Browsing by Author "Torlak, Murat"
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Item Algorithms for Millimeter Wave Imaging and Sensing(2017-05) Patole, Sujeet; Torlak, Murat“Millimeter waves” have enabled potential solutions to key problems in the field of security scanning, medical imaging, automotive radars and next generation wireless communication systems. These applications have become possible due to the advancements in semiconductor technology, permitting inexpensive and densely packed millimeter wave circuits. Imaging and locating targets of interest with millimeter and terahertz waves have experienced dramatic performance improvement due to the higher bandwidth and smaller size of antenna elements at millimeter wave frequencies. This dissertation develops novel signal processing algorithms to improve the performance of millimeter wave imaging and sensing systems. A two dimensional beamforming based millimeter wave imaging technique is formulated, which can reconstruct targets in the near field of an antenna array. The technique shows improved performance over existing switched array imaging. Besides the algorithmic development, a mathematical analysis is performed to investigate the sensitivity of the proposed imaging technique in the presence of phase errors. The later part of this dissertation concentrates on imaging of targets in the far field of an imaging antenna array. Novel algorithms are designed to localize targets jointly across multiple dimensions using superresolution techniques. For the real time implementation of these algorithms, the emphasis is given on the computational cost reduction. Finally, the low complexity far field imaging algorithms are validated with electromagnetic simulators as well as with 24 GHz radar testbed built at The University of Texas at Dallas. Details of radar testbed prototyping and antenna design are also presented.Item Enabling Massive Device Connectivity in 5G Cellular Networks(2018-05) Li, Ting; Torlak, MuratWith the introduction of Internet of Things (IoT), millions of devices are expected to be connected to wireless networks to provide diverse types of services including remote con- trol, surveillance, detection, sensing, etc. As a result, supporting massive connectivity with various functionalities becomes an essential task in building the future cellular networks. Mo- tivated to fulfill such a demand, this dissertation consisting of two parts investigate the roles of different advanced wireless communication techniques on enabling massive connectivity for 5G cellular networks. In the first part (Chapters 2 and 3), random access, spectrum sensing and cognitive radio (CR) are combined to solve the network overloading and packet scheduling issues in nar- rowband (NB), low data rate and high delay tolerance cellular networks. Optimal sensing parameters are derived to maximize the network throughput for different sensing mecha- nisms. Trade-offs between the random access narrowband cognitive radio (NB-CR) network throughput and the sensing accuracy under different sensing environments are also investi- gated. In the second part (Chapters 4 and 5), Massive MIMO technique is employed to support massive connectivity in broadband, high data rate and real-time networks. A single-carrier system architecture applying spatial temporal zero forcing linear equalizers (ST-ZF-LE) is proposed to mitigate frequency-selective fading in Massive MIMO systems. A quantifica- tional analysis is presented to reveal the relationships between system parameters and per- formance in terms of degrees of freedom (DoFs) and signal to interference plus noise ratio (SINR). Closed-form expressions of the ergodic data rate and the outage probability are then derived for both single-cell systems and multi-cell systems with perfect and imperfect channel state information (CSI).Item Enhancing Energy Efficiency and Ranging Accuracy in IoT Networks(May 2023) Helwa, Sherief Mohammad Salaheldin 1989-; Al-Dhahir, Naofal; Ryu, Ill; Fonseka, John P.; Heins, Matthew; Torlak, MuratWe consider an IoT network and try to address some of the issues and limitations of this new wireless system paradigm. Some of the issues come from the design requirements of IoT devices such as low cost, low energy consumption, and extended battery lifetime. These requirements limit the ability of such devices to operate at large bandwidth or use complex receiver circuitry that consume high energy. We focus on these two problems and investigate possible solutions to overcome such issues. We address the problem of receiver power consumption by introducing the usage of low-resolution Analog to Digital Converters (ADCs) under Differential-Phase Shift Keying (D-PSK) modulation. Outage-constrained receiver energy efficiency is then used as our metric to ensure low power consumption while operating at reasonable rates which are reflected in achieving low transmission latency