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A hybrid algorithm is introduced to synthesize the nonuniform symmetric linear antenna arrays in this paper. The array element positions and excitation coefficients of the antenna arrays are optimized by using Gray Wolf Algorithm (GWO) and Convex Optimization Algorithm (CO). In order to further reduce the sidelobe level, the linear antenna array is divided into several subarrays under the constraints of element number, aperture size and minimum adjacent element spacing. Array element positions, excitation coefficients of each subarray and the element number of each subarray are optimized simultaneously. Finally, the optimization results are compared with the results obtained by other literatures. It is obviously found that the method proposed in this paper can obtain lower sidelobe level under the same conditions. So, the effectiveness and feasibility of the algorithm proposed in this paper is proved. [ABSTRACT FROM AUTHOR]

In this study, optimum radiation patterns of Concentric Circular Antenna Arrays (CCAAs) are obtained by using the Atomic Orbital Search (AOS) algorithm for communication spectrum. Communication systems stands as a nascent technological innovation poised to revolutionize the landscape of wireless communication systems. It distinguishes itself through its hallmark features, notably an exceptionally high data transmission rate, expanded network capacity, minimal latency, and a commendable quality of service. The most important issue in wireless communication is a precision antenna array design. The success of this design depends on suppressing the maximum sidelobe levels (MSLs) values of the antenna in the far-field radiation region as much as possible. The AOS, which is a rapid and flexible search algorithm, is a novel physics-based algorithm. The amplitudes and inter-element spacing of CCAAs are optimally determined by utilizing AOS to the reduction of the MSLs. In this study, CCAAs with three and four rings are considered. The number of elements of these CCAAs has been determined as 4–6–8, 8–10–12 and 6–12–18–24. The radiation patterns obtained with AOS are compared with the results available in the literature and it is seen that the results of the AOS method are better. [ABSTRACT FROM AUTHOR]

Abstract Time-modulated array antenna (TMAA) is a new type of array antenna based on time modulation technology. By introducing "time" as the fourth dimensional design freedom into the design of conventional array antennas in three-dimensional space, the array antenna has time modulation characteristics, which better controls the radiation characteristics of the array antenna and achieves the best far-field radiation pattern synthesis. This paper designs a Time-modulated linear array (TMLA) with low sidelobe level (SLL) and low sideband level (SBL) based on the chaotic exchange nonlinear dandelion optimization (CENDO) algorithm. Three optimization methods are studied: firstly, determining the optimal on-time (τ nn ) for each array element; The second is to determine the optimal on-time (τ nn ) and optimal uniform array element spacing (d) for each array element; The third is to determine the optimal opening time (t on ), closing time (t off ), and optimal uniform array element spacing (d) for each array element. To achieve simultaneous reduction of sidelobe level and suppression of harmonic interference. The same array model contains different harmonic frequency radiation. In this article, we only considered two harmonic frequencies, namely the first sideband frequency and the second sideband frequency. Because the harmonic of other sideband frequencies has a very small impact on the radiation of the fundamental wave, it can be ignored. To demonstrate the stronger ability of the CENDO algorithm in optimizing Time-modulated array antennas, and in line with the principle of fairness and impartiality, this paper also simulates different Time-modulated array models and compares the results of the CENDO algorithm with other published literature. It is concluded that this study shows lower SLL and lower SBL in different models. This provides a more scientific and accurate explanation of the superiority of the CENDO algorithm compared to other algorithms in the field of antenna optimization in electromagnetics. At the same time, this also provides great research value and fundamental support for designing high-performance Time-modulated array antennas in subsequent engineering applications.

