Shitao Zhu

Reconfigurable antennas have emerged as a promising solution in wireless communication systems to adapt to dynamic environments and enhance system performance. Pattern reconfigurability plays a crucial role in improving antenna adaptability to spatially diverse multi-cluster channels, thus maximizing channel capacity. Existing works primarily address either the mode selection problem from a pre-designed finite pattern set which adjusts only beamwidth and scanning direction and thus can not offer optimal channel adaptation, or the mode design problem of computing continuously adjustable patterns under the assumation of an infinite pattern number but without considering the limit on the pattern number in real-world antennas. To address this gap, this paper proposes a practical design methodology that jointly accounts for the finite number of reconfigurable patterns and their precise optimization over diverse channel conditions. Specifically, channels are grouped based on user location distribution, and a joint optimization approach combining alternating optimization, auxiliary variables, and bisection is employed to design the optimal pattern set under the constraint of limited reconfigurable states. Simulation results demonstrate the effectiveness of the optimized patterns and show that the achieved capacity exhibits an approximately logarithmic relationship with the number of reconfigurable patterns, thereby providing a practical validation of existing theoretical analyses. This work bridges the gap between theoretical mode design and practical implementation by offering a concrete and implementable solution for designing a finite set of reconfigurable antenna patterns tailored to realistic system constraints.

Reconfigurable arrays enable flexible utilization of system resources and thus become a key technology in modern radar and communication systems. Once the array is reconfigured, its steering vector as well as the correlation matrix of the received signal also changes. Therefore, data-dependent adaptive beamformers, such as the minimum variance distortionless response (MVDR) algorithm, have to recompute their weighting vectors, causing performance degradation in the initial stage of reconfiguration. This paper proposes three types of recursive MVDR algorithms to overcome this problem. By utilizing the block matrix inversion formula, the proposed methods find an efficient way to update the inverse of the reconfigured correlation matrix, which enables the beamformer to operate near the convergence point and maintain stable performance during reconfiguration. Consequently, the proposed methods not only reduce the computational cost compared with the direct inversion method, but also obtain better performance than the recursive method using restart technique. Simulation experiments demonstrate the effectiveness and advantages of the proposed methods.

Electromagnetic vortex waves have received widespread attention in many fields due to their unique physical characteristics. The information dimension provided by vortex electromagnetic waves brings possibilities for future breakthroughs in radar detection and imaging. This article proposes a multi-modal aggregated electromagnetic vortex wave generation method for the first time. Moreover, it conducts vehicle imaging experiments to verify the method’s practicality. The core element of the experiment is to simultaneously generate multiple-mode electromagnetic vortex wave signals with energy accumulation and perform fusion processing. Firstly, multiple orbital angular momentum (OAM) modes are superimposed to generate a mode group, and the initial phase of the modes in the mode group is further controlled to synthesize aggregated electromagnetic vortex waves. Based on the generation of aggregated vortex waves, imaging experiments were conducted using a vehicle-mounted setup. The experimental procedure and multi-modal fusion results were presented. It has been shown that the energy of the main lobe signal of the image target is enhanced by utilizing multi-modal vortex radar information fusion, which can improve the signal-to-noise ratio of the target imaging.

This article investigates optimal antenna array configurations for multiple-input–multiple-output (MIMO) systems. The array configuration, particularly the element spacing, significantly affects the correlation between elements, which can lead to channel rank deficiency or deterioration in condition number. In practical sparse channels, deploying a massive number of antenna elements can become redundant. Therefore, exploring optimal array configurations is crucial for enhancing MIMO communication system. Although substantial theoretical analyzes have been conducted for antenna configurations in Line-of-Sight (LOS) environments, analyzes in non-LOS (NLOS) environments remain limited. Through theoretical analysis, this article demonstrates that in NLOS environments, the optimal condition number of MIMO channels is achieved when the antenna array configuration renders the antenna array matrix orthogonal, with the optimal condition number being determined by the wireless propagation environment. Building upon this finding, we show that there always exists an antenna spacing that makes the antenna array matrix orthogonal in a two-cluster environment, and we provide a closed-form solution for this spacing. For three-cluster environments, we derive the conditions that cluster angles must satisfy to achieve antenna array matrix orthogonality and provide a closed-form solution for optimal antenna spacing under these conditions. Furthermore, we propose a systematic solution based on Newton descent algorithm to calculate optimal antenna spacing, aimed at reducing analytical complexity in environments with more clusters. Finally, numerical simulations validate the theoretical analysis and provide recommendations for optimizing antenna placement in IoT devices operating in sparse channel environments.