values. Additionally, the effects of bandwidth limitations on ranging accuracy are taken into consideration. The idea of Channel Frequency Response (CFR) stitching is applied to expand the bandwidth, where two-way CFR is introduced to ensure CFR coherency upon stitching. Two-way CFR is further studied in details highlighting its advantages and disadvantages. Moreover, two alternative approaches are proposed to overcome two-way CFR’s accuracy degradation drawback by working with one-way response instead. The first techniques is a two-way to one-way CFR conversion where we apply signal processing techniques to detect and correct any phase errors in the CFR after conversion. While the second approach utilizes the novel idea of frequency overlap-based CFR alignment to mitigate the system’s impairments we experience while operating with the one-way approach. Significant ranging accuracy gains were achieved by the proposed techniques and verified by means of accurate system simulation as well as hardware prototyping.Item Exact Outage Probability Analysis of Dual-Transmit-Antenna V-BLAST with Optimum Ordering(IEEE-Institute of Electrical Electronics Engineers Inc, 2018-11-09) Ozyurt, Serdar; Torlak, Murat; Torlak, MuratAn exact outage probability analysis of zero-forcing V-BLAST technique with two transmit antennas and r receive antennas (r ≥ 2) is provided under a decoding order that is optimum in the sense of minimizing the outage probability. Different from the earlier approximated analyses, an exact closed-form expression on the outage probability is attained without neglecting the effect of error propagation. Assuming a one-bit feedback from the receiver to the transmitter, the optimal power and rate allocation values are numerically computed based on the exact outage probability expression under different objective functions. The presented results are also applicable for the corresponding dual scenarios that may involve antenna selection and/or user scheduling.Item Impact of Number of Noise Eigenvectors Used on the Resolution Probability of MUSIC(Institute of Electrical and Electronics Engineers Inc, 2019-01-31) Baral, Ashwin Bhobani; Torlak, Murat; 0000-0001-7229-1765 (Torlak, M); 0000-0002-5110-7727 (Baral, AB); Baral, Ashwin Bhobani; Torlak, MuratThe MUltiple SIgnal Classification (MUSIC) algorithm is a well-known eigenanalysis technique and has been studied extensively. The algorithm relies on accurate partitioning of the eigenvectors of the spatial correlation matrix between the signal (i.e., signal subspace) and noise eigenvectors (i.e., noise subspace). In this paper, we present a novel statistical framework for analyzing the resolution performance of the MUSIC algorithm in resolving closely spaced sources. The statistical framework is based on the first-order approximation of the perturbations in the noise subspace eigenvectors. Using this framework, we derive an analytical expression for the probability of resolution of the MUSIC algorithm according to the number of noise eigenvectors used in the spectrum computation. Such an investigation cannot be carried out with the existing probability of resolution expressions of the MUSIC algorithm. Using the analytical tools presented in this paper, it is possible to predict the resolution performance with respect to many important system parameters, i.e., signal-to-noise ratio (SNR), the number of samples, and the number of noise eigenvectors. For example, we found that the resolution threshold in terms of SNR is independent of the number of noise eigenvectors used. The simulation results are presented to verify the accuracy of the analytical expressions. More importantly, real radio-frequency experiments with a 24-GHz radar platform are carried out to demonstrate the resolution performance of MUSIC to support our findings in practical settings.Item Millimeter-Wave Imaging Using MIMO-SAR Techniques(2020-05) Yanik, Muhammet Emin; 0000-0001-8682-4577 (Yanik, ME); Torlak, MuratThere is a strong desire to exploit the non-ionizing millimeter-wave (mmWave) spectrum (from 30 GHz to 300 GHz) in many high-resolution imaging applications ranging from medical to security. The primary challenge of a cost-effective and low-complexity mmWave imaging system is to achieve high-resolution with as few antenna elements as possible. Multiple input multiple-output (MIMO) radars using the simultaneous operation of spatially diverse transmit and receive antennas are good candidates to meet this challenge. On the other hand, higher integration complexity of extremely dense transceiver electronics limits the use of MIMO-only solutions within a relatively large imaging aperture. Hybrid concepts combining synthetic aperture radar (SAR) techniques and MIMO arrays present a great compromise to achieve short data acquisition time and low-complexity. Despite numerous studies that apply MIMO concepts to SAR techniques, the design process of a MIMO-SAR system is