In this paper, we develop a new algorithm for pattern synthesis of large linear arrays. In our algorithm, the large linear array is divided into multiple disjoint nonuniformly-spaced subarrays and is only weighted at subarray level. Unlike the existing methods, the subarray synthesis is implemented in two stages. Firstly, the optimization of subarray partition and subarray weights are performed by using the K-means approach. Based on the determined subarray partition and subarray weights, the subarray element positions are further optimized by the iterative convex programming method. Compared with the state-of-the-art methods, the proposed algorithm can synthesize lower peak sidelobe level and yield smaller pattern matching error. Numerical results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The design of an effective antenna array is a major challenge encountered in most communication systems. A much-needed requirement is obtaining a directional and high-gain radiation pattern. This study deals with the design of a linear antenna array that radiates with reduced peak-side lobe levels (PSLL), decreases side-lobe average power with and without the first null beamwidth (FNBW) constraint, places deep nulls in the desired direction, and minimizes the close-in-side lobe levels (CSLL). The nature-inspired metaheuristic algorithm multi-verse optimization (MVO) is explored with other state-of-the-art algorithms to optimize the parameters of the antenna array. MVO is a global search method that is less prone to being stuck in the local optimal solution, providing a better alternative for beam-pattern synthesis. Eleven design examples have been demonstrated, which optimizes the amplitude and position of antenna array elements. The simulation results illustrate that MVO outperforms other algorithms in all the design examples and greatly enhances the radiation characteristics, thus promoting industrial innovation in antenna array design. In addition, the MVO algorithm's performance was validated using the Wilcoxon non-parametric test. [ABSTRACT FROM AUTHOR]

Recently, research has been proposed using a few components in digital active phased array antennas to achieve sidelobe blanking beam patterns without the need for additional antennas, aiming to mitigate the degradation of radar detection performance caused by interference signals such as clutter and jamming entering through the sidelobes. To precisely obtain the desired sidelobe blanking beam pattern, simulations are required to manually vary the parameters of the components (such as magnitude, phase, and position) and derive an optimal solution. In this study, instead of a manual approach, a reliable synthesis of optimal sidelobe blanking beam patterns is proposed based on pattern synthesis methods and nature-inspired nonlinear optimization algorithms (that is, genetic algorithm and particle swarm optimization) with the magnitude, phase and position of array elements as variables. A comparison of the algorithmic results for the synthesis of the optimal sidelobe blanking beam patterns was performed. The optimization results showed that both algorithms successfully generated patterns capable of blanking sidelobes, with the particle swarm optimization demonstrating convergence at higher fitness levels. [ABSTRACT FROM AUTHOR]

