The intensive research in the fifth generation (5G) technology is a clear indication of technological revolution to meet the ever-increasing demand and needs for high speed communication as well as Internet of Thing (IoT) based applications. The timely upgradation in 5G technology standards is released by third generation partnership project (3GPP) which enables the researchers to refine the research objectives and contribute towards the development. The 5G technology will be supported by not only smartphones but also different IoT devices to provide different services like smart building, smart city, and many more which will require a 5G antenna with low latency, low path loss, and stable radiation pattern. This paper provides a comprehensive study of different antenna designs considering various 5G antenna design aspects like compactness, efficiency, isolation, etc. This review paper elaborates the state-of-the-art research on the different types of antennas with their performance enhancement techniques for 5G technology in recent years. Also, this paper precisely covers 5G specifications and categorization of antennas followed by a comparative analysis of different antenna designs. Till now, many 5G antenna designs have been proposed by the different researchers, but an exhaustive review of different types of 5G antenna with their performance enhancement method is not yet done. So, in this paper, we have attempted to explore the different types of 5G antenna designs, their performance enhancement techniques, comparison, and future breakthroughs in a holistic way.
The increasing proliferation of advanced devices for UWB, 5G communication, micrometerwave, and millimeter-wave communication demands an antenna which can handle huge data rates, provides high gain and stable radiation pattern as a panacea of most of the current wireless communication problems. Many different antenna designs have been proposed by the researchers but, Antipodal Vivaldi Antenna (AVA) has drawn the attention of most of the researchers because of its high gain, wide bandwidth, less radiation loss, and stable radiation pattern. Different methods are presented to make AVA more compact while maintaining the performance of an antenna to an acceptable level. These different methods are substrate choice, flare shape, slots, and feeding connectors. Also, AVA performance can be enhanced by incorporating corrugation, dielectric lens, patch in between two flares of AVA, balanced AVA (BAVA), metamaterial, computational intelligence (CI), and AVA array. The AVA performance enhancement techniques modify the electrical and physical properties of an antenna which in turn improves its performance. A large number of performance enhancement methods of AVA design have been proposed, however, no comprehensive study exists to categorize these performance enhancement techniques and outline their concepts, advantages, disadvantages, and applications. So, in this paper, we have attempted to outline all methods available for enhancing and optimizing the parameters of AVA. Additionally, to validate some of the important performance enhancement methods, they are incorporated in the basic conventional AVA design and further simulation results are obtained for the same which are in line with the surveyed literature. Each method is explained in detail by incorporating its key points, merits, and demerits. Moreover, illustrations from the literature are given to demonstrate improvement in the parameters as a result of applying a particular performance enhancement technique. INDEX TERMS Antipodal Vivaldi antenna (AVA), AVA array, balanced antipodal Vivaldi antenna (BAVA), corrugations, dielectric lens, metamaterial, parasitic patch, slots.
The gain enhanced antipodal Vivaldi antenna (AVA) is implemented in this study for fifth-generation (5G)communication applications. At first, the conventional AVA is designed and then metamaterial and rectangular corrugations are added to AVA to enhance its gain from 7.75 to 9.53 dB. The designed antenna size is 40 mm × 24 mm × 1.6 mm and it is fabricated on the FR4 substrate. Further, modified AVA with metamaterial and corrugation increases higher cut-off frequency from 30.62 to 34.52 GHz. The gain of the designed antenna varies between 5 and 9.53 dB over 24.77 to 34.52 GHz and its bandwidth is 9.75 GHz. The designed antenna covers 25 to 29.5 GHz and 31.8 to 33.4 GHz 5G frequency bands. Experimental results and the simulated results are nearly same, which demonstrate that the designed antenna can be used for 5G communication applications. K E Y W O R D S 5G communication, antipodal Vivaldi antenna (AVA), corrugations, enhanced gain, metamaterial Amruta S. Dixit and Sumit Kumar contributed equally to this work.
The concept of Internet of Things (IoT) was proposed by Professor Kevin Ashton of the Massachusetts Institute of Technology (MIT) in 1999. IoT is an environment that people understand in many different ways depending on their requirement, point of view and purpose. When transmitting data in IoT environment, distribution of network traffic fluctuates frequently. If links of the network or nodes fail randomly, then automatically new nodes get added frequently. Heavy network traffic affects the response time of all system and it consumes more energy continuously. Minimization the network traffic/ by finding the shortest path from source to destination minimizes the response time of all system and also reduces the energy consumption cost. The ant colony optimization (ACO) and K-Means clustering algorithms characteristics conform to the auto-activator and optimistic response mechanism of the shortest route searching from source to destination. In this article, ACO and K-Means clustering algorithms are studied to search the shortest route path from source to destination by optimizing the Quality of Service (QoS) constraints. Resources are assumed in the active and varied IoT network atmosphere for these two algorithms. This work includes the study and comparison between ant colony optimization (ACO) and K-Means algorithms to plan a response time aware scheduling model for IoT. It is proposed to divide the IoT environment into various areas and a various number of clusters depending on the types of networks. It is noticed that this model is more efficient for the suggested routing algorithm in terms of response time, point-to-point delay, throughput and overhead of control bits.
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