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N.B. Onat
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1
Conference paper
(2025)
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T. Dash, N. B. Onat, Y. Aslan, A. Yarovoy
The reconstruction of Embedded Element Patterns (EEP) in an array of antennas is presented as an attempt to understand the effects of Mutual Coupling (MC) among the antenna elements. The EEP far fields are modeled with a weighted sum of orthonormal spherical harmonic basis functions (explained by their mode numbers). The weight of each significantly contributing mode is estimated by the Singular Value Decomposition (SVD) approach. A practical example of a non-uniform array of circular patch antennas is considered for analyzing the significant contributing modes. Although the values of the weights of the modes are different for each antenna element in the array, the contributing modes remain very similar for all the elements, indicating that the mutual coupling can be sufficiently explained by a limited number of variables (weights) corresponding to limited spherical harmonic modes. The reconstructed patterns are validated by comparing them with full-wave simulation results.
...
The reconstruction of Embedded Element Patterns (EEP) in an array of antennas is presented as an attempt to understand the effects of Mutual Coupling (MC) among the antenna elements. The EEP far fields are modeled with a weighted sum of orthonormal spherical harmonic basis functions (explained by their mode numbers). The weight of each significantly contributing mode is estimated by the Singular Value Decomposition (SVD) approach. A practical example of a non-uniform array of circular patch antennas is considered for analyzing the significant contributing modes. Although the values of the weights of the modes are different for each antenna element in the array, the contributing modes remain very similar for all the elements, indicating that the mutual coupling can be sufficiently explained by a limited number of variables (weights) corresponding to limited spherical harmonic modes. The reconstructed patterns are validated by comparing them with full-wave simulation results.
Doctoral thesis
(2025)
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N.B. Onat, Alexander Yarovoy , Y. Aslan
The rapid advancement of next-generation wireless communication and sensing technologies demands high-performance, intelligent antenna systems capable of adaptive beamforming, efficient signal control, and reliable operation under dynamic conditions. Active phased arrays, especially non-uniform (aperiodic) layouts, offer flexibility for power-efficient side-lobe control and improved spatial coverage, but at the cost of increased electromagnetic, thermal, and computational complexity. Practical IC-integrated aperiodic arrays therefore require innovative design and diagnostic frameworks that efficiently address challenges across pre- and post-manufacturing stages.
Traditionally, design and performance evaluation of active phased arrays have relied on deterministic electromagnetic simulations or optimization-based methods focusing on radiation characteristics such as gain, beamwidth, and side-lobe levels (SLL). As arrays grow more complex, such methods face limitations in scalability and computational feasibility. Machine learning (ML) offers a pathway to overcome these barriers by enabling data-driven modeling, optimization, and diagnosis of large-scale or non-uniform active arrays. This interdisciplinary challenge lies at the intersection of electromagnetics (EM), array modeling, and data-driven methods, requiring frameworks that balance physical accuracy with computational efficiency.
The objective of this doctoral research is to develop and validate ML-based methodologies that enhance both the design and operation of active arrays, emphasizing efficient modeling, synthesis, and diagnosis under mutual coupling (MC), environmental effects, and fabrication-induced uncertainties. The research addresses both pre-manufacturing (e.g., synthesis, EM modeling, and topology optimization) and post-manufacturing (e.g., calibration and fault detection) aspects of active arrays, with a focus on data-efficient and physics-supported ML techniques.
Chapter II introduces the motivation, state-of-the-art challenges in phased array systems, and the potential of ML in overcoming these limitations. A detailed analysis highlights critical issues of mutual coupling, irregular routing, calibration complexity, and environmental sensitivity, establishing the need for intelligent, adaptive frameworks that integrate ML with physical modeling to enhance design reliability and post-deployment robustness.
Chapter III presents data-driven modeling of EM interactions in aperiodic phased arrays. A neural network (NN)-based framework predicts embedded element patterns (EEPs) across the visible space for non-uniform planar arrays. The cascaded NN architecture, combining a fully connected NN and a sub-pixel convolutional network, enables high-resolution pattern prediction with significant computational savings. The influence of dataset size and quality on prediction reliability is analyzed, revealing insights into data efficiency and model generalization.
Chapter IV introduces hybrid ML-physics approaches to improve prediction robustness. Two basis-function-assisted frameworks are developed using the Infinitesimal Dipole Model (IDM) and spherical harmonics. These models achieve compact, reliable EEP representation with smaller datasets while mitigating numerical instability. An ensemble method combining NN-based EEP prediction and constrained IDM achieves a 60% reduction in mean squared error (MSE) and improved prediction stability under MC effects.
