Base Station Antennas Research Articles

Review on 5G Small Cell Base Station Antennas: Design Challenges and Technologies

Review on 5G Small Cell Base Station Antennas: Design Challenges and Technologies

Broadband Suppression of Common-Mode Current Interference in Dual-Band Base Station Antenna Arrays by Loading an LC Circuit

Broadband Suppression of Common-Mode Current Interference in Dual-Band Base Station Antenna Arrays by Loading an LC Circuit

Wideband Channel Estimation with Imperfect Hardware for Reconfigurable Intelligent Surfaces

Channel estimation is central part of reconfigurable intelligent surface (RIS) aided communication for active and passive beamforming. The primary challenge behind channel estimate is large dimensionality stemming from not only massive number of RIS elements but also many antennas in base station. Large dimensionality leads to excessive usage of pilot tones for OFDM signals implying high overhead and decreased throughput. To alleviate the high usage of pilots for channel estimation, in this study we propose to have a low complexity transmitter at the RIS simplifed with an aggressive clipping policy and a robust channel estimator against clipping. For robust channel estimation, a generative machine learning model is adapted to exploit prior information to compensate the information loss due to clipping. The simulation results clearly indicate that the proposed estimator has quite a large resiliency for clipped transmitted signals as compared to linear channel estimators.

Polarization adaptation to improve cell border area bitrates and system capacity in small cells

The target of this paper is to show the impact of polarization adaptation on the received signal quality in an outdoor small-cell deployment scenario. The signal-to-interference ratio (SIR) is the key factor in defining the achievable data rate, and the capacity of the cell. At the cell border area, the SIR value is typically low and causes a significant decrease in the system capacity and achievable data rates. These bad SIR areas at the cell border can be improved by using orthogonal polarizations in the neighboring cells rather than using all polarizations over the whole cell coverage area. For the research work of this paper, a series of measurements were carried out to measure a received signal power and a cross polarization discrimination (XPD) ratio while the signal is transmitted and received with a different set of polarizations. In the measurements, we have considered horizontal, vertical, +/− 45° slanted polarizations, and two different environments, urban street and open space, and three frequency bands, 970−1030 MHz, 2000−2030 MHz and 3364−3400 MHz. The measurement results revealed that at a different distance from the transmitter, for horizontal/vertical polarization, the average XPD is around 24.7 dB and 31.7 dB in the urban street and open area environments, respectively. For +/− 45° slanted polarization, the average XPD is around 11.5 dB and 12.1 dB in the urban street and open area environments, respectively. This paper goes on to propose polarization adaptation in each cell, where the primary polarization is valid for the whole service area of the cell, and secondary polarization is only used in close proximity of the base station antenna. Considering the results, it is emphasized that system capacity can be significantly improved by having only one channel with good SIR values compared to multiple channels with bad SIR values. However, MIMO channels with orthogonal polarizations or with spatial multiplexing can be utilized in the closed vicinity of the base station, i.e., in an area with good SIR values. It is shown that the overall cell capacity can be increased by almost 35% by utilizing polarization adaptation compared to MIMO 2 × 2.

Design of tri-band dual-polarized base station antenna for 2G/3G/4G/5G

Design of tri-band dual-polarized base station antenna for 2G/3G/4G/5G

Enhancing 6G network security: GANs for pilot contamination attack detection in massive MIMO systems

Accurate channel estimation at the base station (BS) is crucial for reliable, high-throughput communication in massive multiple input multiple output (MIMO) systems. However, the process of estimating channel matrices in massive MIMO systems is prone to pilot contamination attacks. These attacks not only degrade the efficiency of channel estimation but also increase the probability of information leakage. In this paper, a method is being proposed for detecting pilot contamination signals using a deep learning model, namely a generative adversarial network (GAN), at the BS of a massive MIMO system. GAN comprises two interconnected models, the first model is the generator, which creates synthetic samples, and the second model is the discriminator, which distinguishes between real and generator-generated samples. Residual block-based convolutional neural networks (CNNs) like ResNet and reverse ResNet are implemented as discriminator model of GAN for improving the detection of pilot-contaminated signals. The generator, structured as an autoencoder, generates artificial data resembling real data from noisy signals. The simulation results demonstrate that the proposed model effectively identifies the pilot-contaminated attacks; this effectiveness is evaluated through performance matrices called accuracy and loss function. Furthermore, the performance of the model is observed by varying the number of BS antennas and pilot lengths.

