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ÖgeTime difference of arrival based passive sensing and positioning system integrated into moving platforms(Graduate School, 2024-07-12)The ability to locate the source of information has been a fundamental need for humanity since its earliest days. This necessity even explains why living beings have evolved to have two ears: to determine the position of a sound source, which is a form of information. As sound propagates, it reaches our ears at different distances, creating an interaural time difference (ITD). The brain uses this time difference, along with the intensity of the sound, to estimate the location of the sound source. This thesis explores the principle of localization using ITD, which has existed in nature for millions of years, from the perspective of a communications engineer. Specifically, it examines the application of this principle to locate a source emitting radio frequencies. There are various methods for locating a signal source. These methods primarily rely on the signal's strength, arrival time, frequency, phase, or a combination of these factors to determine the position. The strength of the incoming signal depends directly on the terrain, the signal's frequency, and its output power. Since these variables are often unpredictable, using signal strength for positioning usually yields low accuracy results. Measuring the signal's phase requires multiple antennas and RF stages, which can only estimate the target's angle, not its precise location. However, by determining the arrival times of signals received by multiple receivers and analyzing the time differences, the position of the target can be estimated. To solve for the unknown x, y, and z coordinates in a 3D space using only time difference of arrival (TDOA) information, at least three equations are necessary. These equations provide the minimum amount of information required for position determination. However, obtaining three linearly independent TDOA equations necessitates a minimum of four receivers. Among these receivers, one is designated as the reference, and the time differences between this reference and the other three receivers are used to create linearly independent equations. These equations are then utilized to determine the target's position. However, because the equations are typically nonlinear, achieving a quick and highly accurate solution is not always straightforward. Additionally, factors such as hardware imperfections and noise can prevent a clear solution to the equation system. Various methods can be employed to address these challenges and improve the accuracy of the results. This study compares algebraic methods such as Least Squares (LS) and heuristic methods like Particle Swarm Optimization (PSO) for signal source localization. LS methods solve the system of equations directly to estimate the target position, while PSO methods optimize a target function to find the best location. Heuristic methods, including PSO, can yield effective results even with nonlinear equations or in noisy environments. In this research, we utilized a variant of the PSO algorithm known as the Firefly Algorithm. The Firefly Algorithm begins by distributing fireflies randomly across a cost function map. The fireflies move towards the solution with the lowest cost, switching to the new best fireflies as lower-cost solutions are found. This approach is advantageous for several reasons: it uses an infinite number of TDOA measurements rather than just three equations, minimizes the likelihood of getting stuck in local minima on the cost map, and achieves high-accuracy localization. Although the Firefly Algorithm requires more computational power compared to algebraic solutions, modern computers can handle this demand effectively. While signal source localization in a 3D environment using time difference of arrival (TDOA) information has often been tested with a 4-receiver system model, successful localization can also be achieved with different system models. Unlike traditional methods, where TDOA data is collected simultaneously from fixed receivers, we propose a system where two receivers are moved to collect TDOA data at different time instances, followed by localization using the collected data. Practical issues encountered with such a system model were investigated through simulation and measurement setups. One challenge was accurately estimating the time differences of arrival of signals received by the receivers. Due to the slow variation of signals in time, time estimation is affected by noise. Another potential problem arises when the sampling frequency of the system is narrower than the signal's bandwidth, causing the cross-correlation of received signals to not yield peak values at the delay samples, making time differences difficult to discern. To address this, we decided to operate the system with the highest possible sampling rate when the bandwidth of the target signal is unknown. Ensuring reliable signal sampling, both receivers are synchronized to the same frequency and time using a GPS-disciplined oscillator. Furthermore, 1 Pulse Per Second (PPS) from GPS is used for time synchronization. Apart from these technical considerations, the trajectory of the receiver stations plays a crucial role in system performance. As the distance between the target receivers increases, so does the distance they need to cover for accurate localization. Additionally, ensuring a high-reliability and high-capacity communication network between receiver stations and the base unit is crucial during system implementation. Without this network, communication disruptions between the receivers and the base station would prevent TDOA data collection and, consequently, localization algorithms from functioning. Lastly, challenges were observed when there are multiple sources emitting signals