The capability of the random radiation frequency diverse metasurface aperture (FrDMA) determines the performance of the FrDMA based coincidence imaging (FrDMACI) system. However, there is still no theoretical analysis result, though the FrDMACI has been experimentally validated. From the perspective of the spatial modulation, the capability of the FrDMA is analyzed assisted by the array synthesis and statistical theory in this paper. First, the equivalent mathematical model is built by regarding the metasurface element (ME) as a radiator with random phase modulation. The low correlated MEs are designed according to the requirement of the FrDMACI system. Then, the degrees of freedom (DOF) of the FrDMA are analyzed for both narrowband and wideband cases, respectively. In addition, the optimal design method of the FrDMA is proposed using the joint optimization of the mathematical optimization and electromagnetic simulation based on a set of MEs. The FrDMAs, including the optimal FrDMA and the random mapping designed FrDMAs, are designed, fabricated and tested. The simulation and measurement results show that the DOF of the FrDMA can be predicted when the aperture size is fixed. Finally, the performances of the FrDMA are validated by the super resolution coincidence imaging experiments. The imaging results show that the optimal FrDMA can approach the ideal state predicted by the proposed method.

To facilitate microwave computational polarimetric imaging (MCPI) within a limited operating frequency bandwidth, a frequency-diverse compact metacavity antenna (CMA) using an aperture-rotation technique is proposed in this communication. The proposed CMA is fundamentally a cavity-based leaky-wave antenna, featuring a compact double-layer cavity and a set of cross-shaped leaky irises. The double-layer cavity, consisting of a disordered cavity as the lower part and a metacavity as the upper part, is designed to provide frequency-dependent excitations. Meanwhile, the cross-shaped leaky irises, with various structural parameters and orientations, exhibit diverse radiation characteristics, allowing for the radiation of spatially low-correlated dual-polarized field patterns across different operating frequencies and rotation angles. Furthermore, due to multiple modulations inside the double-layer cavity and random orientations of the leaky irises, the radiated fields exhibit consistent amplitude levels under different conditions (i.e., frequency and rotation angle), demonstrating enhanced radiation uniformity. The performance of the proposed CMA is evaluated from two aspects: antenna-related metrics and MCPI-related metrics. Both the simulated and measured results confirm the CMA design as a promising solution for narrow-band MCPI applications.

Pencil beam broadening for large-scale antenna arrays has many applications in radar and communication systems to achieve better search and coverage performance. Current windowing methods cannot guarantee that the array gain $G_{\mathrm{{bdr}}}$ on the boundary of a specified beam region is maximized, even though the technique of parameter scanning is employed. This paper introduces a beam broadening method based on the convex quadratic programming (CQP) that maximizes $G_{\mathrm{{bdr}}}$. We first approximate the pattern cut of the broadened beam by a uniform linear array (ULA). Then an analytical formula for the number of elements ($N$) in the ULA that maximizes $G_{\mathrm{{bdr}}}$ is derived. Once $N$ has been determined, we will be able to calculate the half-power beamwidth (HPBW) of the broadened beam. Based on this information, the task of beam broadening can be formulated as a quadratic program with few constraints, which can be transformed to a CQP problem using the symmetry of array structure and solved efficiently using the interior point method (within 0.1 seconds for a $32\times 32$ rectangular array). In addition, we derive a closed-form solution to the CQP problem when the positivity constraints on the weighting coefficients are removed. Array factor analysis and full-wave simulation show that the proposed method obtains a higher beam boundary gain than the conventional windowing methods.