To meet the required coverage and data rate demands of next-generation mobile communications, the coexistence of multiple radio frequency systems operating in well-separated frequency bands in precisely identified scenarios will be exploited in the long term. In this context, it is necessary to investigate frequency-dependent and environment-dependent channel characteristics by exploring the spatial and temporal correlations of multipath channels across different frequency bands and different environments. Traditional evaluation indexes, such as channel parameters and correlation coefficients, either do not enable direct derivation of the relationship between two channels or can only be evaluated from a certain dimension mathematically. Here, by combining large-scale and small-scale channel characteristics, a channel similarity index measure (CSIM) is proposed. This structural similarity index comprehensively considers several multipath parameters of two different channels, including amplitude, phase, delay, angle of arrival (AoA), and angle of departure (AoD). The results of ray tracing simulations and field measurement campaigns conducted across both centimeter wave (cmWave) and millimeter wave (mmWave) bands in typical indoor and outdoor scenarios verify that the proposed CSIM can effectively measure similarity from specific dimensions. Moreover, the statistical distributions of the CSIM, which reflect the similarities between channels of different frequencies, are also presented.

Many full-wave electromagnetic (EM) simulations are needed to design an antenna meeting certain requirements, which involves a considerable computational burden. A multibranch machine learning-assisted optimization (MB-MLAO) method is proposed to dramatically reduce the computational complexity involved in this task. This method is then applied to antenna design and worst-case performance (WCP) searching under a practical manufacturing tolerance. In the conventional Gaussian process regression (GPR)-based MLAO method, a lower confidence bound (LCB) prescreening strategy with an empirical LCB constant is used to weigh the predicted value and predicted uncertainty. Using a variable-fidelity machine learning method, an adaptive LCB variable, and a retraining and repredicting method, the proposed MB-MLAO method can strike a delicate balance between exploitation and exploration in searching. Moreover, variable-fidelity data from full-wave EM simulations are used in the deep GPR machine learning method to further reduce the computational burden. Finally, two test functions and four types of antennas are selected as examples to illustrate the superiority of the proposed MB-MLAO method.

A prephase synthesis method is proposed for sidelobe level (SLL) suppression of a 1-bit phase-only control metasurface under plane wave incidence. The array factor of the metasurface with N×N unit cells shows that controlling the number of prephases with varying values over the reflective surface realizes equivalent amplitude tailoring. Different from optimizing the prephase distribution, selection of the numbers of 0 and π/2 prephases in specific N regions is used to suppress the SLLs. Therefore, the parameters in the optimization can be dramatically reduced from N2 to N. The prephase distribution is then designed based on the optimized number of prephases and a symmetric matrix for SLL suppression in the whole space. The SLLs are further suppressed by optimizing some of the unit cell states based on similar equivalent amplitude tailoring. Simulation and measurement of a set of 1-bit reflective metasurfaces with 20×20 unit cells verify that the phase-only control metasurface realizes SLL suppression to -13 dB for multiple beam directions from -30 to 30 degrees with a 10-degree step under normal plane wave incidence.

Very high wind loads represent one of the major problems for the ultralarge-scale 5G base station array at the sub-6 GHz band, where dozens of or hundreds of antennas are used. An ultracompact dual-polarized cross-dipole antenna with an extremely small overall projected area is presented. The array with low wind load is realized by miniaturized cross dipoles and the replacement of the traditional ground plane with a defected ground structure (DGS) and metal mesh reflector. The DGS is utilized to realize size reduction and isolation enhancement. The projected area of the antenna is reduced by 70%. Therefore, each antenna in the array can be independently packaged using a streamlined radome with a low wind load. And the inter-radome spacing is large enough to make holes that are used to further reduce wind load. The antenna prototype is designed, fabricated, and measured for the sub-1 GHz band. The measured results show that the impedance bandwidth is 680-970 MHz, the polarization isolation is higher than 20 dB, and the gain is around 6.5 dBi. It is verified that the proposed ultracompact antenna of high radiation performance is very suitable for an ultralarge-scale array of low wind load in a 5G base station.