at the same frequency or when environmental factors cause signal reflections and changes in direction, affecting TDOA measurements by the receivers. Before finalizing the system setup, creating a realistic simulation environment is crucial. In the fourth section, we introduced a simulation environment designed in MATLAB to anticipate potential scenarios before the measurement setup. The simulation environment was designed to be consistent with real measurements, including terrain features using the WGS84 geolocation method. Since the system was anticipated to be tested in the TÜBİTAK-BİLGEM Gebze campus area, tests in the simulation environment were conducted accordingly. After creating the simulation environment, the first test was to examine the hyperbolas formed when different receiver paths were created. It was observed that when the target was far from the receiver paths, the hyperbolas intersected each other over a wide area. Conversely, when a receiver rotated around the target, the hyperbolas intersected each other from all directions within a small area. A small intersection area of hyperbolas is crucial for the successful operation of localization algorithms like the firefly algorithm. Secondly, the formation of hyperbolas was observed when the bandwidth of the target signal and the system's sampling frequency were reduced. With low sampling frequency, the resolution of hyperbolas was significantly reduced, spreading over a very small space. When the bandwidth dropped below a few hundred MHz, the hyperbolas generally did not pass near the target. As the sampling frequency and bandwidth increased, the hyperbolas gradually approached the target and began to intersect over it. In the third simulation, a cost function was generated for the firefly algorithm, and costs in the solution space for different receiver paths were examined. As expected, as the target moved away from the receivers, the slope of the cost function around the target decreased, allowing for wider areas to be estimated as solutions. Based on these simulations, two suitable options for receiver paths were identified for the TÜBİTAK-BİLGEM Gebze campus. Finally, the average error in position determination was investigated for different sampling frequencies of sampled target signals for the identified two paths using the firefly algorithm. As expected, errors were significantly higher at low sampling frequencies, decreasing as the sampling frequency increased. The fifth chapter illustrates the measurement setup devised from the insights gleaned from simulations, deductions, and experiences presented thus far in the thesis. It starts by delineating the hardware and software of the ground station, followed by those of the receiver units, and then narrates the measurements encompassing various scenarios. The ground station hardware comprises a simple setup, consisting of a powerful computer and a modem supporting Wi-Fi 6 for communication with the receiver unit. The user interface software enables control of the receiver units from the ground station, allowing adjustments to frequency bandwidth and gain configurations. Additionally, signals received by each unit can be individually represented in time and frequency space for adjusting gains to account for signal visibility variations. Moreover, the interface facilitates data collection, time difference calculation, and execution of the firefly algorithm, with results visualized on a map. The hardware design of the receiver unit has been the most multidisciplinary aspect, given its need for lightweight deployment on UAVs while meeting power requirements throughout the flight. The design considerations for the receiver unit include power needs for communication, computation, and GPS, with antennas strategically positioned on the UAV. Extensive efforts resulted in reducing the system weight to below 3 kilograms when integrated with the protective casing. The software running on the receiver unit operates at a lower level compared to that on the ground station, directly transferring GPS-derived position, time, and frequency information to the SDR and computer hardware, then transmitting it to the ground station via Wi-Fi 6 using the SSH interface. At the TÜBİTAK-BİLGEM Gebze campus test site, various measurements were conducted to evaluate the performance of the system hardware and software. Two receivers were mounted on drone and ground vehicle setups for different test scenarios. Different signal types and receiver paths were tested. Initially, to assess system performance under optimal conditions, a 20 MHz bandwidth high-autocorrelation M-sequence signal was transmitted from a vector signal generator, attempting to locate it with drones. Subsequently, a 20 MHz bandwidth LTE downlink signal was examined. In the third measurement, the focus shifted to existing real LTE signal sources after discontinuing the use of the signal generator. The fourth measurement pushed system boundaries by utilizing a ground vehicle and stationary receiver to locate a narrowband and intermittently available LTE uplink signal. The system performed better than expected, locating the LTE uplink signal source with a 12-meter margin of error. In the final measurement, a pulse-type modulation radar was positioned to test the system's applicability in military settings. In conclusion, this thesis demonstrated the integration of two RF receivers utilizing the TDOA principle onto drones. Simulation environments were initially created to examine system performance, followed by the implementation of the system and localization of various signal sources. These efforts illustrated that the localization accuracy varies based on the type of radio signals emitted and the trajectories followed by the drones. Moreover, the feasibility of performing localization by placing TDOA-based receivers on moving units was established.