Evaluation of the Influence of the Spatial Smoothing Procedure of the Correlation Matrix on the Efficiency of the 2D-MUSIC Method

The occurrence of correlations between radio signals arriving at the antenna array of the base station reduces the effectiveness of subspace methods for positioning user devices in wireless communication networks. For the purpose of radiation sources decorrelation, a spatial smoothing algorithm can be applied dividing the base station antenna array into sub-arrays and then averaging the signal correlation matrices obtained for these sub-arrays. In this paper, we investigate the impact of various ways of dividing a flat equidistant antenna array into subarrays on the performance of the 2D-MUSIC multiple signal classification method in the scenario of ultra-dense networks operating in the FR2 band.

A Neural Network-Based Random Access Protocol for Crowded Massive MIMO Systems.

Fifth-generation (5G) and beyond networks are expected to serve large numbers of user equipments (UEs). Grant-based random access (RA) protocols are efficient when serving human users, typically with large data volumes to transmit. The strongest user collision resolution (SUCRe) is the first protocol that effectively uses the many antennas at the 5G base station (BS) to improve connectivity performance. In this paper, our proposal involves substituting the retransmission rule of the SUCRe protocol with a neural network (NN) to enhance the identification of the strongest user and resolve collisions in a decentralized manner on the UEs' side. The proposed NN-based procedure is trained offline, admitting different congestion levels of the system, aiming to obtain a single setup able to operate with different numbers of UEs. The numerical results indicate that our method attains substantial connectivity performance improvements compared to other protocols without requiring additional complexity or overhead. In addition, the proposed approach is robust regarding variations in the number of BS antennas and transmission power while improving energy efficiency by requiring fewer attempts on the RA stage.

An Efficient Ray-Tracing Approach for the Isolation Assessment of Co-Located Base Station Antennas at mmWave

An Efficient Ray-Tracing Approach for the Isolation Assessment of Co-Located Base Station Antennas at mmWave

Electromagnetic–Thermal Co-Design of Base Station Antennas With All-Metal EBG Structures

Electromagnetic–Thermal Co-Design of Base Station Antennas With All-Metal EBG Structures

Millimeter Wave Wireless Channel Knowledge Map Construction Based on Path Matching and Environment Partitioning

Key technologies in 5G and future 6G, such as millimeter wave massive multiple-input multiple-output (MIMO), relies accurate channel state information (CSI). However, when the number of base station (BS) antenna increases or the number of users is large, it is rather resource-consuming to obtain the CSI. Channel knowledge map (CKM) is an emerging environment-aware wireless communication technology, which stores the physical coordinates of BS and reference locations together with the corresponding channel path information. This makes it possible to obtain CSI with light or even without pilots, which can significantly reduce the overhead of channel estimation and improve system performance, especially suitable for quasi-static wireless environments with relatively stable channels and communication systems using millimeter waves, terahertz waves, visible light, and so on. The main challenge for CKM is how to construct an accurate CKM based on finite measurement data points at limited reference locations. In this work, we proposed a novel CKM construction method based on path matching and environmental partitioning (PMEP-CC) to address the above issues. Specifically, we first sort the propagation paths between reference locations, map them to a high-dimensional space to establish the path correlation coefficient between two reference locations. Then, the communication region are divided into different subregions based on its spatial correlation. Finally, the path information at locations where no measurements are available are estimated based on the known path information within the subregion to construct CKM. Numerical results are provided to show the performance of the proposed method over related studies.

Joint Direct and Indirect Channel Estimation for RIS-Assisted Millimeter-Wave Systems Based on Array Signal Processing

Reconfigurable intelligent surface (RIS)-assisted millimeter wave (mmWave) communication is a promising technology for enlarging the coverage area of millimeter wave systems. Unfortunately, realizing the full potential of these systems requires addressing numerous challenges in channel estimation. In this paper, channel estimation for RIS-assisted mmWave communications is considered. Under the assumption that the array manifolds of the base station antennas and the RIS reflecting elements are given by uniform arrays, an efficient two-stage channel estimation method based on array signal processing techniques is proposed. In the proposed algorithm, the direct and indirect channels are jointly estimated by space-time processing that exploits the sparsity in RIS-assisted mmWave channels and the features associated with uniform arrays. Then, several practical issues, including detection of the number of channel paths, imperfect RIS hardware, and complexity, are addressed. Extensions to the cases of uniform planar array-based RIS, wideband communication, and multiple users are also discussed. Numerical results validate the effectiveness of the proposed method.