non-trivial, especially for mmWave imaging. Many issues have to be carefully addressed. Besides, compared with conventional monostatic sampling schemes, where the measurements are taken by collocated transmit and receive antennas, or MIMO-only solutions, efficient image reconstruction methods for MIMO-SAR topologies are more complicated in short-range applications. This dissertation introduces a complete mmWave imaging solution combining wideband MIMO arrays with SAR techniques, along with computationally efficient novel image reconstruction algorithms optimized for MIMO-SAR configurations. We present highly-integrated and reconfigurable MIMO-SAR testbeds utilizing commercially available complementary metal-oxide semiconductor (CMOS) based system-on-chip wideband MIMO mmWave sensors and motorized rail platforms. Several aspects of the MIMO-SAR testbed design process, including MIMO array calibration, electrical/mechanical synchronization, system-level verification, and performance evaluation, are described. The proposed algorithms are verified by both simulation and processing real data collected with custom-built imaging testbeds. The results confirm that our complete solution presents a strong potential in high-resolution imaging tasks of various real-world applications.Item Multi-antenna Millimeter-wave Radars: Algorithms and Performance Analysis(May 2023) Baral, Ashwin Bhobani 1989-; Torlak, Murat; Fox, Emily; Al-Dhahir, Naofal; Henderson, Rashaunda; Fonseka, John P.Millimeter-wave (mmWave) radars with multi-antenna systems have become popular in numerous automotive and industrial sensing applications. For these applications, target estimation is a crucial function. However, accurately estimating a target’s parameters becomes challenging due to either the limitations of the system parameters or the presence of clutter and interference. To address these challenges, this dissertation focuses on developing robust signal processing algorithms and studying their performance analyses. Direction of arrival (DOA) estimation of a target is a fundamental problem for radar sensors. Super-resolution algorithms like MUltiple SIgnal Classification (MUSIC) have been proposed for better DOA estimation performance compared to classical approaches. MUSIC relies on accurate partitioning of the eigenvectors of the spatial correlation matrix between the signal eigenvectors (i.e., signal subspace) and noise eigenvectors (i.e., noise subspace). In the first part of this dissertation, we present a novel statistical framework for analyzing the resolution performance of the MUSIC algorithm in resolving two closely spaced targets according to the number of noise eigenvectors used in the spectrum evaluation. Using this framework, we derive an analytical expression for the probability of resolution of the MUSIC algorithm. Multiple-input multiple-output (MIMO) radar achieves high angular resolution at the expense of certain limitations of its systems realized using different multiplexing techniques such as time division multiplexing (TDM), frequency division multiplexing (FDM), and code division multiplexing (CDM). Of all these multiplexing techniques, TDM is the appropriate choice for automotive applications due to its low hardware complexity. The latter part of this dissertation focuses on the Doppler ambiguity problem associated with a standard TDM MIMO radar. In a standard TDM MIMO radar, transmitters are activated sequentially according to their natural spatial order. The drawback of the standard TDM MIMO approach is the coupling of velocity and DOA information of the targets. The coupling reduces the unambiguous estimation interval of the Doppler frequencies of the targets by the number of transmit antennas being multiplexed. To solve this problem, we propose a novel cost function for jointly estimating the Doppler frequency and DOA of the targets. In the last part of this dissertation, we address the mutual interference problem between automotive radars. With the increasing demand for radar sensors in automotive applications, this mutual interference between them is inevitable due to their unregulated transmissions. To reliably estimate the target’s parameters, this interference needs to be detected and mitigated. To mitigate automotive interference, we propose a two-stage signal decomposition approach.Item Near-Field 2-D SAR Imaging by Millimeter-Wave Radar for Concealed Item Detection(IEEE, 2019-01-20) Yanik, Muhammet Emin; Torlak, Murat; 0000-0001-7229-1765 (Torlak, M); Yanik, Muhammet Emin; Torlak, MuratRecent progress in complementary metaloxide semiconductor (CMOS) based frequency-modulated continuous-wave (FMCW) radars has made it possible to design low-cost and low-power millimeter-wave (mmWave) sensors. As a result, there is a strong desire to exploit the progress in mmWave sensors in wide range of imaging applications including medical, automotive, and security. In this paper, we