Chapter V applies these ML-assisted models to system-level synthesis and diagnostics. A novel ML-driven optimization method for MIMO radar arrays integrates spherical harmonics-based EEP prediction into a particle swarm optimization (PSO) routine, enabling efficient MC-aware array topology design and minimizing maximum SLL in multi-beam configurations. ML-based post-manufacturing diagnostics are demonstrated for a 64-element active uniform array, where a fully connected NN detects faulty elements in real time using sparse far-field amplitude data.
The findings demonstrate that ML can bridge the gap between EM theory and practical array implementation. The developed frameworks offer data-efficient alternatives for MC-aware active array optimization and system diagnostics, paving the way toward intelligent, reliable, and self-adaptive active array systems for future wireless communication and sensing applications.
...
Traditionally, design and performance evaluation of active phased arrays have relied on deterministic electromagnetic simulations or optimization-based methods focusing on radiation characteristics such as gain, beamwidth, and side-lobe levels (SLL). As arrays grow more complex, such methods face limitations in scalability and computational feasibility. Machine learning (ML) offers a pathway to overcome these barriers by enabling data-driven modeling, optimization, and diagnosis of large-scale or non-uniform active arrays. This interdisciplinary challenge lies at the intersection of electromagnetics (EM), array modeling, and data-driven methods, requiring frameworks that balance physical accuracy with computational efficiency.
The objective of this doctoral research is to develop and validate ML-based methodologies that enhance both the design and operation of active arrays, emphasizing efficient modeling, synthesis, and diagnosis under mutual coupling (MC), environmental effects, and fabrication-induced uncertainties. The research addresses both pre-manufacturing (e.g., synthesis, EM modeling, and topology optimization) and post-manufacturing (e.g., calibration and fault detection) aspects of active arrays, with a focus on data-efficient and physics-supported ML techniques.
Chapter II introduces the motivation, state-of-the-art challenges in phased array systems, and the potential of ML in overcoming these limitations. A detailed analysis highlights critical issues of mutual coupling, irregular routing, calibration complexity, and environmental sensitivity, establishing the need for intelligent, adaptive frameworks that integrate ML with physical modeling to enhance design reliability and post-deployment robustness.
Chapter III presents data-driven modeling of EM interactions in aperiodic phased arrays. A neural network (NN)-based framework predicts embedded element patterns (EEPs) across the visible space for non-uniform planar arrays. The cascaded NN architecture, combining a fully connected NN and a sub-pixel convolutional network, enables high-resolution pattern prediction with significant computational savings. The influence of dataset size and quality on prediction reliability is analyzed, revealing insights into data efficiency and model generalization.
Chapter IV introduces hybrid ML-physics approaches to improve prediction robustness. Two basis-function-assisted frameworks are developed using the Infinitesimal Dipole Model (IDM) and spherical harmonics. These models achieve compact, reliable EEP representation with smaller datasets while mitigating numerical instability. An ensemble method combining NN-based EEP prediction and constrained IDM achieves a 60% reduction in mean squared error (MSE) and improved prediction stability under MC effects.
Chapter V applies these ML-assisted models to system-level synthesis and diagnostics. A novel ML-driven optimization method for MIMO radar arrays integrates spherical harmonics-based EEP prediction into a particle swarm optimization (PSO) routine, enabling efficient MC-aware array topology design and minimizing maximum SLL in multi-beam configurations. ML-based post-manufacturing diagnostics are demonstrated for a 64-element active uniform array, where a fully connected NN detects faulty elements in real time using sparse far-field amplitude data.
The findings demonstrate that ML can bridge the gap between EM theory and practical array implementation. The developed frameworks offer data-efficient alternatives for MC-aware active array optimization and system diagnostics, paving the way toward intelligent, reliable, and self-adaptive active array systems for future wireless communication and sensing applications.
...
The rapid advancement of next-generation wireless communication and sensing technologies demands high-performance, intelligent antenna systems capable of adaptive beamforming, efficient signal control, and reliable operation under dynamic conditions. Active phased arrays, especially non-uniform (aperiodic) layouts, offer flexibility for power-efficient side-lobe control and improved spatial coverage, but at the cost of increased electromagnetic, thermal, and computational complexity. Practical IC-integrated aperiodic arrays therefore require innovative design and diagnostic frameworks that efficiently address challenges across pre- and post-manufacturing stages.