Array synthesis under practical constraints is a vital design task. Traditional array synthesis methods usually deal with isolated antenna elements without considering mutual coupling (MC) or mounting-platform effects, which results in unacceptable degradation in practical array designs. An efficient machine learning-assisted array synthesis (MLAAS) method is introduced using efficient active base element modeling (ABEM). This method greatly extends the boundaries of practical antenna array synthesis from the perspectives of both accuracy and design freedom. Using much fewer samples than those in conventional MLAAS methods, all possible element designs are accurately modeled into one active base element (ABE). Compared with conventional active element pattern (AEP)-based methods, the ABEM aims to predict AEPs for elements with arbitrary allocations and electromagnetic (EM) surroundings, therefore offering more degrees of freedom for practical array designs. Four array design examples are used to verify the effectiveness of the proposed method.

Empirical channel modeling is necessary for the deployment of the fifth-generation (5G) millimeter-wave (mmWave) cellular system in actual environments. In this paper, cluster-based analyses of mmWave channel characteristics in two typical dense urban environments are performed. First, radio propagation measurement campaigns are conducted at two primary 5G bands of 28 GHz and 39 GHz in a central business district and a dense residential area. The custom-designed channel sounder supports high-efficiency directional scanning sounding, which helps to collect sufficient data for statistical channel modeling. Next, using an improved autoclustering algorithm, multipath clusters and their scattering sources are identified. Mapping results show that multiple reflections from exterior walls and diffraction over building corners or rooftops enhance the coverage for non-line-of-sight (NLoS) links and the influences of these propagation mechanisms are intuitively embodied as changes in the topologies of deployment environments. Finally, an appropriate measure for cluster-level channel characteristics is provided including cluster number, Ricean K-factor, root mean squared (RMS) delay spread, RMS angular spread, and their correlations. Comparisons of these parameters across two mmWave bands are also given. The measurement and modeling results shed light on a fully understanding of mmWave channels in dense urban environments across multiple bands.

A broadband dual-polarized single-layer 1-bit unit cell is proposed for achieving the independently controllable dual-beam reflectarray antenna. The unit cell independently provides two-state phase compensation for two orthogonally linearly-polarized waves. The 180-degree reflective phase difference between the two states is achieved by tuning the magnetic resonance of State 0 and the electrical resonance of State 1. With its two resonances close to each other, the unit cell has a reflective phase difference of 180±20 degrees between two states over a broad bandwidth of 27.2-51.1 GHz. The cross-polarization levels of below -30 dB ensure the high isolation between two polarizations. Using the proposed dual-polarized unit cells, a 1-bit dual-beam reflectarray antenna is designed and excited by a dual-polarized horn to show the ability of independently controlling two orthogonally linearly-polarized waves. At 33 GHz, the beams direct to -15 degrees and 20 degrees for the feeding of horizontally and vertically polarized port, respectively. The 1.5-dB gain bandwidth is greater than 20% for both polarizations. The proposed dual-polarized reflectarray antenna with independently controllable 1-bit dual beams provides an alternative design for the multiuser multiple-input multiple-output applications.

An efficient multilayer machine learning-assisted optimization (ML-MLAO)-based robust design method is proposed for antenna and array applications. Machine learning methods are introduced into multiple layers of the robust design process, including worst-case analysis (WCA), maximum input tolerance hypervolume (MITH) searching, and robust optimization, considerably accelerating the whole robust design process. First, based on a surrogate model mapping between the design parameters and performance, WCA is performed using a genetic algorithm to ensure reliability. MITH searching is then carried out using a double-layer MLAO (DL-MLAO) framework to find the MITH of the given design point. Next, based on the training set obtained using DL-MLAO, correlations between the design parameters and the MITH are learned. The robust design is carried out using surrogate models for both the performance and the MITH, and these models are updated online following the ML-MLAO scheme. Furthermore, two examples, including an array synthesis problem and an antenna design problem, are used to verify the proposed ML-MLAO method. Finally, the numerical results and computation time are discussed to demonstrate the effectiveness of the proposed method.

