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- All Subjects: Electrical Engineering
- Status: Published
Description
In the past half century, low-power wireless signals from portable radar sensors, initially continuous-wave (CW) radars and more recently ultra-wideband (UWB) radar systems, have been successfully used to detect physiological movements of stationary human beings.
The thesis starts with a careful review of existing signal processing techniques and state of the art methods possible for vital signs monitoring using UWB impulse systems. Then an in-depth analysis of various approaches is presented.
Robust heart-rate monitoring methods are proposed based on a novel result: spectrally the fundamental heartbeat frequency is respiration-interference-limited while its higher-order harmonics are noise-limited. The higher-order statistics related to heartbeat can be a robust indication when the fundamental heartbeat is masked by the strong lower-order harmonics of respiration or when phase calibration is not accurate if phase-based method is used. Analytical spectral analysis is performed to validate that the higher-order harmonics of heartbeat is almost respiration-interference free. Extensive experiments have been conducted to justify an adaptive heart-rate monitoring algorithm. The scenarios of interest are, 1) single subject, 2) multiple subjects at different ranges, 3) multiple subjects at same range, and 4) through wall monitoring.
A remote sensing radar system implemented using the proposed adaptive heart-rate estimation algorithm is compared to the competing remote sensing technology, a remote imaging photoplethysmography system, showing promising results.
State of the art methods for vital signs monitoring are fundamentally related to process the phase variation due to vital signs motions. Their performance are determined by a phase calibration procedure. Existing methods fail to consider the time-varying nature of phase noise. There is no prior knowledge about which of the corrupted complex signals, in-phase component (I) and quadrature component (Q), need to be corrected. A precise phase calibration routine is proposed based on the respiration pattern. The I/Q samples from every breath are more likely to experience similar motion noise and therefore they should be corrected independently. High slow-time sampling rate is used to ensure phase calibration accuracy. Occasionally, a 180-degree phase shift error occurs after the initial calibration step and should be corrected as well. All phase trajectories in the I/Q plot are only allowed in certain angular spaces. This precise phase calibration routine is validated through computer simulations incorporating a time-varying phase noise model, controlled mechanic system, and human subject experiment.
The thesis starts with a careful review of existing signal processing techniques and state of the art methods possible for vital signs monitoring using UWB impulse systems. Then an in-depth analysis of various approaches is presented.
Robust heart-rate monitoring methods are proposed based on a novel result: spectrally the fundamental heartbeat frequency is respiration-interference-limited while its higher-order harmonics are noise-limited. The higher-order statistics related to heartbeat can be a robust indication when the fundamental heartbeat is masked by the strong lower-order harmonics of respiration or when phase calibration is not accurate if phase-based method is used. Analytical spectral analysis is performed to validate that the higher-order harmonics of heartbeat is almost respiration-interference free. Extensive experiments have been conducted to justify an adaptive heart-rate monitoring algorithm. The scenarios of interest are, 1) single subject, 2) multiple subjects at different ranges, 3) multiple subjects at same range, and 4) through wall monitoring.
A remote sensing radar system implemented using the proposed adaptive heart-rate estimation algorithm is compared to the competing remote sensing technology, a remote imaging photoplethysmography system, showing promising results.
State of the art methods for vital signs monitoring are fundamentally related to process the phase variation due to vital signs motions. Their performance are determined by a phase calibration procedure. Existing methods fail to consider the time-varying nature of phase noise. There is no prior knowledge about which of the corrupted complex signals, in-phase component (I) and quadrature component (Q), need to be corrected. A precise phase calibration routine is proposed based on the respiration pattern. The I/Q samples from every breath are more likely to experience similar motion noise and therefore they should be corrected independently. High slow-time sampling rate is used to ensure phase calibration accuracy. Occasionally, a 180-degree phase shift error occurs after the initial calibration step and should be corrected as well. All phase trajectories in the I/Q plot are only allowed in certain angular spaces. This precise phase calibration routine is validated through computer simulations incorporating a time-varying phase noise model, controlled mechanic system, and human subject experiment.
