As the backbone of the fifth-generation (5G) cellular network, massive multiple-input multiple-output (MIMO) encounters a significant challenge in practical applications: how to deploy a large number of antenna elements within limited spaces. Recently, holographic communication has emerged as a potential solution to this issue. It employs dense antenna arrays and provides a tractable model. Nevertheless, some challenges must be addressed to actualize this innovative concept. One is the mutual coupling among antenna elements within an array. When the element spacing is small, near-field coupling becomes the dominant factor that strongly restricts the array performance. Another is the polarization of electromagnetic waves. As an intrinsic property, it was not fully considered in the previous channel modeling of holographic communication. The third is the lack of real-world experiments to show the potential and possible defects of a holographic communication system. In this paper, we propose an electromagnetic channel model based on the characteristics of electromagnetic waves. This model encompasses the impact of mutual coupling in the transceiver sides and the depolarization in the propagation environment. Furthermore, by approximating an infinite array, the performance restrictions of large-scale dense antenna arrays are also studied theoretically to exploit the potential of the proposed channel. In addition, numerical simulations and a channel measurement experiment are conducted. The findings reveal that within limited spaces, the coupling effect, particularly for element spacing smaller than half of the wavelength, is the primary factor leading to the inflection point for the performance of holographic communications.

Array pattern synthesis with low sidelobe levels is widely used in practice. An effective way to incorporate sensor patterns in the design procedure is to use numerical optimization methods. However, the dimension of the optimization variables is very high for large-scale arrays, leading to high computational complexity. Fortunately, sensor arrays used in practice usually have symmetric structures that can be utilized to accelerate the optimization algorithms. This paper studies a fast pattern synthesis method by using the symmetry of array geometry. In this method, the problem of amplitude weighting is formulated as a second-order cone programming (SOCP) problem, in which the dynamic range of the weighting coefficients can also be taken into account. Then, by utilizing the symmetric property of array geometry, the dimension of the optimization problem as well as the number of constraints can be reduced significantly. As a consequence, the computational efficiency is greatly improved. Numerical experiments show that, for a uniform rectangular array (URA) with 1024 sensors, the computational efficiency is improved by a factor of 158, while for a uniform hexagonal array (UHA) with 1261 sensors, the improvement factor is 284.

The embedded element efficiency (EEE), which quantifies mutual coupling in antenna arrays, critically limits the signal-to-noise ratio in large-scale array communications. However, calculating the EEE for finite arrays remains challenging. Existing methods are either computationally expensive or fail to account for edge effects in finite arrays. In this letter, an efficient method to estimate the EEE for finite arrays is proposed. We first derive the impedance matrix of an array from the pattern overlap matrix, then compute the generalized scattering parameters for a given source network. Consequently, the EEE of each element can be determined. Compared with traditional approaches, this method captures the EEE variations across different elements while maintaining high computational efficiency, and is applicable to arrays with arbitrary geometries. Channel capacity is evaluated to illustrate the impact of EEE on multiple-input multiple-output (MIMO) systems.

The Flying Ad-Hoc Network (FANET) is an important component of the 6G communication system. In order to achieve the precise positioning of unmanned aerial vehicle (UAV) nodes in a FANET when satellite navigation signals are unavailable, simple and accurate direction-of-arrival (DOA) estimation methods are required. In this paper, we propose an improved correlative interferometer method to estimate the DOAs of the UAVs in a FANET. This method adopts a uniform funnel array (UFA) configuration, which consists of a uniform circular array (UCA) and an additional element located above the center. This configuration improves the estimation accuracy for DOAs with large polar angles because it utilizes the degree of freedom in the vertical aperture. In addition, the Fibonacci sampling strategy is employed to overcome the polar clustering phenomenon exhibited by latitude–longitude sampling. Furthermore, in the interferometer, only partial phase differences are used to reduce the storage burden. When calculating the similarity function, we adopt the triangular function instead of the cosine function to improve computational efficiency. The simulation results show that the proposed UFA improves the DOA estimation accuracy by $65.56\%$ over the planar UCA for signals with large polar angles. Moreover, Fibonacci sampling improves the DOA estimation accuracy by $11.54\%$ as compared to the latitude–longitude sampling.

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.