The clustered delay line channel model, in which each cluster consists of a number of rays, is widely used for link-level evaluations in mobile communications. Multiple parameters of each ray, including delay, amplitude, cross polarization ratio (XPR), initial phases of four polarization combinations and the azimuth and elevation angles of arrival and departure, shall be known. These parameters are measured using a channel sounder. The number of rays in every cluster is usually greater than the number of elements in the antenna array of the channel sounder, which represents a challenging issue in multipolarized channel measurements. A new subspace estimation method based on the broadband extended array response of an electromagnetic vector antenna array is proposed to resolve a large number of rays. The interelement spacing of the array can be greater than half the carrier wavelength, which reduces interelement coupling and simplifies the array design, especially for millimeter wave bands. First, the delay of each cluster is estimated using the reference antenna element. Then, the 2D angles of every ray are estimated using the classic rank-deficient multiple signal classification (MUSIC). Lastly, the initial phases, XPR and amplitude of every ray is estimated. Simulation results validate the proposed method.

A single-beam pre-phased 1-bit reflective metasurface is proposed to achieve single-beam patterns under normally incident plane waves. Theoretical analysis and numerical simulations are presented to show that, under normally incident waves, single-beam patterns can be achieved by introducing a fixed pre-phase distribution with two values in the 1-bit metasurface. Compared with conventional 1-bit reflective metasurfaces, the proposed scheme alleviates the inherent limitation of single-beam patterns on 1-bit reflective metasurfaces under normally incident plane waves. To verify the proposed scheme, a 1-bit unit cell is designed with a 180º ± 25º phase difference between the two states for frequencies ranging from 34.3 to 49.9 GHz, and a layer-stacking method is proposed to achieve two pre-phases with a 90-degree phase difference. As an example, three 1-bit reflective metasurfaces comprising 20×20 unit cells with single beams pointing separately at 0, 15 and 30 degrees are designed and measured over frequencies of 37.0 to 41.0 GHz; the measured sidelobe levels are less than -7.8 dB. Simulated and measured results show that the proposed pre-phased 1-bit metasurface can achieve single-beam patterns under normally incident plane waves.

An empirically based analysis of propagation characteristics in two vegetated suburban areas with different types and fractions of vegetation cover in 5G millimeter-wave bands is presented. A basic distance-dependent path loss model with a Gaussian random variance for shadow fading is utilized in accordance with the maximum-power directional and omnidirectional measurement data, therein exploiting significant path loss exponents in the presence of vegetation. In comparison with the existing ITU-R and 3GPP models, the effect of dense-leaved trees on path loss prediction is similar to that of buildings, whereas these standard models are inapplicable for sparse obstacle-line-of-sight (OLoS) links. Consequently, an azimuth-angle-based path loss characterization is proposed considering the antenna pattern, beam misalignment, and blockage effects. Moreover, several composite and cluster-level small-scale channel parameters, such as the number of clusters, delay spread, and angular spread, are extracted. Analysis of the first-arrival cluster in the OLoS setting reveals that forward scattering through foliage is still dominant and is expected to produce a larger azimuth angular spread of the arrival and compact multipath components in the time domain compared with line-of-sight and reflected clusters. Measurement results improve existing 3GPP channel models for suburban macrocell scenarios in millimeter-wave bands.

A dual-polarized symmetrically cross-slotted square patch (SCSSP) antenna with multimode resonance is proposed for 5G millimeter-wave broadband applications. A symmetrical cross-slot is etched on the square patch surface to change the original E-field distribution where a whole square patch is cut into four disconnected equal parts. For both polarizations, this etching also tunes the resonance frequencies of the two modes to be sufficiently close to each other. Therefore, the antenna can realize good impedance performance within the wide designated bandwidth. Dual-polarized radiation of the SCSSP is vertically excited by three bow-tie-shaped slots and fed by two orthogonal substrate integrated coaxial lines. Simulated and measured results show that the fabricated prototype achieves a broad overlapped impedance bandwidth of 50.0% (24-40 GHz), isolation higher than 30 dB between the two input ports, stable radiation pattern, and low cross-polarization over the operating band. Moreover, the proposed SCSSP antenna with compact size, planar shape, and simple vertical feeding is very well-suited for two-dimensional array design.