ContributorsRong, Yu (Author) / Bliss, Daniel W (Thesis advisor) / Richmond, Christ D (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Alkhateeb, Ahmed (Committee member) / Arizona State University (Publisher)
Created2018
Description
The recent trends in wireless communication, fueled by the demand for lower latency and higher bandwidth, have caused the migration of users from lower frequencies to higher frequencies, i.e., from 2.5GHz to millimeter wave. However, the migration to higher frequencies has its challenges. The sensitivity to blockages is a key challenge for millimeter wave and terahertz networks in 5G and beyond. Since these networks mainly rely on line-of-sight (LOS) links, sudden link blockages highly threaten the reliability of such networks. Further, when the LOS link is blocked, the network typically needs to hand off the user to another LOS basestation, which may incur critical time latency, especially if a search over a large codebook of narrow beams is needed. A promising way to tackle the reliability and latency challenges lies in enabling proaction in wireless networks. Proaction allows the network to anticipate future blockages, especially dynamic blockages, and initiate user hand-off beforehand. This thesis presents a complete machine learning framework for enabling proaction in wireless networks relying on the multi-modal 3D LiDAR(Light Detection and Ranging) point cloud and position data. In particular, the paper proposes a sensing-aided wireless communication solution that utilizes bimodal machine learning to predict the user link status. This is mainly achieved via a deep learning algorithm that learns from LiDAR point-cloud and position data to distinguish between LOS and NLOS(non line-of-sight) links. The algorithm is evaluated on the multi-modal wireless Communication Dataset DeepSense6G dataset. It is a time-synchronized collection of data from various sensors such as millimeter wave power, position, camera, radar, and LiDAR. Experimental results indicate that the algorithm can accurately predict link status with 87% accuracy. This highlights a promising direction for enabling high reliability and low latency in future wireless networks.
ContributorsSrinivas, Tirumalai Vinjamoor Nikhil (Author) / Alkhateeb, Ahmed (Thesis advisor) / Trichopoulos, Georgios (Committee member) / Myhajlenko, Stefan (Committee member) / Arizona State University (Publisher)
Created2022
Description
Within the near future, a vast demand for autonomous vehicular techniques can be forecast on both aviation and ground platforms, including autonomous driving, automatic landing, air traffic management. These techniques usually rely on the positioning system and the communication system independently, where it potentially causes spectrum congestion. Inspired by the spectrum sharing technique, Communications and High-Precision Positioning (CHP2) system is invented to provide a high precision position service (precision ~1cm) while performing the communication task simultaneously under the same spectrum. CHP2 system is implemented on the consumer-off-the-shelf (COTS) software-defined radio (SDR) platform with customized hardware. Taking the advantages of the SDR platform, the completed baseband processing chain, time-of-arrival estimation (ToA), time-of-flight estimation (ToF) are mathematically modeled and then implemented onto the system-on-chip (SoC) system. Due to the compact size and cost economy, the CHP2 system can be installed on different aerial or ground platforms enabling a high-mobile and reconfigurable network.
In this dissertation report, the implementation procedure of the CHP2 system is discussed in detail. It mainly focuses on the system construction on the Xilinx Ultrascale+ SoC platform. The CHP2 waveform design, ToA solution, and timing exchanging algorithms are also introduced. Finally, several in-lab tests and over-the-air demonstrations are conducted. The demonstration shows the best ranging performance achieves the ~1 cm standard deviation and 10Hz refreshing rate of estimation by using a 10MHz narrow-band signal over 915MHz (US ISM) or 783MHz (EU Licensed) carrier frequency.
In this dissertation report, the implementation procedure of the CHP2 system is discussed in detail. It mainly focuses on the system construction on the Xilinx Ultrascale+ SoC platform. The CHP2 waveform design, ToA solution, and timing exchanging algorithms are also introduced. Finally, several in-lab tests and over-the-air demonstrations are conducted. The demonstration shows the best ranging performance achieves the ~1 cm standard deviation and 10Hz refreshing rate of estimation by using a 10MHz narrow-band signal over 915MHz (US ISM) or 783MHz (EU Licensed) carrier frequency.
