Base Station Antennas Research Articles

Dual-Band Dual-Polarized Planar Antenna for 5G Millimeter-Wave Antenna-in-Package Applications

This article presents a dual-band, dual-polarized antenna operating from 24 up to 40 GHz at 5G new radio (NR) millimeter-wave (mm-Wave) Frequency Range 2 (FR2) bands. A novel stacking arrangement of ring patches is proposed to achieve wide dual-band operation with stable gain. Two pairs of stacked ring patches operating at lower (24.25–29.5 GHz) and upper (37–40 GHz) FR2 bands are alternately integrated at four metal layers. The antenna offers a sharp roll-off and a filter-like response with radiation nulls between the operating bands due to the stacking configuration. The antenna is aperture-coupled and stripline-fed, and it conforms to the antenna-in-package (AiP) requirements. The proposed single antenna element and a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> array are fabricated utilizing a standard commercial printed circuit board (PCB) fabrication facility. The measurement results show a decent agreement with the simulations in terms of impedance matching, isolation, radiation patterns, and realized gain. The proposed design is a promising candidate for 5G mm-Wave base station antenna arrays.

Design of Novel Ultra-wideband Slow-wave Microstrip Transmission Line

Microwave passive devices with high efficiency, small size, and light weight are one of the fundamental requirements for mobile communication coverage in the 5G frequency band. In this paper, the design of a novel ultra-wideband slow-wave microstrip transmission line is proposed and studied. The electromagnetic field simulation software HFSS is used to simulate the slow-wave microstrip transmission line, and the simulation results show that the ultra-wideband slow-wave structure has the advantage of wider bandwidth, smaller size, and lower cost, which meets the needs of 5G communication systems. Finally, the proposed slow-wave microstrip line is used in the U-shaped phase shifter, and the experimental results show that the novel slow-wave microstrip transmission line has a large application potential in the base station antenna system.

Design of high gain base station antenna array for mm-wave cellular communication systems

Millimeter wave (mm-Wave) wireless communication systems require high gain antennas to overcome path loss effects and thereby enhance system coverage. This paper presents the design and analysis of an antenna array for high gain performance of future mm-wave 5G communication systems. The proposed antenna is based on planar microstrip technology and fabricated on 0.254 mm thick dielectric substrate (Rogers-5880) having a relative permittivity of 2.2 and loss tangent of 0.0009. The single radiating element used to construct the antenna array is a microstrip patch that has a configuration resembling a two-pronged fork. The single radiator has a realized gain of 7.6 dBi. To achieve the gain required by 5G base stations, a 64-element array antenna design is proposed which has a bore side gain of 21.2 dBi at 37.2 GHz. The 8 × 8, 8 × 16, and 8 × 32 antenna array designs described here were simulated and optimized using CST Microwave Studio, which is a 3D full-wave electromagnetic solver. The overall characteristics of the array in terms of reflection-coefficient and radiation patterns makes the proposed design suitable for mm-Wave 5G and other communication systems.

Analysis of scalable channel estimation in FDD massive MIMO

One of the key ideas for reducing downlink channel acquisition overhead for FDD massive MIMO systems is to exploit a combination of two assumptions: (i) the dimension of channel models in propagation domain may be much smaller than the next-generation base-station array sizes (e.g., 64 or more antennas), and (ii) uplink and downlink channels may share the same low-dimensional propagation domain. Our channel measurements demonstrate that the two assumptions may not always hold, thereby impacting the predicted performance of methods that rely on the above assumptions. In this paper, we analyze the error in modeling the downlink channel using uplink measurements, caused by the mismatch from the above two assumptions. We investigate how modeling error varies with base-station array size and provide both numerical and experimental results. We observe that modeling error increases with the number of base-station antennas, and channels with larger angular spreads have larger modeling error. Utilizing our modeling error analysis, we then investigate the resulting beamforming performance rate loss. Accordingly, we observe that the rate loss increases with the number of base-station antennas, and channels with larger angular spreads suffer from higher rate loss.