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ÖgePower allocation for cooperative NOMA systems based on adaptive-neuro fuzzy inference system(Graduate School, 2023)Innovative technologies improving capacity, coverage, energy efficiency, and service quality are required to meet the exponentially increasing traffic demands in wireless communication systems. Non-Orthogonal Multiple Access (NOMA), which allows multiple users to transmit their data simultaneously at the same frequency and time interval, is a promising radio access technology to cope with the challenging requirements of 5G and beyond systems. However, the importance of energy efficiency in cellular networks for the NOMA becomes a major issue as the number of users increases. In a cooperative NOMA architecture, relays are effective in increasing system performance and reducing outage probability. The power allocation in a cooperative NOMA system is a challenging task having a significant impact on the user's perceived quality of service. In this thesis, a fuzzy logic (FL) based relay selection and power allocation approach are proposed for a multi-relay NOMA system with imperfect successive interference cancellation. The power is allocated between the NOMA user pair within a resource block in such a way that the rate fairness is maximized and the system outage is minimized. In order to demonstrate the effectiveness of the proposed system model, we utilize a network scenario including a base station, a variable number of relays, and two users. Relay selection and power allocation are performed using two different fuzzy inference systems (FIS). These FISs are created by training parameters such as channel coefficients, signal-to-noise ratio (SNR), and interference with the Adaptive-Neuro Fuzzy Inference System (ANFIS) method. The first FIS is designed to find relays that can achieve the minimum rate required for communication between the base station and relays. Its input parameters include the channel coefficient, SNR, self-interference, and residual interference between the base station and relays. The output of the FIS is the minimum achievable rate for the users. The second FIS is applied only for the relays that satisfy the minimum data rate requirements. The objective of the second system is to distribute the power fairly between the users. The input parameters of the second FIS are the channel coefficients, SNR, and residual interference between users and relays. The power allocation coefficient for a strong user is obtained as an output of the second FIS. The numerical results obtained by FL are close to the optimum outage probabilities and rate fairness results for all experiments when the number of relays and SNRs are varied. The computationally effective FL may be successfully applied at run time for the power allocation in a cooperative NOMA system, which gives rise to promising outcomes.
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ÖgeAntenna design for breast cancer detection and machine learning approach for birth weight prediction(Graduate School, 2024-01-03)With the advancement of technology in the biomedical field, new diagnostic and treatment methods and new devices are being developed day by day. However, although this situation seems mostly advantageous, the development of technology in some areas poses some difficulties for both patients and doctors in terms of diagnosis and treatment. For example, electromagnetic radiation used for diagnostic purposes can be harmful to patients. In addition, the precision and accuracy of the results of the techniques used also contain a margin of error, and it becomes important for doctors to consider these margins of error in the decision-making process. Based on the briefly mentioned problems, alternative methods are proposed for two different fields in this thesis. In the first study, an alternative method different from standard methods for breast cancer diagnosis will be proposed, and in the second study, machine learning approaches that can determine the baby's birth weight with high accuracy will be presented. Breast cancer remains a major global health problem and requires continuous improvements in diagnostic and control methods to achieve better patient outcomes during treatment and early detection of the disease. As breast cancer is one of the most common and dangerous diseases among women worldwide, it is therefore critical to diagnose it quickly. Considering that breast cancer is the second-leading cause of cancer-related mortality in women, the need for efficient and non-invasive diagnostic methods has become greater. The negative consequences of conventional approaches in terms of their operating principles or application methodologies give rise to this demand. In response to the limitations inherent in traditional diagnostic techniques, microwave imaging methods have been developed for effective diagnosis of breast cancer. The feasibility and efficacy of using microstrip patch antennas for breast cancer detection are especially examined in the first section of this thesis, which explores an alternative medical method. These antennas can be considered an important development in the medical industry as they are able to detect small electromagnetic oscillations that are indicative of early-stage cancer. This study introduces the design and simulation of a rectangular microstrip patch antenna on an FR-4 substrate operating at 2.45 GHz in the ISM band for breast cancer detection. Utilizing the Computer Simulation Technology (CST) software, both the proposed antenna and a five-layer breast phantom, with and without a 5 mm-radius tumor, were comprehensively designed. A breast phantom modeled as a hemisphere and an embedded tumor modeled as a sphere with different dielectric characteristics were successfully simulated. The antenna's performance was evaluated at varying distances from the phantom, revealing alterations in parameters such as electric field, return loss, voltage standing wave ratio, efficiency, specific absorption rate, etc., in the presence of a tumor. The simulation results at different antenna locations show discernible differences in values with and without tumors, indicating that a tumor significantly influences power reflection back to the antenna. The VSWR of the antenna, lower than 2, aligns with acceptable VSWR limits. Furthermore, the proposed antenna demonstrates increased electric field strength in the presence of a tumor. In addition, simulation outcomes in free space and with a 3-D breast phantom indicated that the antenna, positioned 20 mm from the breast phantom, is more efficient in tumor identification compared to the one located at 40 mm. Given its tumor detection capability and satisfactory SAR values, the proposed antenna emerges as a promising tool in biomedical applications. Future studies will explore alternative antenna geometries and techniques to enhance performance and increase tumor detection sensitivity. Birth weight is a critical indicator of both pregnancy progress and infant development, exerting a substantial influence on short- and long-term health conditions in newborns. In