كرار شاكر مطير النعماني

The demand for the array antenna that consists of multiple ports has increased in recent years, because of its main importance in reducing noise and interference between users. In this paper, we propose a new method for designing an 8× 8 (16-ports) multi-input and multi-output (MIMO) antenna. This method relied on the Micro Strip mechanism so that we presented a small antenna that operates at wide and multi-bands of millimeter waves (Mm-Waves). According to the information curves generated by the CST experimental software, it was observed that the proposed antenna operates well from 36 to 60 GHz. Therefore, the antenna achieved the best results in terms of many most important parameters, the reflection coefficient is<-10 dB, return loss is<-25 dB, and voltage standing wave ratio (VSWR) is< 2. In addition, the efficiency of the antenna for all frequencies from 70% to 97%, the envelope correlation coefficient (ECC) is< 0.001, and the diversity gain (DG) is 10 dB for all frequencies, while the maximum gain achieved by the antenna is 9 dB at 46 GHz. All these good results achieved by the antenna make it the prominent and potential element in most of the future 6G wireless communication systems.

A good antenna design has played an essential role in the design of wireless communication systems, international companies are looking for the best design that suits their products in terms of size, bandwidth, gain, cost, and performance. In this paper, three antenna models are designed for fifthgeneration (5G) communications, the first model is a single antenna, the second model is a two-ports multi-input multi-output (MIMO) antenna, and the third model is a four-ports MIMO antenna. The geometric dimensions of a single antenna are 20× 37× 1.6 mm3, the two-ports antenna dimensions are 44× 37× 1.6 mm3, while the four-ports antenna dimensions are 74× 44× 1.6 mm3. The design of these antennas was based on the latest strategies in terms of their small sizes and operating from 13.5 to 20 GHz in wide and multiple bands to be compatible with all advanced communication devices. Based on the results that emerged, it was noted that the reflection coefficient (S11)<-10 dB and has better isolation between the ports is<-26 dB. While the envelope correlation coefficient (ECC) value is< 1.036× 10-9, and the diversity gain (DG) value is 10 dB. All antennas proposed operate in ultrawideband (UWB) which is very necessary for 5G communications devices.

The increasing need for high-speed telecommunications has motivated extensive research on fifth-generation (5G) technology. Many industries require quick data throughput, low latency, and consistent radiation patterns utilizing 5G techniques. This article proposes an antenna with two simulations and measurements for different 5G applications. It is tiny and operates at a wide frequency range of 1 to 9 GHz, making it suitable for various modern systems. The results obtained from this antenna were superior to those proposed in previous articles. It is observed that the reflection coefficient ratio is much less than-10 dB for both the simulation and measurement sides at all frequencies. In addition, the highest value of the reflection coefficient is-82 dB at 8 GHz. The antenna's maximum gain and efficiency are 9.8 dB and 98%, respectively. Thus, the best antenna performance when measuring the voltage standing wave ratio (VSWR) parameter at 8 GHz is up to 0.3. It has been discovered that the measured radiation pattern is nearly omnidirectional and matches the simulated radiation pattern exactly. The discrepancies between the design proposed and the authors' designs in other recent works are that it is based on a spatial and aperture technique for both the patch and ground layers. Therefore, the antenna achieved good results of wide bandwidth, high efficiency, gain, etc. Furthermore, it has been concluded that the antenna is well-suited for many applications within the 5G Internet of Things (IoT) ecosystem.

Artificial intelligence (AI)-aided communications have gained significant traction in recent years due to the widespread application of machine learning (ML) and deep learning (DL) machines with algorithms to solve math problems in wireless communications. This study offers an overview of the use of ML models in antenna design and optimization. This incorporates DL on ML frameworks, categories, and structure to get practical and broad insights using ML techniques for high throughput, quick data analysis, and prediction. This article also comprehensively reviews recent research papers on antenna design via ML. This includes an analysis of several ML algorithms that have been applied to produce antenna parameters such as the reflection coefficient (S-parameters), efficiency and gain values, and radiation patterns of the antennas. However, the current antenna design's structure, variables, and external factors remain complex. In addition, the expense of time and processing resources is inescapable and unacceptable to most designers. ML-based antennas have been created to increase antenna modeling efficiency and accuracy to solve these challenges. Techniques for modeling data may be used to predict the performance of an antenna for a certain set of antenna factors of design. As a result, this study highlights the most sophisticated applied ML techniques that have been presented to increase antenna modeling efficiency and accuracy. The results demonstrate that AI, ML, and DL may minimize simulation needs, predict antenna behavior, and reduce time with high accuracy.