Decentralized Groupwise Expectation Propagation Detector for Uplink Massive MU-MIMO Systems

With the proliferation of emerging Internet of Things (IoT) applications, massive multiuser multiple-input–multiple-output (MU-MIMO) is a promising technology to support the massive connectivity requirement with limited spectral resources. The existing detection algorithms are mainly designed based on a centralized baseband processing architecture, which requires extremely high raw baseband data rates transferred between base-station antennas and the central processing unit (CPU). Decentralized baseband processing (DBP) architecture has recently been proposed to alleviate high interconnect data rates and chip input/output bandwidth bottlenecks. Since the information is not fully shared among each antenna cluster, conventional decentralized detectors suffer from significant performance loss, especially for systems with high overload ratios and/or spatially correlated channels. In this work, we first propose a decentralized groupwise detection paradigm for the star architecture by dividing users into multiple user groups, which can be effectively exploited by factor graph-based message passing algorithms, such as the expectation propagation (EP) algorithm. Then, an efficient message fusion rule is devised based on the product principle of the multivariate Gaussian probability density function at the CPU. To reduce the computational complexity, an approximate groupwise EP (AGW-EP) method is proposed by judiciously selecting reliable constellation vectors during the symbol belief calculation phase. In addition, we extend the proposed methods to the daisy-chain architecture, which requires constant interconnect data rates. Simulation results demonstrate that the proposed groupwise paradigm greatly enhances the performance of the conventional EP detector. Moreover, the proposed decentralized groupwise EP and AGW-EP detection schemes outperform their counterparts, particularly in correlated MIMO channels.

Suppression of Common-Mode Resonance in Multiband Base Station Antennas

5G demands a significant increment in the number of connected devices. As a result, gNodeBs are constantly pushed to serve more spectrum and smaller sectors. These increased capacity demands are met by using multiband antennas in base stations. One of the key challenges with multiband antennas is the pattern distortions due to the presence of other surrounding antenna element structures. This work provides a novel approach to address the challenge of pattern distortion in the lower frequency band 690-960 MHz due to common-mode (CM) currents in the high- frequency-band antenna element operating in the 1810-2690 MHz band. A common-mode suppression circuit is integrated with the impedance matching network of the high-band antenna element to reduce these common-mode currents. The experimental results verified that the common-mode suppression circuit reduces the common-mode currents at low-band frequencies by moving the common-mode resonance frequency outside the low frequency band, resulting in cleaner low-band patterns meeting pattern specifications.

Design of compact dual‐band shared‐aperture antenna system based on frequency selective surface

AbstractA miniaturized dual‐band dual‐polarization base station antenna array operating at 2.4–2.8 and 4.6–5.0 GHz is proposed in this paper. The upper‐band unit is embedded in the lower‐band unit to miniature the array size. The size of the proposed shared‐aperture antenna element is 0.28 × 0.28 . To ensure the normal radiation of dual‐band under the small frequency ratio, a meander slot frequency selective surface structure is introduced, which is electromagnetically transparent to the lower‐band unit and function as metal ground for the upper‐band unit to ensure the normal radiation of the upper and lower‐band units, respectively. Simulation and measured results show that the antenna array meets the following design specifications: dual‐band operating (voltage standing wave ratio [VSWR] &lt; 2), mutual coupling less than −20 dB basically, satisfactory gain and radiation characteristics.

Structured channel covariance estimation from limited samples for large antenna arrays

In massive multiuser multiple antenna systems, the knowledge of the users’ channel covariance matrix is crucial for minimum mean square error channel estimation in the uplink and it plays an important role in several multiuser beamforming schemes in the downlink. Due to the large number of base station antennas, accurate covariance estimation is challenging especially in the case where the number of samples is limited and thus comparable to the channel vector dimension. As a result, the standard sample covariance estimator may yield a too large estimation error which in turn may yield significant system performance degradation with respect to the ideal channel covariance knowledge case. To address such problem, we propose a method based on a parametric representation of the channel angular scattering function. The proposed parametric representation includes a discrete specular component which is addressed using the well-known MUltiple SIgnal Classification (MUSIC) method, and a diffuse scattering component, which is modeled as the superposition of suitable dictionary functions. To obtain the representation parameters, we propose two methods, where the first solves a nonnegative least-squares problem and the second maximizes the likelihood function using expectation–maximization. Our simulation results show that the proposed methods outperform the state of the art with respect to various estimation quality metrics and different sample sizes.