Beamforming Array Antenna Technique Based on Partial Update Adaptive Algorithms

The most important issues for improving the performance of modern wireless communication systems are interference cancellation, efficient use of energy, improved spectral efficiency and increased system security. Beamforming Array Antenna (BAA) is one of the efficient methods used for this purpose. Full band BAA, on the other hand, will suffer from a large number of controllable elements, a long convergence time and the complexity of the beamforming network. Since no attempt had previously been made to use Partial Update (PU) for BAA, the main novelty and contribution of this paper was to use PU instead of full band adaptive algorithms. PU algorithms will connect to a subset of the array elements rather than all of them. As a result, a common number of working antennas for the system's entire cells can be reduced to achieve overall energy efficiency and high cost-effectiveness. In this paper, we propose a new architectural model that employs PU adaptive algorithms to control and minimize the number of phase shifters, thereby reducing the number of base station antennas. We will concentrate on PU LMS (Least Mean Square) algorithms such as sequential-LMS, M-max LMS, periodic-LMS, and stochastic-LMS. According to simulation results using a Uniform Linear Array (ULA) and three communications channels, the M-max-LMS, periodic LMS, and stochastic LMS algorithms perform similarly to the full band LMS algorithm in terms of square error, tracking weight coefficients, and estimation input signal, with a quick convergence time, low level of error signal at steady state and keeping null steering's interference-suppression capability intact.

Multiband antenna design with enhanced radiations using characteristic mode analysis

In this research, a multiband patch antenna design based on the superposition of multiple modes at the same resonant frequency is presented. The concept of the contribution of lower order modes (LOMs) with the higher order dominant modes (HODMs) is investigated using characteristic mode analysis (CMA). In order to provide similar broadside radiations in three bands, the radiating capability of the LOMs is enhanced in the resonant frequencies of HODMs. These HODMs when excited alone provide null in the broadside radiations of the antenna. Using a single feed, enhancement in the broadside radiations of the antenna is achieved with the superposition of multiple modes at the same resonant frequency. Based on the proposed concept two antennas have been designed and fabricated. The antenna provides stable and enhanced radiations. simulated and measured results are in good agreement. These antennas find application in many applications including communication systems, base station antennas, 5G communications, satellite communication etc.

Miniaturized broadband upward-folded dipole for base station applications

This paper proposes a compact and broadband dual-polarized base station antenna based on current path extension for 2G/3G/LTE base stations. The antenna is miniaturized by bending the end of the radiator to extend the current path. By bending the antenna, a new resonant mode is introduced to increase the bandwidth. In addition, a parasitic patch is placed above the antenna to improve impedance matching, and the introduction of artificial magnetic conductors reduces the profile of the antenna and significantly improves the radiation characteristics. The antenna radiator has a compact size of only 0.207×0.207λL2 at the lowest frequency with a total profile of 0.136 λL. The measured results indicate that the proposed antenna has a substantial bandwidth of 45.5% and more than 34 dB of high port isolation with a gain of 7.9 ± 0.5 dBi.

A power method based user selection for reconfigurable intelligent surface (RIS) assisted multiuser massive MISO systems

Reconfigurable intelligent surfaces (RISs) have recently emerged as effective solutions for providing massive connectivity. However, an effective RIS user selection method that can support massive devices remains undeveloped. In this study, a power method (PM)-based RIS user selection algorithm for RIS-assisted massive multiple-input multiple-output systems is proposed. It is proven that minimizing the condition number (CN) of the aggregated downlink channel matrix results in the same performance as maximizing the sum rate. Based on this analysis, a PM-based RIS user selection algorithm that utilizes a CN as an RIS user selection metric is proposed here. Although the complexity of conventional user selection algorithm is proportional to the square of the number of base station (BS) antennas ( M 2 ), that of the proposed algorithm is linearly proportional to the number of BS antennas ( M ). Simulation results demonstrate that, under a practical RIS control link, the proposed PMUS algorithm achieves better performance and lower complexity than the conventional user selection algorithm.

Performance analysis of multiuser massive MIMO with multi-antenna users: Asymptotic data rate and its application

Massive multiple-input multiple-output (MIMO) systems have become an essential feature in the fifth-generation (5G) wireless communication networks. Due to the commercialization of 5G mobile communication devices, the number of multi-antenna devices have increased over the years. Therefore, a precoding scheme based on multi-antenna users is an inevitable feature that needs to be analyzed in massive MIMO systems. The asymptotic ergodic sum-rate analysis of block diagonalization (BD) based multiuser MIMO systems is performed in this study. Based on the random matrix theory results derived from the massive base station antenna characteristics, the singular values of the block diagonalized channel are obtained asymptotically, and thereby, the closed-form expression for the BD based sum-rate is derived. The asymptotic sum-rate performance gap between the BD and the zero-forcing beamforming is then analyzed. Moreover, from the derived asymptotic sum-rate, the optimal number of users needed to maximize the ergodic sum-rate is proposed. Simulation results show that the derived asymptotic analyses are closely aligned to the sum-rate obtained by Monte Carlo simulations. In addition, the simulation results prove that the maximum ergodic sum-rate can be achieved by using the proposed number of users.

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.