Traditionally, design and performance evaluation of active phased arrays have relied on deterministic electromagnetic simulations or optimization-based methods focusing on radiation characteristics such as gain, beamwidth, and side-lobe levels (SLL). As arrays grow more complex, such methods face limitations in scalability and computational feasibility. Machine learning (ML) offers a pathway to overcome these barriers by enabling data-driven modeling, optimization, and diagnosis of large-scale or non-uniform active arrays. This interdisciplinary challenge lies at the intersection of electromagnetics (EM), array modeling, and data-driven methods, requiring frameworks that balance physical accuracy with computational efficiency.
The objective of this doctoral research is to develop and validate ML-based methodologies that enhance both the design and operation of active arrays, emphasizing efficient modeling, synthesis, and diagnosis under mutual coupling (MC), environmental effects, and fabrication-induced uncertainties. The research addresses both pre-manufacturing (e.g., synthesis, EM modeling, and topology optimization) and post-manufacturing (e.g., calibration and fault detection) aspects of active arrays, with a focus on data-efficient and physics-supported ML techniques.
Chapter II introduces the motivation, state-of-the-art challenges in phased array systems, and the potential of ML in overcoming these limitations. A detailed analysis highlights critical issues of mutual coupling, irregular routing, calibration complexity, and environmental sensitivity, establishing the need for intelligent, adaptive frameworks that integrate ML with physical modeling to enhance design reliability and post-deployment robustness.
Chapter III presents data-driven modeling of EM interactions in aperiodic phased arrays. A neural network (NN)-based framework predicts embedded element patterns (EEPs) across the visible space for non-uniform planar arrays. The cascaded NN architecture, combining a fully connected NN and a sub-pixel convolutional network, enables high-resolution pattern prediction with significant computational savings. The influence of dataset size and quality on prediction reliability is analyzed, revealing insights into data efficiency and model generalization.
Chapter IV introduces hybrid ML-physics approaches to improve prediction robustness. Two basis-function-assisted frameworks are developed using the Infinitesimal Dipole Model (IDM) and spherical harmonics. These models achieve compact, reliable EEP representation with smaller datasets while mitigating numerical instability. An ensemble method combining NN-based EEP prediction and constrained IDM achieves a 60% reduction in mean squared error (MSE) and improved prediction stability under MC effects.
Chapter V applies these ML-assisted models to system-level synthesis and diagnostics. A novel ML-driven optimization method for MIMO radar arrays integrates spherical harmonics-based EEP prediction into a particle swarm optimization (PSO) routine, enabling efficient MC-aware array topology design and minimizing maximum SLL in multi-beam configurations. ML-based post-manufacturing diagnostics are demonstrated for a 64-element active uniform array, where a fully connected NN detects faulty elements in real time using sparse far-field amplitude data.
The findings demonstrate that ML can bridge the gap between EM theory and practical array implementation. The developed frameworks offer data-efficient alternatives for MC-aware active array optimization and system diagnostics, paving the way toward intelligent, reliable, and self-adaptive active array systems for future wireless communication and sensing applications.
Traditionally, design and performance evaluation of active phased arrays have relied on deterministic electromagnetic simulations or optimization-based methods focusing on radiation characteristics such as gain, beamwidth, and side-lobe levels (SLL). As arrays grow more complex, such methods face limitations in scalability and computational feasibility. Machine learning (ML) offers a pathway to overcome these barriers by enabling data-driven modeling, optimization, and diagnosis of large-scale or non-uniform active arrays. This interdisciplinary challenge lies at the intersection of electromagnetics (EM), array modeling, and data-driven methods, requiring frameworks that balance physical accuracy with computational efficiency.
The objective of this doctoral research is to develop and validate ML-based methodologies that enhance both the design and operation of active arrays, emphasizing efficient modeling, synthesis, and diagnosis under mutual coupling (MC), environmental effects, and fabrication-induced uncertainties. The research addresses both pre-manufacturing (e.g., synthesis, EM modeling, and topology optimization) and post-manufacturing (e.g., calibration and fault detection) aspects of active arrays, with a focus on data-efficient and physics-supported ML techniques.
Chapter II introduces the motivation, state-of-the-art challenges in phased array systems, and the potential of ML in overcoming these limitations. A detailed analysis highlights critical issues of mutual coupling, irregular routing, calibration complexity, and environmental sensitivity, establishing the need for intelligent, adaptive frameworks that integrate ML with physical modeling to enhance design reliability and post-deployment robustness.