This paper introduces an application of the dandelion optimization (DO) algorithm in antenna arrays. This is the first time that the DO algorithm has been used for optimizing antenna arrays. For antenna array optimization, sidelobe level (SLL) and deep nulls are key technical indicators. A lower SLL can improve the signal-to-noise ratio and reduce the impact of clutter signals outside the main beam. Deep nulls need to be aligned with the direction of interference to eliminate the influence of interference sources. The combination of the two can effectively improve the anti-interference ability of the entire system. Therefore, antenna arrays with ultra-low sidelobes and ultra-deep nulls are currently hot in the field of antenna array design and are also some of the key technologies needed to achieve modern high-performance radar systems. As a new type of evolutionary algorithm inspired by nature, the DO algorithm is inspired by the wind propagation behavior of dandelions in nature. This algorithm iteratively updates the population from three stages of ascent, descent, and landing, ultimately finding the optimal position. It has good optimization ability in solving complex problems such as those involving nonlinearity, discreteness, and non-convexity, and the antenna array pattern synthesis optimization problem belongs to multivariate nonlinear problems. Therefore, the DO algorithm can be effectively applied in the field of antenna array optimization. In this work, we use the following method to obtain an optimized pattern of a linear array with the lowest sidelobe level (SLL), null placement in particular directions, and a lower notch in particular directions: by controlling the antenna array's element spacing and leaving the phase unchanged to optimize the current amplitudes and by controlling the excitation current and phase fixation of the antenna array and changing the element spacing. In the first and second examples, different algorithms are used to reduce the SLL of the antenna. In the first example, the DO algorithm reduces the SLL to −33.37 dB, which is 2.67 dB, 2.67 dB, 3.77 dB, 2.74 dB, and 2.52 dB lower than five other algorithms. In the second example, the SLL optimized by the DO algorithm is −42.56 dB, which is 5.04 dB and 1.48 dB lower than two other algorithms. In both examples, the DO algorithm reduces the SLL lower than other algorithms when the main lobe of the antenna is not significantly widened. Examples 3, 4, and 5 use the DO algorithm to optimize the amplitude of the current, generating deep nulls and deep notches in specific directions. In Example 3, the DO algorithm obtains a depth of nulls equal to −187.6 dB, which is 66.7 dB and 44.3 dB lower than that of the flower pollination algorithm (FPA) and the chaotic colony predation algorithm (CCPA), respectively. In Example 4, the deep null obtained by the DO algorithm is as low as −98.69 dB, which is 6.67 dB lower than the deep null obtained by the grey wolf optimization (GWO) algorithm. In Example 5, the deep notch obtained by the DO algorithm is as low as −63.1 dB, which is 6.4 dB and 1.9 dB lower than the spider monkey optimization (SMO) algorithm and the grasshopper optimization algorithm (GOA), respectively. The data prove that the DO algorithm produces deeper nulls and notches than other algorithms. The last two examples involve reducing sidelobe levels and generating deep nulls by optimizing the spacing between elements. In Example 5, the SLL obtained using the DO algorithm is −22.8766 dB, which is 0.1998 dB lower than the lowest SLL of −22.6768 dB among other algorithms. In Example 6, the SLL obtained using the DO algorithm is −20.1012 dB, and the null depth is −125.1 dB, which is 1.592 dB lower than the SLL obtained by the cat swarm optimization (CSO) algorithm and 19.1 dB lower than the deep null obtained by the GWO algorithm, respectively. In summary, the results of six simulation experiments indicate that the DO algorithm has better optimization ability in linear array optimization than other evolutionary algorithms. [ABSTRACT FROM AUTHOR]

This work introduces a novel approach for predicting the complex element weights of a rectangular planar phased array for a desired beam pattern using physics-aware convolutional neural networks (PA-CNN). An image representation of the desired radiation pattern is provided as input to the proposed network architecture, where PA-CNN learns to recognize spatial features and map input patterns to the amplitude and phase of the antenna element weights. In addition to the classical mean squared error (MSE) loss for regression to true weights, a novel physics-aware loss function is designed to jointly force the PA-CNN to estimate true weight while generating physically consistent beam patterns. The performance of the proposed model is evaluated using metrics such as MSE of estimated weights and traditional radiation pattern metrics such as accuracy of peak direction. The obtained results demonstrate that the models successfully learn the nonlinear relation between antenna parameters and desired beam patterns, where the physics-aware approach outperforms the base CNN cases with an improvement of 6.2% in the MSE between radiation patterns. This approach provides a promising solution for learning complex weights of arrays for synthesizing desired patterns while ensuring physical consistency.

A novel approach is presented for synthesizing radiation patterns of substrate-integrated waveguide leaky wave antennas in two stages. In the first stage, the antenna is considered an equivalent rectangular waveguide with slots, and a comprehensive equivalent circuit model is constructed. Varying slot configurations are accommodated by the developed equivalent circuit model. Each slot section is represented as an admittance with the incorporation of Elliott’s slot theory. Computations of the relative aperture fields are performed using circuit theory, and the far-field pattern is estimated through array theory. In the second stage, the circuit model is utilized for genetic algorithm-based optimization, enabling customization of the radiation pattern to meet specific requirements. The methodology has a computational advantage over full-wave simulations, resulting in a significantly faster and more efficient design process. Numerical verification through simulation of various examples and experimental validation through antenna fabrication are presented, affirming agreement between calculated and measured results. Remarkable effectiveness in antenna engineering can be attained for future wireless communication systems by using the proposed technique.

