Wireless Metropolitan Area Networks Research Articles

A low-profile antenna with parasitic elements and a DGS-based partial ground plane for 5G/WMAN applications

A low-profile antenna with three parasitic elements is designed and presented for fifth-generation (5G) and wireless metropolitan area network (WMAN) applications. This prototype covers the frequency range of 2.75–4.94 GHz, which is applicable for the lower 5G (3.33–4.2 GHz), WWAN n48 CBRS (US) (3.55–3.7 GHz), WiMAX rel 2 (3.4–3.6 GHz), n77 (3.3–4.2 GHz, most European and Asian countries), n78 (3.3–3.8 GHz, USA), and n79 (4.4–5.0 GHz, China, Hong Kong, Japan, and Russia) bands. The prototype is made of a low-loss, commercially available substrate material known as Rogers RT 5880 (εr = 2.2, tanδ = 0.0009) with a thickness of 0.79 mm. The optimized dimension of the proposed antenna is 35 × 25 × 0.79 mm3 (i.e., 691.25 mm3). The proposed 10-element array antenna is fed by a 50 Ω feeder. The maximum gain and directivity of the prototype antenna are 4.3 dB and 4.75 dBi, respectively. The radiation efficiency of the prototype varies from 86.79 to 92.14% (simulated) and from 86.23 to 91.48% (measured), and it is 89.48% (simulated) and 90.59% (measured) at 3.225 GHz. The impedance profile of the proposed 10-element array is (49.80-j1.72) Ω, which ensures good impedance matching. The VSWR and surface current of the low-profile antenna are 1.036 and 107.931 A/m at the center frequency of 3.225 GHz, respectively. The value of the scattering parameter (S11) is − 36 dB at the resonant frequency. By using a DGS-based partial ground plane and parasitic elements, it enhances bandwidth to 2.19 GHz. Therefore, the tested prototype is an excellent candidate to be deployed for 5G/WMAN applications with respect to the different presented parametric studies.

Non-dominated Sorting Genetic Optimization-based Fog Cloudlet Computing for Wireless Metropolitan Area Networks

Non-dominated Sorting Genetic Optimization-based Fog Cloudlet Computing for Wireless Metropolitan Area Networks

Load Balancing for Multiedge Collaboration in Wireless Metropolitan Area Networks: A Two-Stage Decision-Making Approach

Mobile edge computing (MEC) relieves the latency and energy consumption of mobile applications by offloading computation-intensive tasks to nearby edges. In wireless metropolitan area networks (WMANs), edges can better provide computing services via advanced communication technologies. For improving the Quality-of-Service (QoS), edges need to be collaborated rather than working alone. However, the existing solutions of multi-edge collaboration solely adopt a centralized or decentralized decision-making way of load balancing, making it hard to achieve the optimal result because the local and global conditions are not jointly considered. To solve this problem, we propose a novel Two-stage Decision-making method of load Balancing for multi-Edge Collaboration (TDB-EC). First, the centralized decision-making is executed with global information, where a deep neural networks (DNN) based prediction model is designed to evaluate the range of task scheduling between adjacent edges. Next, considering the global condition of load balancing, the decentralized decision-making is executed with local information, where a deep Q-networks (DQN) based Q-value prediction model of adjustment operations is developed to evaluate the load balancing plan among edges. Finally, the objective load balancing plan is obtained via feedback-control. Extensive simulation experiments demonstrate the adaptability of the TDB-EC to various scenarios of multi-edge load balancing, which approximates the optimal result and outperforms three classic methods.

Model of Optimal Placement of Cloudlets in a Wireless Metropolitan Area Network

Cloud computing has recently emerged as a new paradigm for processing and storing large amounts of data. The rapid increase in the number of mobile phones and IoT devices benefitingfrom cloud computing services reduces the Internet bandwidth, resulting in delays in delivering data processed on remote cloud servers to the user. Mobile devices use edge computing systems (cloudlet, fog computing, etc.) to overcome resource shortages, power consumption and delays in communication channels. Edge computing systems place processing devices (cloudlets) close to users. The closer the cloudlets to mobile devices, the lower the processing time and energy consumption of the mobile device, and the higher the bandwidth of communication channels. Thus, cloudlet-based mobile computing clouds are widely used to reduce the latency in the Internet communication channels and energy consumption on mobile devices. This article identifies the most popular places for cloud servers in metropolitan mobile networks and discusses the optimal placement of a limited number of cloudlets in those places.

Cost-Aware Placement Optimization of Edge Servers for IoT Services in Wireless Metropolitan Area Networks

Edge computing migrates cloud computing capacity to the edge of the network to reduce latency caused by congestion and long propagation distance of the core network. And the Internet of things (IoT) service requests with large data traffic submitted by users need to be processed quickly by corresponding edge servers. The closer the edge computing resources are to the user network access point, the better the user experience can be improved. On the other hand, the closer the edge server is to users, the fewer users will access simultaneously, and the utilization efficiency of nodes will be reduced. The capital investment cost is limited for edge resource providers, so the deployment of edge servers needs to consider the trade-off between user experience and capital investment cost. In our study, for edge server deployment problems, we summarize three critical issues: edge location, user association, and capacity at edge locations through the research and analysis of edge resource allocation in a real edge computing environment. For these issues, this study considers the user distribution density (load density), determines a reasonable deployment location of edge servers, and deploys an appropriate number of edge computing nodes in this location to improve resource utilization and minimize the deployment cost of edge servers. Based on the objective minimization function of construction cost and total access delay cost, we formulate the edge server placement as a mixed-integer nonlinear programming problem (MINP) and then propose an edge server deployment optimization algorithm to seek the optimal solution (named Benders_SD). Extensive simulations and comparisons with the other three existing deployment methods show that our proposed method achieved an intended performance. It not only meets the low latency requirements of users but also reduces the deployment cost.

