Base Station Antennas Research Articles

Antena Tatasusun Pantulan Boleh Konfigurasi Semula: Latar Belakang, Klasifikasi dan Mekanisme Boleh Konfigurasi

Reflectarray antennas have emerged as one of the most suitable choice for 5G network base station antennas due to their ability to combine the advantages of parabolic reflectors and phased array antennas. These antennas offer a lightweight structure, high gain, and low impedance mismatch losses. High gain and low loss are crucial factors in ensuring the antenna delivers low latency and high-quality 5G radio signals. The application of 5G technology becomes more practical and efficient with the capability to adjust the functionality of a single 5G antenna or enable reconfiguration. This reconfigurable technique simplifies the multi-antenna systems and significantly reduces costs, making it more practical and systematic. Furthermore, this reconfigurability is particularly beneficial for satellite applications, as it allows for the reconfiguration of the signal in the event of satellite movement on the space. This paper aims to discuss the historical development of reconfigurable reflectarray antennas, focusing on the innovations in each configurable mechanism. The paper further explores the classification of configurations employed by this antenna, encompassing frequency configuration, radiation pattern, polarization, and hybrid configurations that integrate multiple existing configuration classifications. Finally, the research paper provides a discussion of the most recent research and innovations in the configurable mechanisms of reflectarray antennas.

Spectral and Energy Efficiency Trade-Off in UAV-Based Olive Irrigation Systems

Precision agriculture, also referred to as smart farming, is one of the main pillars of modern society to achieve the Sustainable Development Goals (SDGs). Precision agriculture aims to improve the quality and quantity of production while conserving scarce natural resources. Smart farming has grown in recent years thanks to the adoption of modern technologies, including artificial intelligence (AI) and the Internet of Things (IoT). In this work, we consider an irrigation system for olive orchards based on unmanned aerial vehicles (UAVs). Specifically, UAVs ensure remote sensing (RS), which offers the advantage of collecting vital information on a large temporal and spatial scale (which cannot be achieved with traditional technologies). However, UAV-based irrigation systems face tremendous challenges due to the various requirements of a powerful computing ability, battery capacity, energy efficiency, and spectral efficiency for different connected devices. This paper addresses the energy efficiency and spectral efficiency trade-off problem of UAV-based irrigation systems. We propose to adopt massive multiple input, multiple output (M-MIMO) technology to ensure wireless communication. In fact, this technology plays a significant role in future sixth-generation (6G) wireless mobile networks and has the potential to enhance the energy efficiency as well as the spectral efficiency. We design a network model with a three-layered architecture and analytically compute the achievable spectral efficiency and the energy efficiency of the studied system. Then, we numerically determine the optimal number of ground base station antennas as well as the optimal number of IoT devices that should be used to ensure the maximum energy efficiency while guaranteeing a high spectral efficiency. The numerical results prove that the proposed UAV-based irrigation system outperforms conventional systems and demonstrate that the best spectral and energy efficiency trade-off is obtained by using the M-MMSE combiner.

Robust precoder design with transmit antenna selection for overlay cognitive radio massive MIMO system

Massive MIMO systems were utilized in overlay Cognitive Radio (CR) networks to allow simultaneous primary and secondary data transmission while enhancing the spectral efficiency. However, the precoder design of a massive MIMO system especially in the overlay CR paradigm usually has a trade-off between achieved data rate and complexity. In this paper, the precoder design for overlay Cognitive Radio (CR) massive MIMO system is introduced, where a two-stage precoder that accommodates Transmit Antenna Selection (TAS) is proposed for the Secondary Base Station (SBS). The unselected antennas in the cognitive base station are proposed to be utilized to relay the data of the Primary Users (PUs) to maximize the network sum rate. The proposed precoder design for primary data aims to cancel the interference inflicted on Secondary Users (SUs) as a result of relaying the primary data. Moreover, a fast algorithm is proposed which aims to select and distribute the SBS antennas for primary and secondary data transmission to achieve better data and convergence rates, this algorithm is called Transmit Antenna Selection and Distribution (TASD). Simulation results show that the proposed precoders' design and the TASD algorithm enhance the total system sum rate (primary and secondary rates) compared to the conventional TAS (where the unselected antennas were not used to relay PUs' data). This enhancement was achieved by relaying primary data without affecting the rate of SUs. As a result, the computational time of the TASD algorithm is reduced by 52% compared to the full array case while the total sum rate of the algorithm is only 3% less than the full array scenario. Furthermore, the results in case of using a practical correlated channel proved the robustness of the proposed TASD algorithm, as it achieved nearly the same results compared to uncorrelated channels.