A single-feed broadband quasi-isotropic antenna was designed for non-line-of-sight (NLOS) wireless sensor networks. The proposed antenna is based on a combination of fork-shaped crossed dipoles. It shows the broadband of quasi-isotropic radiation characteristics with high radiation efficiency. The electrical size ka of the proposed antenna is 0.94 with respect to its lower operating frequency. Its profile is also extremely thin at 0.0015λ. The impedance is matched from 1.8 to 4.3 GHz, or an 81.9% fractional bandwidth, whereas the maximum gain deviation ranging from 6.2 to 9.2 dB for the quasi-isotropic radiation is achieved from 1.8 to 3.6 GHz with a 10 dB criterion, which is close to the impedance bandwidth. The performance from the computed expectations is verified, as it shows a gain deviation of 8.4–9.8 dB from 1.9 to 3.3 GHz with an 80% fractional impedance bandwidth. The proposed antenna also shows good spatial coverage of circular polarization at high frequencies. Lastly, the received power level performance of the proposed antenna is tested under the NLOS condition, which shows a higher level compared to the linearly polarized, broadband omni-directional monopole antenna. [ABSTRACT FROM AUTHOR]

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High-performance beamforming incorporating multiple objectives for large-scale antenna arrays becomes increasingly important to improve the capacity and efficiency of wireless communication systems. The speed of synthesizing a desired beam pattern is critical in wireless communications systems to adapt to highly dynamic wireless channels. A modified particle swarm optimization (PSO) algorithm for synthesizing array beam patterns is proposed in this study. The initial positions of particles in PSO are designated following a Taylor distribution instead of being given uniformly distributed random values as in the classical PSO algorithm. The fitness functions are defined to include multiple objectives represented by producing multiple main lobes with customized deep and broadened nulls. Several scenarios have been established to examine the feasibility of the proposed algorithm. Moreover, the performance of the proposed algorithm is compared with those of the ones based on the classical PSO. A significant performance improvement for obtaining beamforming coefficients has been achieved. The robustness of the proposed algorithm is demonstrated further by applying it to a finite array on a curved surface for beamforming. [ABSTRACT FROM AUTHOR]

This paper proposes a gradient-based optimization method for pattern synthesis of phase-only thinned concentric elliptical antenna arrays (CEAAs) with the tangent interpolation function of continuous 0–1 differentiation in topology optimization. The optimization model is reasonably constructed and actualized by simultaneously optimizing the presence or absence of metal materials and the excitation phase within each array element. In order to facilitate solving the optimization problem using the gradient-based algorithms and performing the CEAA pattern synthesis, the tangent interpolation function of metal material with a pseudo-resistance is introduced to realize the continuous 0–1 differentiation of each array element. The design objective is to maximize the difference between the radiation energies of the main-beam direction and the peak sidelobe level with the constraints on the total number of remaining elements. For ensuring that the mutual coupling between the elements is accurately considered, the full-wave method of moments (MoM) is integrated into the optimization method to synthesize the array radiation pattern. Typical examples are provided to verify the effectiveness of the proposed method. Compared with the uniform array and the reference CEAA, the proposed method not only achieves the sidelobe suppression and aperture reduction but also significantly decreases the total number of array elements. [ABSTRACT FROM AUTHOR]