ContributorsYu, Hanguang (Author) / Bliss, Daniel (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Alkhateeb, Ahmed (Committee member) / Ogras, Umit Y. (Committee member) / Arizona State University (Publisher)
Created2020
Description
With the rapid development of reflect-arrays and software-defined meta-surfaces, reconfigurable intelligent surfaces (RISs) have been envisioned as promising technologies for next-generation wireless communication and sensing systems. These surfaces comprise massive numbers of nearly-passive elements that interact with the incident signals in a smart way to improve the performance of such systems. In RIS-aided communication systems, designing this smart interaction, however, requires acquiring large-dimensional channel knowledge between the RIS and the transmitter/receiver. Acquiring this knowledge is one of the most crucial challenges in RISs as it is associated with large computational and hardware complexity. For RIS-aided sensing systems, it is interesting to first investigate scene depth perception based on millimeter wave (mmWave) multiple-input multiple-output (MIMO) sensing. While mmWave MIMO sensing systems address some critical limitations suffered by optical sensors, realizing these systems possess several key challenges: communication-constrained sensing framework design, beam codebook design, and scene depth estimation challenges. Given the high spatial resolution provided by the RISs, RIS-aided mmWave sensing systems have the potential to improve the scene depth perception, while imposing some key challenges too. In this dissertation, for RIS-aided communication systems, efficient RIS interaction design solutions are proposed by leveraging tools from compressive sensing and deep learning. The achievable rates of these solutions approach the upper bound, which assumes perfect channel knowledge, with negligible training overhead. For RIS-aided sensing systems, a mmWave MIMO based sensing framework is first developed for building accurate depth maps under the constraints imposed by the communication transceivers. Then, a scene depth estimation framework based on RIS-aided sensing is developed for building high-resolution accurate depth maps. Numerical simulations illustrate the promising performance of the proposed solutions, highlighting their potential for next-generation communication and sensing systems.
ContributorsTaha, Abdelrahman (Author) / Alkhateeb, Ahmed (Thesis advisor) / Bliss, Daniel (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Michelusi, Nicolò (Committee member) / Arizona State University (Publisher)
Created2023
Description
In this dissertation, I implement and demonstrate a distributed coherent mesh beamforming system, for wireless communications, that provides increased range, data rate, and robustness to interference. By using one or multiple distributed, locally-coherent meshes as antenna arrays, I develop an approach that realizes a performance improvement, related to the number of mesh elements, in signal-to-noise ratio over a traditional single-antenna to single-antenna link without interference. I further demonstrate that in the presence of interference, the signal-to-interference-plus-noise ratio improvement is significantly greater for a wide range of environments. I also discuss key performance bounds that drive system design decisions as well as techniques for robust distributed adaptive beamformer construction. I develop and implement an over-the-air distributed time and frequency synchronization algorithm to enable distributed coherence on software-defined radios. Finally, I implement the distributed coherent mesh beamforming system over-the-air on a network of software-defined radios and demonstrate both simulated and experimental results both with and without interference that achieve performance approaching the theoretical bounds.
ContributorsHoltom, Jacob (Author) / Bliss, Daniel W (Thesis advisor) / Alkhateeb, Ahmed (Committee member) / Herschfelt, Andrew (Committee member) / Michelusi, Nicolò (Committee member) / Arizona State University (Publisher)
Created2023
Description
Millimeter-wave (mmWave) and sub-terahertz (sub-THz) systems aim to utilize the large bandwidth available at these frequencies. This has the potential to enable several future applications that require high data rates, such as autonomous vehicles and digital twins. These systems, however, have several challenges that need to be addressed to realize their gains in practice. First, they need to deploy large antenna arrays and use narrow beams to guarantee sufficient receive power. Adjusting the narrow beams of the large antenna arrays incurs massive beam training overhead. Second, the sensitivity to blockages is a key challenge for mmWave and THz networks. Since these networks mainly rely on line-of-sight (LOS) links, sudden link blockages highly threaten the reliability of the networks. Further, when the LOS link is blocked, the network typically needs to hand off the user to another LOS basestation, which may incur critical time latency, especially if a search over a large codebook of narrow beams is needed. A promising way to tackle both these challenges lies in leveraging additional side information such as visual, LiDAR, radar, and position data. These sensors provide rich information about the wireless environment, which can be utilized for fast beam and blockage prediction. This dissertation presents a machine-learning framework for sensing-aided beam and blockage prediction. In particular, for beam prediction, this work proposes to utilize visual and positional data to predict the optimal beam indices. For the first time, this work investigates the sensing-aided beam prediction task in a real-world vehicle-to-infrastructure and drone communication scenario. Similarly, for blockage prediction, this dissertation proposes a multi-modal wireless communication solution that utilizes bimodal machine learning to perform proactive blockage prediction and user hand-off. Evaluations on both real-world and synthetic datasets illustrate the promising performance of the proposed solutions and highlight their potential for next-generation communication and sensing systems.