other words, fetal weight emerges as a pivotal indicator of short- and long-term health problems in newborns, both in developed and developing countries. Understanding the contributing factors to low birth weight (LBW) and high birth weight (HBW) can inform the implementation of optimal interventions for the population's health. In the second study, we present our research on the prediction of birth weight classification through the application of various machine learning algorithms. For this investigation, 913 medical observation units, each characterized by 19 features encompassing actual birth weight information and ultrasound measurements, were employed. In the study, a number of data preprocessing steps were performed on the data set before the data set was directly used to train the classifier models. To address the issue of imbalanced data across classes, we implemented the synthetic minority oversampling technique (SMOTE). Additionally, feature scaling was applied to standardize numerical attributes within a particular range in the dataset, as there are different physiological variables with different units and orders of magnitude. In this work, nine different machine learning classifier models are used. They are decision tree, discriminant analysis, naive bayes, support vector machine, k-nearest neighbor, kernel approximation, ensemble classifier, artificial neural network, and logistic regression. The hyperparameters of each model were kept at default values, and no hyperparameter tuning was made. To evaluate the performance of nine distinct supervised machine learning algorithms, we compared birth weight classification models with and without feature selection, utilizing numerous evaluation metrics. These different metrics are accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and area under the receiver operating curve. Referring to the Pearson correlation coefficient technique applied to the data set, abdominal circumference, head circumference, biparietal diameter, femur length, and hemoglobin levels at the 0th and 6th hours are highly correlated with birth weight. The results of our analysis highlight that the subspace kNN-based ensemble classifier outperforms other machine learning models, achieving the best macro-average accuracy of 99.87% without feature selection and 99.75% with feature selection. Additionally, we observed that the bilayered neural network exhibits similar performance to the kNN-based model, with the best macro-average accuracy of 99.62%, irrespective of feature selection. Furthermore, principal component analysis (PCA) was applied to the data set as an unsupervised method for birth weight classification. The outcome clearly demonstrates the successful classification of most data points by PCA. The findings of this study underscore the potency of machine learning as a robust and non-invasive method for accurately predicting the birth weight classification of infants. In light of these factors, a health program could be devised to prevent the occurrence of LBW and HBW since recognition of LBW or HBW in a newborn may signal potential problems that could manifest immediately after birth or later in life. At the end of the thesis, performance improvement methods have been proposed based on the two studies we conducted, and we hope that the results of our research will shed light on future studies.
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ÖgeMeasurement-based antenna misalignment analysis and angle of arrival estimation for terahertz wireless communication systems(Graduate School, 2023-09-07)As the demand for instant information and faster data transmission rates increases, the bandwidth requirements of wireless communication systems are predicted to exceed the capabilities of current millimeter-wave (mm-Wave) systems. To address this need, Terahertz (THz) wireless communication systems have emerged as a promising option for 6G and future wireless systems, offering a large contiguous bandwidth in the range of 0.1 THz - 10 THz that is applicable for both indoor and outdoor communication. However, the implementation of THz communication systems presents challenges due to substantial propagation losses, molecular absorption, and the effects of antenna misalignment on system performance. This master's thesis focuses on addressing the aforementioned challenges in THz communication systems. Our primary objective is to analyze the effects of antenna misalignment on system performance. To achieve this, we have designed and implemented a comprehensive measurement system capable of accurately characterizing the impact of misalignment on THz communication channels. By conducting extensive experiments and measurements, we aim to quantify the degradation in system performance caused by antenna misalignment and establish a thorough understanding of the underlying mechanisms. Furthermore, we aim to develop novel angle of arrival (AoA) estimation techniques specifically tailored for THz communication systems. These techniques will leverage advanced signal processing algorithms and innovative antenna array designs to accurately estimate the arrival angles of incoming signals, even in the presence of misalignment. By improving the accuracy of AoA estimation, we anticipate significant advancements in beamforming, spatial multiplexing, and other key aspects of THz system design. Through our research efforts, we strive to contribute to the development of more efficient and reliable THz wireless communication systems for future generations. By mitigating the impact of antenna misalignment and enhancing the accuracy of AoA estimation, we envision THz systems that can achieve higher data rates, improved coverage, and enhanced overall system performance. This work has the potential to revolutionize wireless communication and pave the way for the seamless integration of THz technology into various applications, including 6G networks, ultra-fast wireless links, and high-capacity communication systems. As a result, this master's thesis examines the potential of the THz band for wireless communication systems in the face of growing demand for data capacity in mobile networks. The study highlights the limitations of conventional frequency spectrums, such as the mm-Wave systems, and demonstrates how the THz band can overcome these limitations. The importance of comprehensive measurement campaigns and new algorithms to estimate the AoA and address antenna misalignment in THz wireless communication systems is emphasized. The thesis proposes a new algorithm, the AoSA-gold-MUSIC, which is designed specifically for THz-enabled space information networks (SINs) and is able to estimate AoA accurately while being computationally efficient. The analysis of channel impulse and frequency responses from the measurement campaign provides valuable insights into the behavior of electromagnetic waves in different scenarios and shows how the THz band could pave the way for next-generation wireless communication systems with disruptive metrics. These metrics include data rates of up to 100 Gbps, latency as low as 0.1 ms, and high spectrum efficiency.
