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.

Multibeam antenna (MBA) radiations provide linkages to multiple nodes and can bring enhanced communication quality with reduced power consumption for Internet of Things (IoT) applications. In this article, a novel method providing a low-cost and flexible MBA solution is proposed. Multiple orbital angular momentum (OAM) modes are used as internal excitations of the antenna, and the generated multiple beams are pencil beams with no OAM mode, which is convenient for IoT nodes connection. First, aperture phase distributions that convert incident vortex beams with different and orthogonal topological orders into designated directional pencil beams are acquired and superimposed to form a metasurface aperture that, under excitations of vortex beams with multiple orders, produces multibeam each with a predefined direction. The proposed multibeam method is then illustrated with a transmissive metasurface design, with four beams pointing at arbitrary half-space directions. Techniques, such as OAM modes spacing and aperture partitioning are utilized for reducing sidelobe levels (SLLs). Furthermore, a similar multibeam metasurface antenna with the additional linear-to-circular polarization conversion function is demonstrated, which opens the possibility of utilizing different polarizations on top of the OAM-based multibeam. This article successfully explores and achieves a novel antenna system with multiple radiations suited for multiple IoT nodes accessing with low cost and low energy consumption, and also provides a new degree-of-freedom for OAM applications.

A parameter extraction method for the LC circuit of a dual-band bandpass filter (DB-BPF) based on the fully connected network is proposed in this article. The network learning process consists of two stages: 1) pretraining and 2) fine-tuning. The network after pretraining has the ability to output the inaccurate values of LC circuit parameters with the desired${S}$-parameters (obtained according to the filter design requirements) as input. The precise LC circuit parameters can be obtained from the network during the subsequent fine-tuning process. This parameter extraction method can be applied to LC circuits of various bandpass filters. In addition to the amplitude of S-parameter, the phase of S-parameter is also used as learning data for the pretraining network. Comparative experiments show that the most accurate LC circuit parameters can be predicted using the network trained with phase information compared with the network trained without phase information. Two 7-order narrowband and two 9-order wideband DB-BPFs with four transmission zeros at different center frequencies are designed to demonstrate the validity of the proposed method, and the experimental results show that the proposed method is valid and effective.

The resolution of the microwave radar coincidence imaging (MRCI) can break the diffraction limit, which has been validated by experiments. However, there is still no theoretical analysis. In this article, the resolution of the MRCI is theoretically analyzed using the orthogonal subspace projection algorithm based on the space spanned by the discrete reference radiation mode. First, a target location estimate (TLE) method using the data from the nonfocusing radar array of the MRCI system is proposed to estimate the target position assisted by the equivalent detection method. The estimation precision of the TLE method is approximately equal to the 3-dB beamwidth of the coherent transmitting radar array with the same aperture; hence, the imaging plane can be obtained. Then, the equivalent internal noise (generated by the error of the target distance estimation, i.e., the location of the imaging plane) is theoretically analyzed. Finally, the imaging resolution threshold of the MRCI system is theoretically analyzed. The relationship between the resolution threshold and the factors (such as the deployment of the transmitting radar array, the distance between the target and the radar array, and the signal-to-noise ratio of the imaging system) is summarized. The proposed estimation method and the analyses of the MRCI system are validated through a set of simulations and experiments.

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.

The spatial modulation coincidence imaging (SMCI), as a novel kind of microwave coincidence imaging method, is proposed in this paper. The SMCI system provides a new way to produce the time-space independent signal instead of multitransmitter architecture with wideband randomly modulated signal in radar coincidence imaging. Due to some special features, metamaterial plate is utilized as the reflective surface to modulate the incident signal to construct random radiation field. The resolution of SMCI system is analyzed under large viewing angle with two different transmitting signals. Reflective surface is nonuniformly divided to derive the expression of resolution. The analysis results show that the resolution of SMCI system is mainly determined by the size of reflective surface and center frequency, which is similar to the traditional aperture. The SMCI system is low cost and flexible in design. Simultaneously, it can avoid the synchronization problem between subsources. Moreover, the SMCI system can achieve the resolution of space target through single transmitter-single receiver radar system. High-resolution image can be reconstructed since the tests are nonlinear. Finally, a series of simulation experiments is presented based on the nondirect-viewing scene we proposed. Using the algorithm based on a compressed sensing theory, we reconstructed the target image with high resolution.

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.

The orthogonal frequency division multiplexing (OFDM) based ground-penetrating radar (GPR) has been proven to have many advantages. The estimation of amplitudes and time delays of interface echoes is significant particularly in GPR systems for pavement profiling. For the recently developed OFDM-based GPR systems, this letter proposes an algorithm for precisely estimating the echo amplitudes and time delays. We estimate the propagation channel in frequency domain and transform the resulted channel frequency response to time domain. Considering the reduction of the channel impulse response (CIR) leakage caused by IFFT, a windowing method is applied before the IFFT. The challenge is that the general windowing method leads to resolution decrease and peak value losses for the windowed CIR, which contains the echo amplitude and time delay information. We design a window that causes very little echo amplitude loss and improves the estimation resolution. In this way, the echo amplitudes and time delays can be obtained from the largest few extreme values of the windowed CIR. The algorithm performs a low complexity, and the simulation results show that the estimation errors for both amplitudes and time delays are small with the range resolution improved.

In this paper, we propose an automatic algorithm for estimating objects depth using f-k migration and velocity scanning method for the homogeneous medium. Experimental results show that the relative estimating error of depth is as low as 10%.