This paper 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 analyses have been conducted for antenna configurations in line-of-sight (LOS) environments, analyses in non-line-of-sight (NLOS) environments remain limited. Through theoretical analysis, this paper 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.

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.

In this paper, the channel of an indoor holographic multiple-input multiple-output (MIMO) system is measured. It is demonstrated through experiments for the first time that the spatial oversampling of holographic MIMO systems is able to increase the capacity of a wireless communication system significantly. However, the antenna efficiency is the most crucial challenge preventing us from getting the capacity improvement. An extended EM-compliant channel model is also proposed for holographic MIMO systems, which is able to take the non-isotropic characteristics of the propagation environment, the antenna pattern distortion, the antenna efficiency, and the polarization characteristics into consideration.

Nowadays, there is no good scheme for simultaneously achieving super-resolution imaging within a coherent beamwidth and beam scanning in microwave radar. In this paper, a synthetic beam scanning method based on a randomly excited antenna array, which possesses the ability of super-resolution coincidence imaging, is proposed. The beamwidth of the randomly excited array is comparable to that of the conventional phased array with the same size, and its steering angle can be adjusted by modulating the excitation signals. Firstly, the optimized covariance matrix of the proposed array to uniformly bunch the spatial radiation energy in a specific angle range is obtained by means of the sequential quadratic programming (SQP) algorithm. Accordingly, the partially correlated excitation signals restricted by the optimized covariance matrix are determined. Then, a secondary weighted modulation method is proposed to steer the main beam to the direction of interest. The excitation signals are weighted by the conjugated radiation fields in the direction of interest. Furthermore, the super-resolution ability of the proposed coincidence imaging system is analyzed based on the first-order statistical characteristics of the radiation fields. Simulations and experiments demonstrate that synthetic beam scanning can be realized by controlling the excitation signals. Besides, a 10 times super-resolution coincidence image can be reconstructed within a coherent beamwidth (3dB-beamwidth) when the signal-to-noise (SNR) is greater than 15 dB, assisted by the fast Bayesian learning (FBL) algorithm.

The mutual coupling between antenna elements significantly deteriorates the performance of antenna arrays. Many decoupling techniques have been developed over the years. A key step of decoupling processing is to obtain the decoupling matrix (or mutual impedance matrix). However, the accurate decoupling matrix is unavailable in practice because mutual coupling depends not only on the geometry of the array, but also on the excitations of the elements. Unfortunately the decoupling errors can also lead to severe performance degradation of antenna arrays, especially for the adaptive beamformers. In this paper we propose a decoupling error reduction method based on the reduced- rank multistage Wiener filter (MWF). The reduced-rank MWF projects the weight vector of beamformer onto the Krylov subspace, which has a regularization effect that can reduce the performance degradation caused by random errors in the array. Besides having a simple implementation structure, the proposed method is computationally efficient. Moreover, simulation results show that the proposed method considerably improves the signal-to-interference-plus-noise ratio (SINR) of the beamformer when there exist decoupling errors in the array.

The sectored multi-probe anechoic chamber (MPAC) is regarded as a promising millimeter-wave (mmWave) over-the-air (OTA) testing solution. However, there are still debates in the literature about which channel emulation method, i.e., pre-faded synthesis (PFS) or plane wave synthesis (PWS), should be used for the sectored MPAC testing system. In this paper, a thorough comparison between the two channel emulation methods is conducted. It is found that for sectored MPAC (where the active probes are unlikely to be uniformly distributed over the probe panel) the PFS is more accurate than the PWS. Explanations are given.

A super-resolution computational imaging method, called post random modulation radar imaging, based on single-input multiple-output radar detection system is proposed in this letter. In the proposed method, the target is detected in coherent mode using the multilinear frequency modulation signal. The echoes received by the radar elements of the receiving array are recorded, respectively. Then, random modulations of the directional pattern factor of the receiving radar array, which are nonlinear processes, are optimized according to the position and the size of the target that are estimated roughly using the traditional method. Finally, the super-resolution radar imaging is obtained to solve the equation group formed by the data from the nonlinear post random modulation process. The resolution of the proposed imaging method can break the diffraction limit corresponding to the aperture of the receiving radar array. The anti-interference ability of the proposed approach is improved significantly compared with the incoherent imaging method using a multiple-input single-output structure. Experiments are carried out to validate the proposed approach.