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ÖgeA compact two stage GaN power amplifier design for sub-6GHz 5G base stations(Graduate School, 2023)Both commercial and military systems use wireless communication networks. The range of applications is wide, including radar, mobile communications, Wi-Fi, SATCOM and many more. They all have different requirements and different solutions to meet their needs. The development of mobile communications began with 1G in the 1970s, and new generations have found their place in the radio communications market. In 2019, 5G New Radio has started to be expanded worldwide with higher data rate, wider frequency bands, lower latency features. Moreover, there are more frequency bands are available for 5G New Radio. These are called sub-6GHz and mmWave. As the name suggests, the sub-6 GHz frequency bands are below the 6 GHz frequency bands, including the bands of the previous generation. On the other hand, mmWave frequency bands are above 24 GHz. With the goal of low latency, engineers are developing new solutions for the next generation of base stations. One solution is to deploy smaller base stations more frequently than traditional macro base stations. These small cell base stations are called Micro, Pico, Femtocells. As the size of base stations has decreased, the transmitters and receivers of the cells require new technological developments. As the transmitters contain power amplifiers, they are known to dissipate significant amounts of DC power and require appropriate thermal protection. Also, with the increasing demand for small cells, the size of the transmitters must also be considered, along with the nuisance of heat. One of the most important component of the transmitters is power amplifiers. They are the last element of the transmitter before the antenna and amplify the RF power using DC power. In this work, the power amplifier is studied. The size of the power amplifiers play important role for the 5G New Radio small base station cells. Also, due to the size of power amplifiers being small, the power density and thermal conductivity managements are examined. GaN transistors gained popularity over GaAs and Si semiconductor technologies since their thermal conductivity is better and their power density is higher. They are also capable of amplifying higher power levels and have broader bandwidths. For these reasons a compact GaN HEMT power amplifier module is designed to meet the requirements of 5G small cell base stations. For thermal reasons, the efficiency of the power amplifier is crucial. The traditional power amplifiers are divided into classes that is determined by their bias points. These are Class A, Class B, Class C and Class AB. Class A is theoretically the least efficient and Class C is the most efficient. Also, the linearity is important factor in telecommunications because of complex modulation systems. Class A is the most linear and Class C is the least linear of all classes. As a result of this compromise, our power amplifier module operates in Class AB, which balances efficiency and linearity. In this work, a compact two-stage power amplifier module is designed with high gain, high linearity and high efficiency. 2 bare die GaN HEMT transistors are used with 0201 packaged lumped components for matching circuits on a laminate PCB. The PA module measures 10x6 mm. Given these dimensions, the alternative design option is MMIC technology, but the cost of a GaN-based wafer is significantly higher than our solution. A large signal model of the transistor is used and simulated with the EM co-simulation. The simulations are resulted as the output power level of 5W with 0.1 dB gain compression at the center frequency 3.5 GHz. The stability of the PA module is secured with series resistors. The designed power amplifier module is manufactured and implemented with the die transistors and components by using die bonder and wire bonder machines. Small signal and large signal measurement setups are prepared and the device is tested. Due to the mesh settings the designed power amplifiers matching circuits are shifted. 18.5 dB gain is measured with 30% PAE at the output power level of 2W. The simulations are repeated with accurate EM simulations and the results are matched.