSS, between the S-band system and the TDRSS, GSTDN and SGLS networks, and between the S-band payload communication equipment and the other Orbiter avionic equipment were investigated. The principal activities reported are: (1) performance analysis of the payload narrowband bent-pipe through the Ku-band communication system; (2) performance evaluation of the TDRSS user constraints placed on the S-band and Ku-band communication systems; (3) assessment of the shuttle-unique S-band TDRSS ground station false lock susceptibility; (4) development of procedure to make S-band antenna measurements during orbital flight; (5) development of procedure to make RFI measurements during orbital flight to assess the performance degradation to the TDRSS S-band communication link; and (6) analysis of the payload interface integration problem areas

Interference-robust CMOS receivers for IoT:Highly linear RF front-ends at low power

Wireless technologies have brought Internet access to more than half of the world’s population in the last decade. Nowadays, Internet-of-Things (IoT) technology extends the internet connectivity to sensor nodes embedded in machines, animals, and plants. It will soon put us in a realm of billions of interconnected sensor nodes networking and communicating with each other. Such unprecedented growth of wireless devices puts a big challenge of sustainable and robust connectivity in front of us. Concretely, this challenge demands a wireless sensor node with low power and robust connectivity. Radios are the physical interface for sensor nodes with the external world and are one of the power-hungry components in sensor nodes. Hence it is imperative to make them energy-efficient and interference-robust. This thesis explores CMOS passive mixer-first receiver topology to enhance the interference tolerance of receivers in IoT radios. The dissertation proposes a novel N-path filter/mixer topology at the circuit level and a multipath cross-correlation technique at the system level. Two test-chips of mixer-first receiver front ends, using these techniques, are implemented in CMOS FDSOI 22nm technology as a proof-of-concept. The experimental prototypes demonstrate voltage gain in passive mixers and exhibit high-Q widely-tunable RF filtering, large out-of-band and harmonic interferer tolerance, and moderate noise figure while consuming much lower power than several state-of-the-art receivers

Deep learning for wireless communications : flexible architectures and multitask learning

Demand for wireless connectivity has never been higher and continues to grow rapidly. Connecting more devices requires mindfulness in managing the limited resources of energy and radio spectrum. The advent of Software Defined Radio (SDR) has enabled breathroughs in radio configurability, enabling dynamic spectrum access and physical layer optimizations at runtime. In recent years Machine Learning (ML) has been a key enabling technology of various innovations in the wireless communications domain, taking advantage of the newfound flexibility in SDR. The new ML-based signal processing models are no longer based entirely on Digital Signal Processing (DSP) expertise, but are developed in a data-driven approach. This paradigm shift in receiver design is recent, and appropriate architectures and best model training practices have yet to be established. This thesis explores multiple wireless communications tasks addressed with the toolbox of Deep Learning (DL), which is a subset of ML. Many existing DL solutions are hampered by the limitations of the chosen architectures, which limits their adoptability as drag-and-drop solutions by wireless system designers. Recurrent Neural Network (RNN) and Fully Convolutional Neural Network (FCN) architecture types are explored that enable the adaptability one would expect of classic DSP functions (like the filter). The field of wireless communications boasts a wealth of data, due to the mature and feature-rich simulation software ecosystem. In Radio Frequency Machine Learning (RFML) this is regularly leveraged to produce datasets for the new data-driven models. Techniques like Multitask Learning (MTL) can exploit this simulated data even further by allowing models to be trained on their primary task, like signal classification or demodulation, while simultaneously estimating the channel quality. The findings presented in this work show that fully convolutional architectures can be more appropriate for tasks like frame synchronization compared to commonly applied classification models. RNN-based autoencoders achieve good results as an end-to-end trainable receiver solution, however they can be challenging to apply to longer sequences. MTL is identified as an excellent technique not only for training unique models, capable of performing multiple tasks, but as a regularization technique in RFML.Demand for wireless connectivity has never been higher and continues to grow rapidly. Connecting more devices requires mindfulness in managing the limited resources of energy and radio spectrum. The advent of Software Defined Radio (SDR) has enabled breathroughs in radio configurability, enabling dynamic spectrum access and physical layer optimizations at runtime. In recent years Machine Learning (ML) has been a key enabling technology of various innovations in the wireless communications domain, taking advantage of the newfound flexibility in SDR. The new ML-based signal processing models are no longer based entirely on Digital Signal Processing (DSP) expertise, but are developed in a data-driven approach. This paradigm shift in receiver design is recent, and appropriate architectures and best model training practices have yet to be established. This thesis explores multiple wireless communications tasks addressed with the toolbox of Deep Learning (DL), which is a subset of ML. Many existing DL solutions are hampered by the limitations of the chosen architectures, which limits their adoptability as drag-and-drop solutions by wireless system designers. Recurrent Neural Network (RNN) and Fully Convolutional Neural Network (FCN) architecture types are explored that enable the adaptability one would expect of classic DSP functions (like the filter). The field of wireless communications boasts a wealth of data, due to the mature and feature-rich simulation software ecosystem. In Radio Frequency Machine Learning (RFML) this is regularly leveraged to produce datasets for the new data-driven models. Techniques like Multitask Learning (MTL) can exploit this simulated data even further by allowing models to be trained on their primary task, like signal classification or demodulation, while simultaneously estimating the channel quality. The findings presented in this work show that fully convolutional architectures can be more appropriate for tasks like frame synchronization compared to commonly applied classification models. RNN-based autoencoders achieve good results as an end-to-end trainable receiver solution, however they can be challenging to apply to longer sequences. MTL is identified as an excellent technique not only for training unique models, capable of performing multiple tasks, but as a regularization technique in RFML

The 30/20 GHz mixed user architecture development study

A mixed-user system is described which provides cost-effective communications services to a wide range of user terminal classes, ranging from one or two voice channel support in a direct-to-user mode, to multiple 500 mbps trunking channel support. Advanced satellite capabilities are utilized to minimize the cost of small terminals. In a system with thousands of small terminals, this approach results in minimum system cost

Analysis and Design of Energy Efficient Frequency Synthesizers for Wireless Integrated Systems

Advances in ultra-low power (ULP) circuit technologies are expanding the IoT applications in our daily life. However, wireless connectivity, small form factor and long lifetime are still the key constraints for many envisioned wearable, implantable and maintenance-free monitoring systems to be practically deployed at a large scale. The frequency synthesizer is one of the most power hungry and complicated blocks that not only constraints RF performance but also offers subtle scalability with power as well. Furthermore, the only indispensable off-chip component, the crystal oscillator, is also associated with the frequency synthesizer as a reference. This thesis addresses the above issues by analyzing how phase noise of the LO affect the frequency modulated wireless system in different aspects and how different noise sources in the PLL affect the performance. Several chip prototypes have been demonstrated including: 1) An ULP FSK transmitter with SAR assisted FLL; 2) A ring oscillator based all-digital BLE transmitter utilizing a quarter RF frequency LO and 4X frequency multiplier; and 3) An XO-less BLE transmitter with an RF reference recovery receiver. The first 2 designs deal with noise sources in the PLL loop for ultimate power and cost reduction, while the third design deals with the reference noise outside the PLL and explores a way to replace the XO in ULP wireless edge nodes. And at last, a comprehensive PN theory is proposed as the design guideline.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153420/1/chenxing_1.pd