A significant number of causes for people killed by automobile accidents on both public and interior roads are the driver's failure to pay attention to the roadway, as well as other factors such as tiredness, sleep during driving, using the phone, or even the driver being intoxicated, etc. This work aims to identify sleepy status, pinpoint the onset of sleep, and alert the driver when their level of alertness drops below a level that does not improve productivity or the vehicle's overall safety protocols to prevent drowsiness-related crashes. In this article, we present a new method for implementing drowsiness detection inexpensively, using a Raspberry Pi as a central processor board that connects all practical measurement components. In addition, the proposed method includes the following activities: tracking the driver's head vibration while driving using tilt sensors. Moreover, a camera was mounted in front of the driver to take photos and record videos of the driver's health. As a result, the results were entirely satisfactory, and if the driver was operating the vehicle while fatigued, the response was 95% reliable and fast.

In recent years, deep distributed neural networks (DDNNs) and neural networks (NN) have excelled in an extensive list of applications. For example, deep convolutional neural networks (DCNNs) are constantly gaining new features in various tasks in computer vision. At the same time, the number of end devices, including Internet of Things (IoT) devices has increased prominently. These devices are attractive targets for machine learning applications because they are often directly connected to sensors. For example (cameras, microphones, and gyroscopes) that record large amounts of input data in a stream mode. This study presents the design of a DDNN with end devices, edges, and clouds that spans computer hierarchies. The idea presented is considered one of the new ideas because it depends on two layers to distinguish, namely the convolutional layer and the pooling layer. The main objective behind using these two layers in one proposal is to provide and obtain the best results. Finally, we discovered that the proposed technique produced the best results in terms of accuracy and cost, with the precision of the definition reaching 99 % and the cost being quite affordable at 25. As a result, we conclude that these results are far superior to those achieved by the researchers in their ideas provided in previous recent literature.

The rapid expansion of digital media platforms and their growing user base in the wireless industry necessitate communication systems to provide information at high speeds with reliable connections. Therefore, wireless communication systems with a single antenna cannot accomplish these requirements. Consequently, the access and utilization of multi-input multi-output (MIMO) antennas are becoming more common in contemporary high-speed transmission systems. This article covers the fundamentals of MIMO antenna operation, the metrics for MIMO antenna performance parameters, and the design methodologies for specifying the three most commonly used antennas (two-port, quad-port, and eight-port). Additionally, it discusses their ability to improve channel capacity significantly. It focuses on designing MIMO antennas with ultra-wideband (UWB) for 5G systems operating between 1 and 27 GHz and millimeter-wave (mmWave) bands from 30 to 100 GHz. This article is valuable for researchers interested in developing MIMO antennas for diverse applications. It compiles advanced methods related to materials, advancements, challenges, and state-of-the-art technologies used in the design of high-performance MIMO antennas. We concluded that antennas that operate at mmWave frequencies have small dimensions and suffer from isolation problems in the MIMO formation. In contrast, antennas operating below 6 GHz are large and do not suffer from isolation problems.

The demand for high-efficiency, small-sized ultrawide-band (UWB) antennas has risen due to the need for fast wireless communication systems. In this manuscript, we propose a novel geometry for a 4-port multi-in multi-out (MIMO) antenna with an L-shaped structure that solves the limitations of current designs in UWB applications. The dimensions and thickness of an antenna design are 18×18×1.5875 mm. It operates in multiple bands, such as Ku, K, and millimeter wave (mmWave) spectrum ranging from 17 to 100 GHz. This feature is ideal for small fifth-generation (5G) and sixth-generation (6G) wireless communication devices. We used two separate ground strips at the bottom to minimize interference in the MIMO element design and enhance antenna performance. The antenna performed exceptionally well in all measured parameters. The reflection coefficient (RC) was less than -10 dB, the mutual coupling (MC) coefficient reached -62.4 dB, and the radiation antenna efficiency ranged between 82% and 94%. It achieved the highest gain of 13 dBi at 87 GHz. In addition, the envelope correlation coefficient (ECC) is < 0.02, and the diversity gain (DG) ranges between 9.9 and 10 dB. This antenna surpasses all others in every parameter when compared to proposals by different researchers. This makes it a superior choice for modern wireless devices catering to 5G and 6G networks.

The design of a millimeter wave (mmW) antenna for the 5G mobile applications is presented in this paper. The designed antenna has dimensions of 10× 10× 0.245 mm3. This includes the copper ground plane. The resonance of the proposed mmW antenna lies within the range of 33 GHz and 43 GHz. These frequency bands are covering the 5G proposed band in terms of the signal speed, data transmission, and high spectral efficiencies. Computer simulation technology (CST) software is used to simulate the proposed 5G antenna including the characteristics of S-parameters, gain, and radiation pattern. Simulation results show that the return loss at resonant frequencies goes-22 dB, which satisfies the requirements of 5G mobile technology.