ContributorsCharan, Gouranga (Author) / Alkhateeb, Ahmed (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Turaga, Pavan (Committee member) / Michelusi, Nicolò (Committee member) / Arizona State University (Publisher)
Created2024
Description
The objective of this work is to design a novel method for imaging targets and scenes which are not directly visible to the observer. The unique scattering properties of terahertz (THz) waves can turn most building surfaces into mirrors, thus allowing someone to see around corners and various occlusions. In the visible regime, most surfaces are very rough compared to the wavelength. As a result, the spatial coherency of reflected signals is lost, and the geometry of the objects where the light bounced on cannot be retrieved. Interestingly, the roughness of most surfaces is comparable to the wavelengths at lower frequencies (100 GHz – 10 THz) without significantly disturbing the wavefront of the scattered signals, behaving approximately as mirrors. Additionally, this electrically small roughness is beneficial because it can be used by the THz imaging system to locate the pose (location and orientation) of the mirror surfaces, thus enabling the reconstruction of both line-of-sight (LoS) and non-line-of-sight (NLoS) objects.
Back-propagation imaging methods are modified to reconstruct the image of the 2-D scenario (range, cross-range). The reflected signal from the target is collected using a SAR (Synthetic Aperture Radar) set-up in a lab environment. This imaging technique is verified using both full-wave 3-D numerical analysis models and lab experiments.
The novel imaging approach of non-line-of-sight-imaging could enable novel applications in rescue and surveillance missions, highly accurate localization methods, and improve channel estimation in mmWave and sub-mmWave wireless communication systems.
Back-propagation imaging methods are modified to reconstruct the image of the 2-D scenario (range, cross-range). The reflected signal from the target is collected using a SAR (Synthetic Aperture Radar) set-up in a lab environment. This imaging technique is verified using both full-wave 3-D numerical analysis models and lab experiments.
The novel imaging approach of non-line-of-sight-imaging could enable novel applications in rescue and surveillance missions, highly accurate localization methods, and improve channel estimation in mmWave and sub-mmWave wireless communication systems.
ContributorsDoddalla, Sai Kiran kiran (Author) / Trichopoulos, George (Thesis advisor) / Alkhateeb, Ahmed (Committee member) / Zeinolabedinzadeh, Saeed (Committee member) / Aberle, James T., 1961- (Committee member) / Arizona State University (Publisher)
Created2019
Description
Limited spectral access motivates technologies that adapt to diminishing resources and increasingly cluttered environments. A joint positioning-communications system is designed and implemented on \acf{COTS} hardware. This system enables simultaneous positioning of, and communications between, nodes in a distributed network of base-stations and unmanned aerial systems (UASs). This technology offers extreme ranging precision ($<$ 5 cm) with minimal bandwidth (10 MHz), a secure communications link to protect against cyberattacks, a small form factor that enables integration into numerous platforms, and minimal resource consumption which supports high-density networks. The positioning and communications tasks are performed simultaneously with a single, co-use waveform, which efficiently utilizes limited resources and supports higher user densities. The positioning task uses a cooperative, point-to-point synchronization protocol to estimate the relative position and orientation of all users within the network. The communications task distributes positioning information between users and secures the positioning task against cyberattacks. This high-performance system is enabled by advanced time-of-arrival estimation techniques and a modern phase-accurate distributed coherence synchronization algorithm. This technology may be installed in ground-stations, ground vehicles, unmanned aerial systems, and airborne vehicles, enabling a highly-mobile, re-configurable network with numerous applications.