Shaping flattened scattering patterns in broadband using passive reconfigurable intelligent surfaces for indoor NLOS wireless signal coverage enhancement

A novel Passive Reconfigurable Intelligent Surface (PRIS) with 100 � 100 elements, which is shaped to scatter flattened patterns in broadband, is presented to enhance the signal coverage of the indoor Non-Line-of-Sight (NLOS) area for 5G millimeter wave (mmWave) wireless communications. The signal from the base station antenna as a primary source can be deflected to the NLOS area by the proposed PRIS with a flattened scattering pattern that is insusceptible to being blocked. The broadband meta-atom, realized by the framed four arrow branches, operates from 24 GHz to 30 GHz. The genetic algorithm is applied to optimize the phase shift distribution of PRIS for a shaped pattern. Combining full-wave simulation and 3D ray-tracing simulation, the signal coverage is computed in an indoor L-shaped corridor scenario for 5G mmWave wireless communications. To verify the design, the proposed PRIS is further manufactured and tested. The experimental results reveal that the signal enhancement in the NLOS area by the proposed PRIS with the flattened scattering pattern is superior to the conventional design.

Recent Advances of Ultramassive Multiple-Input, Multiple-Output Technologies: Realizing a Sixth-Generation Future in Spatial and Beam Domains

To explore the full potential of ultramassive multiple-input, multiple-output (MIMO) communication systems, it is fundamental to understand new ultramassive MIMO channel characteristics and establish pervasive channel models. On this basis, large-dimensional spatial-temporal transmission and random access technologies need to be investigated and evaluated for better practical implementation. First, this article reviews recent advances of ultramassive MIMO technologies in the traditional spatial domain, including wireless channel characterization and modeling, channel estimation, spatial multiplexing, and precoding. Second, considering the dramatic increase of base station (BS) antennas and access users in ultramassive MIMO systems, the confronted high-dimensional complexity and computing burden of these ultramassive MIMO technologies are indicated. To provide an efficient and systematic solution, the emerging tendency to transform related technologies from the traditional spatial domain to the beam domain is introduced. The utilities of large sparsity merit, reduced energy consumption, and improved usage of radio frequency (RF) chains in the beam domain channel are elaborated. Last, future challenges of ultramassive MIMO communication systems are discussed.

The Use of Machine Learning Techniques for Optimal Multicasting in 5G NR Systems

Multicasting is a key feature of cellular systems, which provides an efficient way to simultaneously disseminate a large amount of traffic to multiple subscribers. However, the efficient use of multicast services in fifth-generation (5G) New Radio (NR) is complicated by several factors, including inherent base station (BS) antenna directivity as well as the exploitation of antenna arrays capable of creating multiple beams concurrently. In this work, we first demonstrate that the problem of efficient multicasting in 5G NR systems can be formalized as a special case of multi-period variable cost and size bin packing problem (BPP). However, the problem is known to be NP-hard, and the solution time is practically unacceptable for large multicast group sizes. To this aim, we further develop and test several machine learning alternatives to address this issue. The numerical analysis shows that there is a trade-off between accuracy and computational complexity for multicast grouping when using decision tree-based algorithms. A higher number of splits offers better performance at the cost of an increased computational time. We also show that the nature of the cell coverage brings three possible solutions to the multicast grouping problem: (i) small-range radii are characterized by a single multicast subgroup with wide beamwidth, (ii) middle-range deployments have to be solved by employing the proposed algorithms, and (iii) BS at long-range radii sweeps narrow unicast beams to serve multicast users.

A Multihop Strategy for the Planning of EM Skins in a Smart Electromagnetic Environment

This article deals with the electromagnetic (EM) planning of static and passive EM skins (EMSs) in complex urban scenarios to improve the EM coverage of the base-station (BTS) antenna by means of a set of multihop (MH) cascaded reflections. Toward this end, an innovative system-by-design (SbD)-based method is developed where wireless links of arbitrary “depth,” which are implemented through multiple anomalous cascaded reflections, are optimized. Representative numerical results, concerned with real-world scenarios, are analyzed to assess the capabilities and the potentialities of such an instance of the so- called smart EM environment (SEME).

Dual-Broadband Dual-Polarized Base Station Antenna Array With Stable Radiation Pattern

This letter presents an interleaved dual-broadband dual-polarized base station antenna array consisting of a lower-band element and two upper-band subarrays. L-shaped strips are loaded in the lower-band radiator to reduce its scattering effect on the upper-band radiation. Cross-dipoles fed by coupling technique are innovated for the upper-band radiator to achieve wide bandwidth. In addition, unshorted baluns are proposed to reduce the detrimental effect of the induced current on the radiation performance between the two bands. The lower- and upper-bands range from 660–970 MHz and 1.6–2.9 GHz, corresponding to 38% and 57.8% bandwidths, respectively. Very stable radiation patterns, wide bandwidths, and good port isolations are achieved for both bands.