Chapter III presents data-driven modeling of EM interactions in aperiodic phased arrays. A neural network (NN)-based framework predicts embedded element patterns (EEPs) across the visible space for non-uniform planar arrays. The cascaded NN architecture, combining a fully connected NN and a sub-pixel convolutional network, enables high-resolution pattern prediction with significant computational savings. The influence of dataset size and quality on prediction reliability is analyzed, revealing insights into data efficiency and model generalization.
Chapter IV introduces hybrid ML-physics approaches to improve prediction robustness. Two basis-function-assisted frameworks are developed using the Infinitesimal Dipole Model (IDM) and spherical harmonics. These models achieve compact, reliable EEP representation with smaller datasets while mitigating numerical instability. An ensemble method combining NN-based EEP prediction and constrained IDM achieves a 60% reduction in mean squared error (MSE) and improved prediction stability under MC effects.
Chapter V applies these ML-assisted models to system-level synthesis and diagnostics. A novel ML-driven optimization method for MIMO radar arrays integrates spherical harmonics-based EEP prediction into a particle swarm optimization (PSO) routine, enabling efficient MC-aware array topology design and minimizing maximum SLL in multi-beam configurations. ML-based post-manufacturing diagnostics are demonstrated for a 64-element active uniform array, where a fully connected NN detects faulty elements in real time using sparse far-field amplitude data.
The findings demonstrate that ML can bridge the gap between EM theory and practical array implementation. The developed frameworks offer data-efficient alternatives for MC-aware active array optimization and system diagnostics, paving the way toward intelligent, reliable, and self-adaptive active array systems for future wireless communication and sensing applications.
Journal article
(2025)
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Aparna Kannan, Nehir Berk Onat, Marco Spirito Alexander Yarovoy, Yanki Aslan
In this contribution, we analyze machine learning-assisted solutions to tackle real-time fault detection in large-scale active phased array antennas. The challenge of integrating the circuit component nonlinearities and mutual coupling effects in fault-finding methodologies is addressed. A novel machine learning (ML) solution based on an array theory-enhanced neural network (NN) is proposed. To address the practical constraints of large array measurements, sparse far-field (FF) measurements are considered. The method is experimentally verified by applying it to fault detection in a 64-element planar phased array prototype operating at 26 GHz with far-field measurements collected by a fixed high-speed multi-probe pattern acquisition setup, the Antenna Dome. Significant improvement in fault prediction performance over a conventional Genetic Algorithm (GA) based heuristic approach with improvements of up to 40%, 25%, and 20% in predicting 8-, 4-, and 2-element faults, respectively, is demonstrated. The robustness of the proposed performance with respect to the number of 3D spatial field sampling points is shown, offering efficient diagnostics with in-field pattern sampling compatibility and low hardware complexity.
...
In this contribution, we analyze machine learning-assisted solutions to tackle real-time fault detection in large-scale active phased array antennas. The challenge of integrating the circuit component nonlinearities and mutual coupling effects in fault-finding methodologies is addressed. A novel machine learning (ML) solution based on an array theory-enhanced neural network (NN) is proposed. To address the practical constraints of large array measurements, sparse far-field (FF) measurements are considered. The method is experimentally verified by applying it to fault detection in a 64-element planar phased array prototype operating at 26 GHz with far-field measurements collected by a fixed high-speed multi-probe pattern acquisition setup, the Antenna Dome. Significant improvement in fault prediction performance over a conventional Genetic Algorithm (GA) based heuristic approach with improvements of up to 40%, 25%, and 20% in predicting 8-, 4-, and 2-element faults, respectively, is demonstrated. The robustness of the proposed performance with respect to the number of 3D spatial field sampling points is shown, offering efficient diagnostics with in-field pattern sampling compatibility and low hardware complexity.
Conference paper
(2024)
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Nehir Berk Onat, Alexander Yarovoy, Yanki Aslan
The sunflower array topology concept is introduced, for the first time, to the constrained infinitesimal dipole modeling (IDM) technique to increase the computational efficiency and reduce the modeling errors. The concept is applied to embedded element pattern predictions via matrix inversion. A novel study on the impact of the type and orientation of the dipoles on the IDM performance in pattern mean square error (MSE) and stability against noise (linked to the matrix condition number) is conducted. A 5 by 5 patch antenna array modeled with 81 dipoles is used for demonstration. It is shown that using magnetic dipoles (oriented in the direction of a radiating edge of the patch) in IDM yields the optimal performance. Besides, the sunflower topology significantly lowers the MSE (by 5 dB, on average), while reducing the condition number by a factor of 10.
...