Diagnosis of subarray‐structured base station antennas in a compact setup based on solving linear equations

AbstractSince the total number of the antenna elements can be up to hundreds in a massive multiple‐input multiple‐output (MIMO) system, subarray‐structured base station (BS) array configurations are widely adopted to achieve a good cell coverage and to reduce the required number of radio frequency chains at the same time. It is crucial for any BS product to ensure that the antenna elements perform correctly as expected. Therefore, the necessity of array diagnosis is evident, especially for large BS arrays. Furthermore, it is essential that the diagnosis can be achieved in a compact and cost‐effective setup with high measurement efficiency (i.e. only a few measurement samples are required). The principle of the diagnosis method presented in this article is to obtain the S‐parameters between the subarrays and the probe via solving linear equations. In the simulation, a BS array composed of 16 subarrays with each containing 3 elements is used to validate the diagnosis method at 3.5 GHz. An array composed of 4 subarrays with each containing 3 elements was used in the measurements to verify the diagnosis method with two different phase tuning matrices at 3 GHz. Successful diagnosis results have been achieved in both the simulations and the measurements.

V-Band Geodesic Generalized Luneburg Lens Antenna With High Beam Crossover Gain

Quasi-optical beamformers provide attractive properties for antenna applications at millimetre-wave frequencies. Antennas implemented with these beamformers have demonstrated wide angle switching of directive beams, making them suitable as base station antennas for future communication networks. For these applications, it is essential to ensure a high beam crossover gain to provide a robust service to end users within the steering range. Here, we propose a geodesic generalized Luneburg lens antenna operating from 57 to 67 GHz that provides increased crossover gain compared to previously reported geodesic Luneburg lens antennas. The focal curve of the generalized Luneburg lens can be displaced from the beamformer, allowing for a higher angular resolution in the placement of the feed array along the focal curve. The lens is fed with 21 ridge waveguides with an angular separation of 5.1 degrees, thus providing beam steering in a 102-degree range. The peak realized gain varies from 19 to 21 dBi throughout the steering and frequency ranges and the beam crossover gain is roughly 3 dB below the peak gain. The simulations are experimentally validated.

A Wide-Beam Antenna Array With Spatially Variable-Orthogonal-Polarizations (SVOP) Achieved by Polarization-Mixing

This paper introduces a novel polarization-mixing strategy to significantly widen the beamwidth of dual-linear-polarized antenna arrays without changing the array topology. Instead of using the two polarizations separately as usual, it blends the orthogonal linearly-polarized radiations to achieve wider beamwidth. The proposed method is implemented on a typical ±45°-polarized base station antenna array with its thought process and working mechanism elaborated. Much wider beamwidth is achieved compared to the traditional pattern synthesis methods based on amplitude and phase weighting or sparse arrays. The beamwidth can be controlled by simply tuning one phase shifter. Importantly, this method allows all the elements to be fully excited with the same amplitude, thus avoiding using additional amplifiers or attenuators. In the meantime, the polarization-mixing method leads to spatially-variable polarizations (SVP). To obtain polarization diversity required in cellular communication systems, two SVP arrays with the same pattern shapes are designed to have their polarizations orthogonal to each other in all directions of interest. The conditions of achieving orthogonal patterns using this method are theoretically derived and thoroughly validated in theory and in simulation. It is shown that the obtained spatially-variable-orthogonal-polarization (SVOP) arrays have a much broader beam pattern and better polarization orthogonality (PO) than that of the dual-polarized antenna element.

A Wideband Dual-Polarized Base Station Antenna With Heat Dissipation Function Based on PIN Structure

A Wideband Dual-Polarized Base Station Antenna With Heat Dissipation Function Based on PIN Structure

Energy-Efficient Ground Base Station Antenna Array System for Wireless Back-Hauling and Two State Charging of Drone Base Stations