Fast Fluid Antenna Multiple Access Enabling Massive Connectivity

Massive connectivity over wireless channels relies on aggressive spectrum sharing techniques. Conventionally, this may be achieved by sophisticated signal processing and optimization of applying multiple antennas and/or complex multiuser decoding at each user terminal (UT). Different from previous methods, this letter proposes a radical approach for massive connectivity, which employs fluid antenna at each UT to exploit the interference null, created naturally by multipath propagation and the randomness of UT’s data, on a symbol-by-symbol basis for multiple access. The proposed fast fluid antenna multiple access ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> -FAMA) system adopts a large, distributed antenna array at the base station (BS) to transmit each UT’s signal from each of the BS antennas and lets each UT overcome the interference on its own using its fluid antenna. Our main contribution is a technique that estimates the best port of fluid antenna for reception at every symbol instance. The proposed approach needs only cross-correlation calculations and single-user decoding at each UT and requires no precoding at the BS. Simulation results demonstrate that for a BS with 16 antennas supporting 16 co-channel users, a multiplexing gain of 14.87 is achieved even when the channel has a strong line-of-sight (LoS) and multipath is few. The multiplexing gain can also rise to 24.36 if a 30-antenna BS is serving 30 co-channel users.

Ultra-Wideband Dual-Polarized Base-Station Antenna With Stable Radiation Pattern

This communication presents an ultrawideband dual-polarized antenna consisting of a main radiator, parasitic elements, and a ground plane. The main radiator is composed of two pairs of dual dipoles, which are cross-arranged and fed by coplanar-strip lines in order to achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pm \mathrm{45}^{\circ }$ </tex-math></inline-formula> dual-polarization within a wide frequency band. Parasitic elements, including a circular patch and four pairs of dipole strips, are employed for improving the radiation performance in high frequencies, which is very essential for stabilizing the radiation pattern over the entire bandwidth. The proposed antenna can cover the frequency range from 1.7 to 5.1 GHz for both polarizations, corresponding to an ultrawide bandwidth of 100%. The gain and half-power beamwidth (HPBW) are very stable within the entire bandwidth, which are <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8.2~\pm ~0.7$ </tex-math></inline-formula> dBi and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm{65}^{\circ }\pm \mathrm{5}^{\circ }$ </tex-math></inline-formula> , respectively. The measured results agree well with simulated ones, indicating that the proposed antenna is a very promising candidate for the future base-station communication system.

Design, Challenges and Developments for 5G Massive MIMO Antenna Systems at Sub 6-GHz Band: A Review.

Massive multiple-input multiple-output (mMIMO) is a wireless access technique that has been studied and investigated in response to the worldwide bandwidth demand in the wireless communication sector (MIMO). Massive MIMO, which brings together antennas at the transmitter and receiver to deliver excellent spectral and energy efficiency with comparatively simple processing, is one of the main enabling technologies for the upcoming generation of networks. To actualize diverse applications of the intelligent sensing system, it is essential for the successful deployment of 5G-and beyond-networks to gain a better understanding of the massive MIMO system and address its underlying problems. The recent huge MIMO systems are highlighted in this paper's thorough analysis of the essential enabling technologies needed for sub-6 GHz 5G networks. This article covers most of the critical issues with mMIMO antenna systems including pilot realized gain, isolation, ECC, efficiency, and bandwidth. In this study, two types of massive 5G MIMO antennas are presented. These types are used depending on the applications at sub-6 GHz bands. The first type of massive MIMO antennas is designed for base station applications, whereas the most recent structures of 5G base station antennas that support massive MIMO are introduced. The second type is constructed for smartphone applications, where several compact antennas designed in literature that can support massive MIMO technology are studied and summarized. As a result, mMIMO antennas are considered as good candidates for 5G systems.