The sunflower array topology concept is introduced, for the first time, to the constrained infinitesimal dipole modeling (IDM) technique to increase the computational efficiency and reduce the modeling errors. The concept is applied to embedded element pattern predictions via matrix inversion. A novel study on the impact of the type and orientation of the dipoles on the IDM performance in pattern mean square error (MSE) and stability against noise (linked to the matrix condition number) is conducted. A 5 by 5 patch antenna array modeled with 81 dipoles is used for demonstration. It is shown that using magnetic dipoles (oriented in the direction of a radiating edge of the patch) in IDM yields the optimal performance. Besides, the sunflower topology significantly lowers the MSE (by 5 dB, on average), while reducing the condition number by a factor of 10.
Conference paper
(2024)
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Marta Buenaventura-Camps, Yanki Aslan, Philipp Freidl, Pascal Aubry, Nehir Berk Onat, Johan Janssen, Marcel Geurts, Alexander Yarovoy
A novel dual-linear polarized active aperiodic millimeter-wave (26 GHz band) phased array is prototyped. Four-channel integrated circuits are used for beamforming. An asymmetrical signal distribution and antenna feeding network is designed. A heuristic post-calibration method is applied in beam steering. The measurements show that, as compared to the benchmark array with a conventional square-grid layout, the proposed aperiodic topology can significantly decrease the peak sidelobe level, the extent of which depends on the polarization and scan angle. This comes at the expense of larger array size, higher feed network losses and increased cross-polarization levels.
...
A novel dual-linear polarized active aperiodic millimeter-wave (26 GHz band) phased array is prototyped. Four-channel integrated circuits are used for beamforming. An asymmetrical signal distribution and antenna feeding network is designed. A heuristic post-calibration method is applied in beam steering. The measurements show that, as compared to the benchmark array with a conventional square-grid layout, the proposed aperiodic topology can significantly decrease the peak sidelobe level, the extent of which depends on the polarization and scan angle. This comes at the expense of larger array size, higher feed network losses and increased cross-polarization levels.
Conference paper
(2024)
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N.B. Onat, F. Fioranelli, Alexander Yarovoy, Y. Aslan
This paper examines how training data affects machine learning-assisted antenna pattern prediction under mutual coupling. For demonstration, a neural network-based approach is used to predict the embedded pattern of a central patch antenna element near randomly distributed patches. It is shown that when the full-wave simulated dataset size is excessively reduced, the high prediction error in the validation set may become a critical issue. Maintaining sufficient accuracy in pattern prediction with a relatively small dataset remains an open challenge.
...
This paper examines how training data affects machine learning-assisted antenna pattern prediction under mutual coupling. For demonstration, a neural network-based approach is used to predict the embedded pattern of a central patch antenna element near randomly distributed patches. It is shown that when the full-wave simulated dataset size is excessively reduced, the high prediction error in the validation set may become a critical issue. Maintaining sufficient accuracy in pattern prediction with a relatively small dataset remains an open challenge.
Journal article
(2024)
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Nehir Berk Onat, Ignacio Roldan, Francesco Fioranelli, Alexander Yarovoy, Yanki Aslan
A novel ensemble prediction technique is introduced to enhance the accuracy of far-field embedded element pattern (EEP) prediction under mutual coupling (MC) effects, while relaxing the training data size challenge in neural network (NN)-based algorithms. The proposed method integrates a two-stage NN for direct EEP prediction from full-wave simulated pattern data in spherical coordinates with a fully connected NN for the prediction of excitation coefficients of an array of infinitesimal dipoles, approximating the full-wave simulated EEPs via constrained infinitesimal dipole modeling (IDM). Quasi-randomly distributed five-element pin-fed S-band patch antenna arrays are used for demonstration purpose. It is shown that, for a large-sized (3500 topologies) and relatively small-sized (1500 topologies) dataset, incorporating IDM-NN with the benchmarked direct EEP-NN in an ensemble technique increases the pattern prediction accuracy by 11% and 60% on average, respectively.
...
A novel ensemble prediction technique is introduced to enhance the accuracy of far-field embedded element pattern (EEP) prediction under mutual coupling (MC) effects, while relaxing the training data size challenge in neural network (NN)-based algorithms. The proposed method integrates a two-stage NN for direct EEP prediction from full-wave simulated pattern data in spherical coordinates with a fully connected NN for the prediction of excitation coefficients of an array of infinitesimal dipoles, approximating the full-wave simulated EEPs via constrained infinitesimal dipole modeling (IDM). Quasi-randomly distributed five-element pin-fed S-band patch antenna arrays are used for demonstration purpose. It is shown that, for a large-sized (3500 topologies) and relatively small-sized (1500 topologies) dataset, incorporating IDM-NN with the benchmarked direct EEP-NN in an ensemble technique increases the pattern prediction accuracy by 11% and 60% on average, respectively.