A ground base station (GBS) antenna array system (AAS) geometry and feed technique are proposed for 360 degree azimuth beam scan, elevation beam scan, azimuth beam-width control, elevation beam-width control, and in existing AASs these four capabilities do not coexist. Firstly, the results confirm that the number of active radio-frequency (RF) switches in the proposed AAS feed network is reduced by more than 99 %, and the AAS achieves a power consumption reduction of up to 50 % without fully connected (FC) architecture and 35 % with FC architecture compared to the existing AAS. Also, an independent GBS AAS azimuth and elevation plane beam-width control can be achieved from 65 to 0.01 degrees which makes it suitable for GBS-assisted wireless back-hauling plus wireless charging of unmanned aerial vehicle base stations (UAV-BSs), dynamic GBS sectorisation order control, and millimeter wave beam tracking. Also, a parallel concurrent UAV-BS battery charging configuration with power source swap capability is proposed to charge UAV-BS using the GBS back-haul and onboard power amplifier (PA) system as the power sources. The results show that the proposed charging configuration improves hovering time by 180.7 % compared to the existing onboard PA system configuration for battery charging. Furthermore, the proposed charging configuration can recharge the battery of the inactive state UAV-BS to increase the state of charge of two 16 Ampere hour batteries to 80 % in 180.13 minutes, and UAV-BS’s inactive state charging was not proposed in the existing onboard PA system configuration for UAV-BS’s battery charging.

LSTM-GRU Model-Based Channel Prediction for One-Bit Massive MIMO System

In this paper, we propose a deep learning (DL) model to estimate the channel matrix for a massive multi-input multi-output (MIMO) system with a one-bit analog-to-digital converter (ADC). In the literature, various DL methods were proposed to estimate the channel matrix; however, they were limited to a small range of noise levels. We are driven to break the data appropriately so that the estimation performance improves with the signal-to-noise ratio (SNR) because the primary premise is that the DL model should perform better in the case of higher SNR. We suggested breaking the received signal into sequences to consider the entire system as multiple sub-systems, where each antenna serves the same number of users considered as a sub-system. Consequently, the model trained the data received by each sub-system sequentially. However, the conventional back-propagation with extensive training, because of the large number of base station (BS) antennas, causes a vanishing gradient, leading to training failure. Hence, we combine long short-term memory (LSTM) with gated recurrent units (GRU) to accelerate the training speed of the network under the hypothesis that LSTM effectively processes MIMO data and overcomes the vanishing gradient problem, whereas GRU accelerates the convergence rate. The results show that our model outperforms other methods at different SNRs and various numbers of BS antennas.

A non-uniform collinear dipole array for sub-6 band

In the fifth-generation (5G) wireless communications, due to the advantages such as wide range and low cost, sub-6 frequency band is widely used. It is a challenge to develop the antennas for sub-6 base stations which are wide band, high gain and compact. In this paper, a center-fed collinear antenna array with a compact size is developed which based on the turnstile antenna. Non-uniform dipole array has been used to achieve broadband so that the disadvantages of series can be improved. The gain bandwidth has been significantly increased as compared to uniform array antennas. Wider directivity gain bandwidth of 35% is provided by the non-uniform array. The proposed array antenna can be used to omnidirectional radiation applications. The directivity gain bandwidth reached to 20.7% better than traditional series fed antennas.

Channel Training for RIS-Aided Indoor Terahertz MIMO Systems

Reconfigurable intelligent surface (RIS) is a powerful tool in assisting large-scale tera-hertz (THz) communication, especially when the line-of-sight (LoS) propagation link is blocked. However, when the base station (BS) antenna array and RIS panel are very large, the THz channel shifts from the well-known far-field model to the less-investigated near-field model. Conventional far-field based channel training mismatches with the near-field channel characteristics, which leads to significant performance loss. In this letter, we investigate the near-filed channel training for an indoor RIS aided THz communication system consisting of a THz BS, an RIS, and an user equipment (UE) that are geographically close to each other. A BS channel training method is designed based on particle swarm optimization (PSO), where the unified near-field channel model is accommodated. Simulation results verify the effectiveness of the proposed channel training. Moreover, with RIS illumination, the UE received signal-to-noise ratio (SNR) can be further improved under imperfect channel and localization.

Base Station Antenna Uptilt Optimization for Cellular-Connected Drone Corridors

Reliable wireless coverage in drone corridors is critical to enable a connected, safe, and secure airspace. To support beyond visual line of sight (BVLOS) operations of aerial vehicles in a drone corridor, cellular base stations (BSs) can serve as a convenient infrastructure as they are widely deployed to provide seamless wireless coverage. However, antennas in the existing cellular networks are down-tilted to optimally serve their ground users, which results in coverage holes at higher altitudes when they are used to serve drones. In this paper, we consider the use of additional uptilted antennas at each cellular BS and optimize the uptilt angle to maximize the wireless coverage probability across a given drone corridor. Through numerical results, we characterize the optimal value of the antenna uptilt angle for a given antenna pattern as well as the minimum/maximum altitudes of the drone corridor.