A Wideband Base Station Antenna Loaded with Bow-Tie-Like Parasitic Elements

A ±45° dual-polarization wideband antenna for 5G base station application is proposed using cross dipoles, bow-tie-like parasitic elements and baluns. The antenna is modeled, simulated and optimized to get an optimal size of 115mm × 115mm × 27.8mm. The antenna has a -10dB bandwidth of 2.24GHz-3.75GHz and a fractional bandwidth of 50.4% and its isolation of two orthogonal ports is exceed than 25dB over the operating frequency band. The dual-polarized antenna is fabricated and measured to get a result that it has wide bandwidth, good directional radiation patterns, and high peak gain of 8.1±1.1dBi, making it suitable for 5G base station applications.

A Unified Framework for Full-Duplex Massive MIMO Cellular Networks With Low Resolution Data Converters

In this work, we provide a unified framework for full-duplex (FD) massive multiple-input multiple-output (MIMO) cellular networks with low-resolution analog-to-digital and digital-to-analog converters (ADCs and DACs). An objective of this work is to derive an accurate model to account for a wide variety of network irregularities and imperfections, including loopback self-interference (SI) that arises in full-duplex systems and quantization error from low-resolution data converters. Our contributions for forward and reverse links include (1) deriving signal-to-quantization-plus-interference-plus-noise ratio (SQINR) under pilot contamination, linear minimum mean square error (LMMSE) channel estimation and channel hardening; (2) deriving closed-form and approximate analytical expressions of spectral efficiency; (3) deriving asymptotic results and power scaling laws with respect to the number of quantization bits, base station antennas, and users, as well as base station and user equipment power budgets; and (4) analyzing outage probability and spectral efficiency vs. cell shape, shadowing, noise, cellular interference, pilot contamination, pilot overhead, and frequency reuse. Cell shapes include hexagonal, square, and Poisson Point Process (PPP) tessellations. In simulation, we quantify spectral and energy efficiency as well as the impact of SI power, inter-user interference, and cell shape on outage probability. We carry out the analysis for sub-7 GHz long term evolution (LTE) bands and then extend the framework to support millimeter wave (mmWave) bands.

Optimal Power Allocation With Multiple Joint Associations in Multi-User MIMO Full-Duplex Systems

Optimum power allocation is an effective way to mitigate residual self-interference and inter-user interference in multiple input multiple output full-duplex (FD) systems. However, current research mainly considers parts of influencing factors and sets service models fixed. Given this, we comprehensively focus on three perspectives in a novel power allocation method, which involve the muting management (MM) and the assignment of both base station antennas and subcarriers in the FD system. Then, we formulate an optimization problem to maximize the total spectrum efficiency. According to the categories of variables in the nonconvex objective function, we first propose a hierarchical algorithm, which is decomposed into the first-order Taylor approximation (FOTA) method and the greedy algorithm. The continuous and discrete variables related subproblems are solved through FOTA and greedy algorithm, respectively. Among them, the greedy algorithm is an alternative to a traditional method of exhaustive search. Considering the high complexity of the greedy algorithm, we further introduce deep reinforcement learning (DRL) instead to solve the corresponding subproblem. Thus, two Double Deep Q-learning Networks are constructed to train the samples in each sub-slot. Simulation results validate that the hybrid DRL-convex method outperforms the hybrid greedy-convex method. Meanwhile, the MM introduced scheme’s performance gain is more evident than that of the method without MM in many scenarios.

Rethinking WMMSE: Can Its Complexity Scale Linearly With the Number of BS Antennas?

Precoding design for maximizing weighted sum-rate (WSR) is a fundamental problem for downlink of massive multi-user multiple-input multiple-output (MU-MIMO) systems. It is well-known that this problem is generally NP-hard due to the presence of multi-user interference. The weighted minimum mean-square error (WMMSE) algorithm is a popular approach for WSR maximization. However, its computational complexity is cubic in the number of base station (BS) antennas, which is unaffordable when the BS is equipped with a large antenna array. In this paper, we consider the WSR maximization problem with either a sum-power constraint (SPC) or per-antenna power constraints (PAPCs). For the former, we prove that any nontrivial stationary point must have a low-dimensional subspace structure, and then propose a reduced-WMMSE (R-WMMSE) with linear complexity by exploiting the solution structure. For the latter, we propose a linear-complexity WMMSE approach, named PAPC-WMMSE, by using a novel recursive design of the algorithm. Both R-WMMSE and PAPC-WMMSE have simple closed-form updates and guaranteed convergence to stationary points. Simulation results verify the efficacy of the proposed designs, especially the much lower complexity as compared to the state-of-the-art approaches for massive MU-MIMO systems.