ContributorsHerschfelt, Andrew (Author) / Bliss, Daniel W (Thesis advisor) / Cochran, Douglas (Committee member) / Richmond, Christ (Committee member) / Alkhateeb, Ahmed (Committee member) / Arizona State University (Publisher)
Created2019
Description
Unmanned aerial systems (UASs) have recently enabled novel applications such as passenger transport and package delivery, but are increasingly vulnerable to cyberattack and therefore difficult to certify. Legacy systems such as GPS provide these capabilities extremely well, but are sensitive to spoofing and hijacking. An alternative intelligent transport system (ITS) was developed that provides highly secure communications, positioning, and timing synchronization services to networks of cooperative RF users, termed Communications and High-Precision Positioning (CHP2) system. This technology was implemented on consumer-off-the-shelf (COTS) hardware and it offers rapid (<100 ms) and precise (<5 cm) positioning capabilities in over-the-air experiments using flexible ground stations and UAS platforms using limited bandwidth (10 MHz). In this study, CHP2 is considered in the context of safety-critical and resource limited transport applications and urban air mobility. The two-way ranging (TWR) protocol over a joint positioning-communications waveform enables distributed coherence and time-of-flight(ToF) estimation. In a multi-antenna setup, the cross-platform ranging on participating nodes in the network translate to precise target location and orientation. In the current form, CHP2 necessitates a cooperative timing exchange at regular intervals. Dynamic resource management supports higher user densities by constantly renegotiating spectral access depending on need and opportunity. With these novel contributions to the field of integrated positioning and communications, CHP2 is a suitable candidate to provide both communications, navigation, and surveillance (CNS) and alternative positioning, navigation, and timing (APNT) services for high density safety-critical transport applications on a variety of vehicular platforms.
ContributorsSrinivas, Sharanya (Author) / Bliss, Daniel W. (Thesis advisor) / Richmond, Christ D. (Committee member) / Chakrabarti, Chaitali (Committee member) / Alkhateeb, Ahmed (Committee member) / Arizona State University (Publisher)
Created2020
Description
Reconfigurable metasurfaces (RMSs) are promising solutions for beamforming and sensing applications including 5G and beyond wireless communications, satellite and radar systems, and biomarker sensing. In this work, three distinct RMS architectures – reconfigurable intelligent surfaces (RISs), meta-transmission lines (meta-TLs), and substrate integrated waveguide leaky-wave antennas (SIW-LWAs) are developed and characterized. The ever-increasing demand for higher data rates and lower latencies has propelled the telecommunications industry to adopt higher frequencies for 5G and beyond wireless communications. However, this transition to higher frequencies introduces challenges in terms of signal coverage and path loss. Many base stations would be necessary to ensure signal fidelity in such a setting, making bulky phased array-based solutions impractical. Consequently, to meet the unique needs of 5G and beyond wireless communication networks, this work proposes the use of RISs characterized by low-profile, low-RF losses, low-power consumption, and high-gain capabilities, making them excellent candidates for future wireless communication applications. Specifically, RISs at sub-6GHz, mmWave and sub-THz frequencies are analyzed to demonstrate their ability to improve signal strength and coverage.
Further, a linear meta-TL wave space is designed to achieve miniaturization of true-time delay beamforming structures such as Rotman lenses which are traditionally bulky. To address this challenge, a modified lumped element TL model is proposed. A meta-TL is created by including the mutual coupling effects and can be used to slow down the electromagnetic signal and realize miniaturized lenses. A proof-of-concept 1D meta-TL is developed to demonstrate about 90% size reduction and 40% bandwidth improvement.
Furthermore, a conformable antenna design for radio frequency-based tracking of hand gestures is also detailed. SIW-LWA is employed as the radiating element to couple RF signals into the human hand. The antenna is envisaged to be integrated in a wristband topology and capture the changes in the electric field caused by various movements of the hand. The scattering parameters are used to track the changes in the wrist anatomy. Sensor characterization showed significant sensitivity suppression due to lossy multi-dielectric nature tissues in the wrist. However, the sensor demonstrates good coupling of electromagnetic energy making it suitable for on-body wireless communications and magnetic resonance imaging applications.
ContributorsKashyap, Bharath Gundappa (Author) / Trichopoulos, Georgios C (Thesis advisor) / Balanis, Constantine A (Committee member) / Aberle, James T (Committee member) / Alkhateeb, Ahmed (Committee member) / Imani, Seyedmohammedreza F (Committee member) / Arizona State University (Publisher)
Created2023