present a low-cost high-resolution mmWave imager prototype that combines commercially available 77 GHz system-on-chip FMCW radar sensors and synthetic aperture radar (SAR) signal processing techniques for concealed item detection. To create a synthetic aperture over a target scene, the imager is constructed with a two-axis motorized rail system which can synthesize a large aperture in both horizontal and vertical directions. Our prototype system is described in detail along with signal processing techniques for two-dimensional (2-D) image reconstruction. The imaging examples of concealed items in various scenarios confirm that our low-cost prototype has a great potential for high-resolution imaging tasks in security applications.Item Near-Field MIMO-SAR Millimeter-Wave Imaging With Sparsely Sampled Aperture Data(Institute of Electrical and Electronics Engineers Inc., 2019-03-04) Yanik, Muhammet Emin; Torlak, Murat; 0000-0001-8682-4577 (Yanik, ME); 0000-0001-7229-1765 (Torlak, M); Yanik, Muhammet Emin; Torlak, MuratThe primary challenge of a cost-effective and low-complexity near-field millimeter-wave (mmWave) imaging system is to achieve high resolution with a few antenna elements as possible. Multiple-input multiple-output (MIMO) radar using simultaneous operation of spatially diverse transmit and receive antennas is a good candidate to increase the number of available degrees of freedom. On the other hand, higher integration complexity of extremely dense transceiver electronics limits the use of MIMO only solutions within a relatively large imaging aperture. Hybrid concepts combining synthetic aperture radar (SAR) techniques and sparse MIMO arrays present a good compromise to achieve short data acquisition time and low complexity. However, compared with conventional monostatic sampling schemes, image reconstruction methods for MIMO-SAR are more complicated. In this paper, we propose a high-resolution mmWave imaging system combining 2-D MIMO arrays with SAR, along with a novel Fourier-based image reconstruction algorithm using sparsely sampled aperture data. The proposed algorithm is verified by both simulation and processing real data collected with our mmWave imager prototype utilizing commercially available 77-GHz MIMO radar sensors. The experimental results confirm that our complete solution presents a strong potential in high-resolution imaging with a significantly reduced number of antenna elements. © 2019 IEEE.Item Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging Systems(2022-05-01T05:00:00.000Z) Smith, Josiah; Torlak, Murat; Khan, Latifur; Busso-Recabarren, Carlos A.; Lehmann, Randall E.; Al-Dhahir, NaofalIncreasing attention is being paid to millimeter-wave (mmWave), 30 GHz to 300 GHz, and terahertz (THz), 300 GHz to 10 THz, sensing applications including security sensing, in- dustrial packaging, medical imaging, and non-destructive testing. Traditional methods for perception and imaging are challenged by novel data-driven algorithms that offer improved resolution, localization, and detection rates. Over the past decade, deep learning technology has garnered substantial popularity, particularly in perception and computer vision appli- cations. Whereas conventional signal processing techniques are more easily generalized to various applications, hybrid approaches where signal processing and learning-based algo- rithms are interleaved pose a promising compromise between performance and generalizabil- ity. Furthermore, such hybrid algorithms improve model training by leveraging the known characteristics of radio frequency (RF) waveforms, thus yielding more efficiently trained deep learning algorithms and offering higher performance than conventional methods. This dissertation introduces novel hybrid-learning algorithms for improved mmWave imaging systems applicable to a host of problems in perception and sensing. Various problem spaces are explored, including static and dynamic gesture classification; precise hand localization for human computer interaction; high-resolution near-field mmWave imaging using forward synthetic aperture radar (SAR); SAR under irregular scanning geometries; mmWave image super-resolution using deep neural network (DNN) and Vision Transformer (ViT) archi- tectures; and data-level multiband radar fusion using a novel hybrid-learning architecture. Furthermore, we introduce several novel approaches for deep learning model training and dataset synthesis. Depending on the application, a varying balance of classical signal pro- cessing techniques and deep learning is applied to optimally leverage the advantages of each technique. To verify the proposed algorithms, we employ virtual prototyping via simula- tion and develop custom-built imaging testbeds for empirical testing. Our custom tools for algorithm development, dataset generation, system-level design, and deployment are made public to promote further innovation in this arena. The simulation