Machine-Learning-Assisted Antenna Optimization With Data Augmentation

In this letter, a machine learning assisted antenna optimization method is proposed based on the random forest (RF) algorithm with data augmentation (DA). Using only a small number of samples, the prediction and optimization accuracy of the RF algorithm is ensured with repeated data augmentation, which balances different types of samples during the training. With the proposed DA-RF method, the AR bandwidth of a circularly polarized omnidirectional base station antenna is optimized. By learning the relationship between the loop orientations and the AR bandwidth efficiently, the AR bandwidth is improved by 41% compared with the best one in the samples. The estimation accuracy of the proposed method outperforms other similar methods, with fewer iterations as well. The method is also successfully applied to multi-objective optimizations.

Design and Optimization of Hardware Impaired Multi-Cell Rician-Faded mMIMO Systems With Pilot Decontamination Precoding

We consider a hardware-impaired multi-cell Rician-faded massive multi-input multi-output (mMIMO) system with two-layer pilot decontamination precoding, also known as large-scale fading precoding (LSFP). We derive a closed-form spectral efficiency (SE) expression by assuming a flexible dynamic analog-to-digital converter (ADC)/digital-to-analog converter (DAC) architecture, and hardware-impaired radio frequency chains at the base stations (BSs) and user equipments. The dynamic ADC/DAC architecture enables us to vary the resolution of ADC/DAC connected to each BS antenna, and suitably choose them to maximize SE. We design a distortion-aware minimum mean squared error (DA-MMSE) precoder, and investigate its usage by combining it with the two-layer LSFP, and conventional single-layer precoding (SLP). We show that the DA-MMSE precoder with SLP outperforms its distortion-unaware counterpart, which is used with LSFP. We analytically show that for pure LoS channels, the LSFP reduces to SLP, and its implementation can thus be avoided. It is shown that the LSFP can tolerate high hardware impairments at the BS, but is extremely sensitive to the ADC resolution of the user. We also optimize the global energy efficiency by using a minorization-maximization based algorithm, and show its improved performance over the conventional SE optimization techniques.

Uplink Assisted MIMO Channel Feedback Method Based on Deep Learning.

In order to solve the problem wherein too many base station antennas are deployed in a massive multiple-input-multiple-output system, resulting in high overhead for downlink channel state information feedback, this paper proposes an uplink-assisted channel feedback method based on deep learning. The method applies the reciprocity of the uplink and downlink, uses uplink channel state information in the base station to help users give feedback on unknown downlink information, and compresses and restores the channel state information. First, an encoder-decoder structure is established. The encoder reduces the network depth and uses multi-resolution convolution to increase the accuracy of channel state information extraction while reducing the number of computations relating to user equipment. Afterward, the channel state information is compressed to reduce feedback overhead in the channel. At the decoder, with the help of the reciprocity of the uplink and downlink, the feature extraction of the uplink's magnitudes is carried out, and the downlink channel state information is integrated into a channel state information feature matrix, which is restored to its original size. The simulation results show that compared with CSINet, CRNet, CLNet, and DCRNet, indoor reconstruction precision was improved by an average of 16.4%, and outside reconstruction accuracy was improved by an average of 21.2% under all compressions.

Optimized transmit antenna selection and self-attention based convolutional resource allocation model for massive MIMO technology

Massive multiple input and multiple output (MIMO) plays an important role in enhancing the transmission reliability and capacity of the transmitting channel. However, the resource allocation (RA) and transmit antenna selection (TAS) scheme are essential for massive MIMO systems to reduce implementation costs and complex operations. Because the presence of a large antenna consumes enormous resources that must be reduced in order to develop a user-friendly model. Hence, this article introduces a novel TAS and RA scheme in a massive MIMO system that can enhance communication performance efficiently. In this study, an improved sheep flock optimization algorithm (ISFOA) is first emphasized to select the efficient antenna, thus effectively minimizing the cost and the design complexity. Then, a novel deep learning (DL) based self-attention aided deep convolutional neural network (SA-DCNN) model for the stable allocation of resources to all available users is proposed. Thus, the user equipment (UE) can develop a better quality path and reduce the power consumption of the entire system. In the experimental scenario, the performance of the proposed model is compared with an existing technique based on sum rate, spectral efficiency (SE) and energy efficiency (EE) by varying base station (BS) antennas, varying user, signal to noise ratio (SNR), and sub-carriers along with the computational performance. Particularly, when the SNR=10 dB, the proposed model obtains the SE of about 50 bits/s Hz compared to the existing techniques.