Efficient Embedded Element Pattern Prediction via Machine Learning
A Case Study with Planar Non-Uniform Sub-Arrays
Conference paper
(2023)
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Nehir Berk Onat, Ignacio Roldan, Francesco Fioranelli, Alexander Yarovoy, Yanki Aslan
Efficient prediction of embedded element patterns (EEPs) is including the mutual coupling (MC) effects in the optimization of irregular planar arrays is studied for the first time in the literature. An ANN-based methodology is used to predict the pattern of each element in the whole visible space for a flexible planar array topology in milliseconds. The technique is proposed is validated on a 4-element planar non-uniform sub-array structure. Excellent accuracy on the EEP prediction while providing great efficiency in computational time and load in comparison to the full-wave simulations is demonstrated.
...
Efficient prediction of embedded element patterns (EEPs) is including the mutual coupling (MC) effects in the optimization of irregular planar arrays is studied for the first time in the literature. An ANN-based methodology is used to predict the pattern of each element in the whole visible space for a flexible planar array topology in milliseconds. The technique is proposed is validated on a 4-element planar non-uniform sub-array structure. Excellent accuracy on the EEP prediction while providing great efficiency in computational time and load in comparison to the full-wave simulations is demonstrated.
Conference paper
(2023)
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Nicolas Van De Kreeke, Yanki Aslan, Nehir Berk Onat, Alexander Yarovoy
The effects of multipath on the statistical cell-edge user service quality is for the first time investigated for mm-wave multi-user communication systems. The focus is given on setting the user spacing constraints and the transmit array topology via thinning, which can be used to enhance wireless security or decrease analog/digital complexity. A hybrid line-of-sight/non-line-of-sight channel is created by using a statistical model following the communication standards. The multipath signal components are included in the model by using non-coherent or coherent modes of operation. It is shown in simulation that selection, by the medium access control layer, of large angular spacings between the simultaneously served users and application of antenna array thinning at the array edges improves the system performance.
...
The effects of multipath on the statistical cell-edge user service quality is for the first time investigated for mm-wave multi-user communication systems. The focus is given on setting the user spacing constraints and the transmit array topology via thinning, which can be used to enhance wireless security or decrease analog/digital complexity. A hybrid line-of-sight/non-line-of-sight channel is created by using a statistical model following the communication standards. The multipath signal components are included in the model by using non-coherent or coherent modes of operation. It is shown in simulation that selection, by the medium access control layer, of large angular spacings between the simultaneously served users and application of antenna array thinning at the array edges improves the system performance.
Conference paper
(2022)
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Yanki Aslan, Nehir Berk Onat
The optimization of mode excitation coefficients in linear periodic arrays of multi-mode antenna elements is studied for grating lobe reduction. A novel beamforming architecture is proposed with a new optimization problem based on equi-amplitude element excitations for optimal power efficiency. The capabilities of the proposed synthesis approach on suppressing the grating lobe for wide scan angles, and on maintaining the peak gain at the steering angle are analyzed. A 16-element 0.7-wavelength spaced array of dual-mode circular patch antenna elements is used for demonstration purposes. It is shown that a good performance trade-off is achieved when the excitation amplitude of the high order mode is restricted to a sufficiently large value. The ratio of the peak gain outside the main lobe to the gain at the angle of steering is reduced up to about −15 dB and −14 dB for scanning towards 30 and 45 degrees off-broadside, respectively.
...
The optimization of mode excitation coefficients in linear periodic arrays of multi-mode antenna elements is studied for grating lobe reduction. A novel beamforming architecture is proposed with a new optimization problem based on equi-amplitude element excitations for optimal power efficiency. The capabilities of the proposed synthesis approach on suppressing the grating lobe for wide scan angles, and on maintaining the peak gain at the steering angle are analyzed. A 16-element 0.7-wavelength spaced array of dual-mode circular patch antenna elements is used for demonstration purposes. It is shown that a good performance trade-off is achieved when the excitation amplitude of the high order mode is restricted to a sufficiently large value. The ratio of the peak gain outside the main lobe to the gain at the angle of steering is reduced up to about −15 dB and −14 dB for scanning towards 30 and 45 degrees off-broadside, respectively.