This paper introduces an application of the dandelion optimization (DO) algorithm in antenna arrays. This is the first time that the DO algorithm has been used for optimizing antenna arrays. For antenna array optimization, sidelobe level (SLL) and deep nulls are key technical indicators. A lower SLL can improve the signal-to-noise ratio and reduce the impact of clutter signals outside the main beam. Deep nulls need to be aligned with the direction of interference to eliminate the influence of interference sources. The combination of the two can effectively improve the anti-interference ability of the entire system. Therefore, antenna arrays with ultra-low sidelobes and ultra-deep nulls are currently hot in the field of antenna array design and are also some of the key technologies needed to achieve modern high-performance radar systems. As a new type of evolutionary algorithm inspired by nature, the DO algorithm is inspired by the wind propagation behavior of dandelions in nature. This algorithm iteratively updates the population from three stages of ascent, descent, and landing, ultimately finding the optimal position. It has good optimization ability in solving complex problems such as those involving nonlinearity, discreteness, and non-convexity, and the antenna array pattern synthesis optimization problem belongs to multivariate nonlinear problems. Therefore, the DO algorithm can be effectively applied in the field of antenna array optimization. In this work, we use the following method to obtain an optimized pattern of a linear array with the lowest sidelobe level (SLL), null placement in particular directions, and a lower notch in particular directions: by controlling the antenna array’s element spacing and leaving the phase unchanged to optimize the current amplitudes and by controlling the excitation current and phase fixation of the antenna array and changing the element spacing. In the first and second examples, different algorithms are used to reduce the SLL of the antenna. In the first example, the DO algorithm reduces the SLL to −33.37 dB, which is 2.67 dB, 2.67 dB, 3.77 dB, 2.74 dB, and 2.52 dB lower than five other algorithms. In the second example, the SLL optimized by the DO algorithm is −42.56 dB, which is 5.04 dB and 1.48 dB lower than two other algorithms. In both examples, the DO algorithm reduces the SLL lower than other algorithms when the main lobe of the antenna is not significantly widened. Examples 3, 4, and 5 use the DO algorithm to optimize the amplitude of the current, generating deep nulls and deep notches in specific directions. In Example 3, the DO algorithm obtains a depth of nulls equal to −187.6 dB, which is 66.7 dB and 44.3 dB lower than that of the flower pollination algorithm (FPA) and the chaotic colony predation algorithm (CCPA), respectively. In Example 4, the deep null obtained by the DO algorithm is as low as −98.69 dB, which is 6.67 dB lower than the deep null obtained by the grey wolf optimization (GWO) algorithm. In Example 5, the deep notch obtained by the DO algorithm is as low as −63.1 dB, which is 6.4 dB and 1.9 dB lower than the spider monkey optimization (SMO) algorithm and the grasshopper optimization algorithm (GOA), respectively. The data prove that the DO algorithm produces deeper nulls and notches than other algorithms. The last two examples involve reducing sidelobe levels and generating deep nulls by optimizing the spacing between elements. In Example 5, the SLL obtained using the DO algorithm is −22.8766 dB, which is 0.1998 dB lower than the lowest SLL of −22.6768 dB among other algorithms. In Example 6, the SLL obtained using the DO algorithm is −20.1012 dB, and the null depth is −125.1 dB, which is 1.592 dB lower than the SLL obtained by the cat swarm optimization (CSO) algorithm and 19.1 dB lower than the deep null obtained by the GWO algorithm, respectively. In summary, the results of six simulation experiments indicate that the DO algorithm has better optimization ability in linear array optimization than other evolutionary algorithms.

A single-feed broadband quasi-isotropic antenna was designed for non-line-of-sight (NLOS) wireless sensor networks. The proposed antenna is based on a combination of fork-shaped crossed dipoles. It shows the broadband of quasi-isotropic radiation characteristics with high radiation efficiency. The electrical size ka of the proposed antenna is 0.94 with respect to its lower operating frequency. Its profile is also extremely thin at 0.0015λ. The impedance is matched from 1.8 to 4.3 GHz, or an 81.9% fractional bandwidth, whereas the maximum gain deviation ranging from 6.2 to 9.2 dB for the quasi-isotropic radiation is achieved from 1.8 to 3.6 GHz with a 10 dB criterion, which is close to the impedance bandwidth. The performance from the computed expectations is verified, as it shows a gain deviation of 8.4–9.8 dB from 1.9 to 3.3 GHz with an 80% fractional impedance bandwidth. The proposed antenna also shows good spatial coverage of circular polarization at high frequencies. Lastly, the received power level performance of the proposed antenna is tested under the NLOS condition, which shows a higher level compared to the linearly polarized, broadband omni-directional monopole antenna.