A Low-Profile High Isolation Wideband Dual-Polarized Antenna for Sub-1 GHz Base Stations

In this paper, we present a low profile, wideband dual-polarized suspended patch antenna for LTE700/LTE850/CDMA850/GSM900 applications with high isolation. The proposed antenna element consists of two suspended patches excited orthogonally through modified cross-slot. It is theoretically and experimentally verified that this modification significantly widens −15 dB impedance bandwitdh and improves port isolation between polarizations. Experiments reveal in parallel with conducted simulations that the antenna is capable of exhibiting 37% −15 dB impedance bandwidth (688-1000 MHz) and more than 35 dB port isolation over operating frequency band. In principle planes, the antenna provides symmetric broadside radiation patterns at boresight with half-power beamwidths of 55.32º-65.61º and 61.79º-66.84º. Gain of the antenna varies within 7.3-8.4 dBi range at measured frequencies. The size of the antenna is 380 × 380 × 43.6 mm3 which makes it very suitable to integrate into commercially deployed base stations. Geometry, feeding mechanism, parametric studies and experimental results of the proposed antenna are presented throughout the paper.

Dual‐band coaperture 4G/5G base station antenna array with low scattering elements

AbstractA kite‐shaped low‐band (LB) element loaded with mushroom‐shaped structure is proposed. The element has a bandpass characteristic for the electromagnetic wave within the frequency range of 3.4–3.8 GHz and can reduce the influence on the high‐band (HB) antenna in the dual‐band coaperture antenna. The LB elements operating in the 0.698–0.96 GHz frequency band are embedded in the massive multiple input, multiple output (MIMO) antenna. One prototype of the coaperture antenna is fabricated for demonstration. The measured results show that the pattern distortion of most of the antenna ports of the HB antenna is very small after adding the LB antenna. The maximum gain deviation of the traffic beam scanning within the range of ±60° is less than 0.57 dB, indicating that the LB element can effectively suppress scattering and is suitable for multiband coaperture base‐station antenna.

A Survey of Five Generations of MIMO Multiband Base Station Antennas

AbstractAs telecommunication systems evolve, there is an increasing demand for multi‐band MIMO base‐station antennas. Antenna arrays are generally used to support multiple frequencies with multiple ports in a compact size. However, this can result in significant degradation in scattering patterns and return loss/port isolation due to the close proximity of the radiation from the array. Various techniques have been proposed in the literature to address these issues. This paper presents an overview of the challenges faced by base station antennas (BSAs) over time and their general solutions. With the technological advancements of communication systems, accompanied by high data rates, high‐frequency antenna designs and MIMO operations have been introduced at the base station level. Base station antennas encountered design challenges, including broad bandwidth, high isolation levels, stable half‐power beam width, beam squint reduction, high cross polarization discrimination (XPD), gain improvement, beam steering, compact size, and cost reduction. This survey provides information on the solutions adopted by researchers over time for each design challenge, with citations. Additionally, the authors discuss techniques developed to overcome these challenges, demonstrating the evolution of BSAs from the first to the fifth generation of telecommunication systems.

A Miniaturized Dual-Band Dual-Polarized Base Station Antenna Loaded With Duplex Baluns

A novel miniaturized dual-band and dual-polarized base station antenna loaded with duplex baluns is proposed in this letter. The duplex balun consists of a lower-band filter structure (LFS) and an upper-band filter structure (UFS). The LFS contains a stepped-impedance filter, an open-circuited stub, and an impedance matching section. The UFS contains a highpass filter and an impedance matching section. The proposed antenna has two independent current paths, which enables it to operate in dual bands, and the two paths are bent to achieve its small overall size. By miniaturizing the antenna and loading LFS&UFS, the operating bands of 1.71-2.17 GHz and 3.22-3.7 GHz, the compact size of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0.24 \times 0.24 \times 0.18\lambda _{LL}^3$</tex-math></inline-formula> (λ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">_LL</i> is the wavelength at the lowest frequency of the lower band), the lower coupling coefficients(<-20dB), the normal in-band radiation characteristics, and good out-of-band suppression characteristics(>20dB at the lower-band ports as well as >23dB at the upper-band ports) are obtained. It represents an excellent scheme for 5G applications.