and experimental results demonstrate the wide application space of hybrid-learning algorithms and the efficacy of joint signal processing data-driven algorithms for radar sensing, perception, and imaging.Item Optimization of interference alignment beamforming vectors(The University of Texas at Dallas, 2013-05-24) Kim, Douglas E.; Torlak, Murat; Eric Jonsson School of Engineering and Computer Science.Interference alignment, while optimum in its achievement of the maximum degrees of freedom for the K user interference channel, does so only at high SNR and for large numbers of dimensions over which to align the interference, n. A sizable SNR penalty is paid in order to approach the theoretical outerbound and only grows increasingly higher for larger n. For the single antenna, K = 3 interference channel, an efficient means of drastically reducing the required power to approach the outerbound of 3/2 is presented. By no longer using a vector of all ones in the creation of the transmit beamforming vectors as originally proposed, a new weighted vector w is designed in order to distribute the power across the precoding vectors more evenly. Furthermore, we introduce a new structure of beamforming vectors that also provides significant savings in SNR independently of vector w. By doing so, our proposed designs achieve the same degrees of freedom of the original scheme at only a fraction of the SNR.Item Performance Analysis and Techniques for Wireless Systems with Unmanaged and Managed Interference(2017-12) Ali, Ahmed Omar Desouky; 0000-0001-7229-1765 (Torlak, M); Torlak, MuratIn the past few decades, the world has witnessed enormous advances in wireless communication systems introducing unprecedented applications and services. Such advances brought the idea of Internet of Things (IoT) to life, where almost every object is interconnected to each other through the existing Internet infrastructure. This results in enormous increase in the number of wireless devices sharing the same medium and bandwidth in different networks. Consequently, interference generated by such devices is expected to increase drastically, and hence innovative techniques to mitigate such interference have to be investigated. In this dissertation, we investigate the interference in wireless communication networks. The dissertation is divided into two parts dealing with unmanaged and managed interference, respectively. In the first part, we investigate the second order statistics of a system consisting of a single receiver with multiple antennas, a single desired user and multiple interferers. Specifically, we derive exact closed-form level-crossing rate (LCR) expressions for such a system under different spatial correlation assumptions which, to the best of our knowledge, are reported for the first time in literature. We also derive an approximate LCR expression in the spatially uncorrelated receiver to get better insight. Moreover, we investigate the effect of the different system’s parameters on the LCR. Then, we introduce two applications where in the first we use these new expressions to evaluate the system’s packet error rate (PER). The second application uses the derived LCR expressions in Finite-State Markov Chain (FSMC) modeling to evaluate the system’s throughput when deploying Automatic Repeat Request (ARQ). We also formulate an optimization problem where we find the packet length maximizing the system’s throughput. In the second part of the dissertation, we deal with managed interference where we use joint transmit and receive beamforming to mitigate the interference for frequency selective fading channels. In particular, we propose exploiting the guard intervals, e.g., the cyclic prefix or the zero-padding intervals, to increase the degrees of freedom (DoFs) used, and consequently the sum rate of the system through applying interference alignment (IA) iterative schemes. We derive an upper bound on the number of allowable DoFs per user and evaluate the system’s sum rate and compare it with other IA schemes reported in literature. We also investigate the effect of partial channel state information (CSI) on the performance of such a system and propose a robust beamforming transceiver design. We also derive an approximate expression for the system’s average sum stream rate when exploiting the guard interval in transmission in presence of CSI error. Furthermore, we attempt to address the question whether it is better to align the interference or orthogonalize the transmitted signals through investigating an uplink cellular system deploying IA in presence of CSI uncertainty. In this system, we derive closed-form lower and upper bounds and approximation for the average cell rate and use the derived expressions to determine analytically the operation regions of IA relative to orthogonal multiple access (OMA) schemes. Finally, we propose a hybrid IA/OMA transmission scheme to improve the network performance.