A Time-Driven Cloudlet Placement Strategy for Workflow Applications in Wireless Metropolitan Area Networks.

With the rapid development of mobile technology, mobile applications have increasing requirements for computational resources, and mobile devices can no longer meet these requirements. Mobile edge computing (MEC) has emerged in this context and has brought innovation into the working mode of traditional cloud computing. By provisioning edge server placement, the computing power of the cloud center is distributed to the edge of the network. The abundant computational resources of edge servers compensate for the lack of mobile devices and shorten the communication delay between servers and users. Constituting a specific form of edge servers, cloudlets have been widely studied within academia and industry in recent years. However, existing studies have mainly focused on computation offloading for general computing tasks under fixed cloudlet placement positions. They ignored the impact on computation offloading results from cloudlet placement positions and data dependencies among mobile application components. In this paper, we study the cloudlet placement problem based on workflow applications (WAs) in wireless metropolitan area networks (WMANs). We devise a cloudlet placement strategy based on a particle swarm optimization algorithm using genetic algorithm operators with the encoding library updating mode (PGEL), which enables the cloudlet to be placed in appropriate positions. The simulation results show that the proposed strategy can obtain a near-optimal cloudlet placement scheme. Compared with other classic algorithms, this algorithm can reduce the execution time of WAs by 15.04–44.99%.

On Cost-Aware Heterogeneous Cloudlet Deployment for Mobile Edge Computing

Edge computing undertakes downlink cloud services and uplink terminal computing tasks, data interaction latency and network transmission cost are thus significantly reduced. Although a lot of research has been conducted in mobile edge computing (MEC), which assumed that all homogeneous cloudlets are placed in WMAN and user mobility is also ignored, little attention is paid to how to place heterogeneous cloudlets in wireless metropolitan area network (WMAN) to minimize the deployment cost of cloudlets. Meanwhile, the method of selecting an optimal access point (AP) for deployment, modeling and heuristic algorithm (HA) needs to be improved. Therefore, this paper design a new heterogeneous cloudlet deployment model considering the quality of service (QoS) and mobility of users, and the Improved Heuristic Algorithm (IHA) is proposed to minimize cloudlet deployment cost. The extensive simulations demonstrate that IHA is more efficient than HA and the designed model is superior to the existing work.

TURBO Coded OFDM Improves BER Performance Evaluation for Digital Video Broadcasting

Abstract: Turbo coded Orthogonal Frequency Division Multiplexing (OFDM) is attractive technique for high data rate in wireless communication applications, mobile communications (4G) and Wireless Metropolitan Area Networks (WMAN) and Digital Video Broadcasting (DVB). The performance of TURBO Coded 16 QAM and 64 QAM schemes with OFDM for high speed data rate applications is compared which is used in digital video broadcasting. BER analysis is used to assess the system's performance. Due to the high data rate of 64 QAM, attaining BER of ૚૙ି૝ requires Eb/No of 0.9 dB in 16QAM, whereas obtaining BER of ૚૙ି૝ requires 0.17 dB in 64QAM. By using OFDM removes Inter Symbol Interference (ISI) and Adjacent Channel Interference (ACI). Keywords: Wireless communication, mobile communication, OFDM, QAM, TURBO Encoding

Технические принципы создания беспроводной локальной сети Wi-Fi

This article discusses the stages of development, characteristics of a wireless local area network Wi-Fi, options for its topology and equipment used. Currently, there are such different types of wireless networks as Wireless Wide Area Network, Wireless Metropolitan Area Networks (Wireless Neighborhood Area Network), Wireless Local Area Network, Wireless Personal Area Network. Each of the listed wireless networks has its own specific range and application. Wireless LAN Wi-Fi refers to the Wireless Local Area Network. The first wireless LAN standard, Wi-Fi, was approved in 1997. From the moment of their appearance to the present day, the following standards of this network have appeared: 802.11a, 802.11b, 802.11g, 802.11n, 802.11ac, 802.11ad, 802.11ax. For each standard, the speeds and frequency ranges at which the data transmission was carried out are indicated, and the technologies on which they are built are described. Wireless LAN Wi-Fi has three topologies: 1) Ad-Hoc (point-to-point) or Independent Basic Service Set; 2) Basic Service Set ("client / server") and 3) Extended Service Set ("extended service areas"). To build a Wi-Fi network, network adapters, access points, routers, ADSL modems, Wi-Fi phones, Wi-Fi antennas, Wi-Fi repeaters are used. Wi-Fi adapters are used by devices that do not have a built-in Wi-Fi module. Wi-Fi access points are designed for organizing wireless access within a local network. Wi-Fi routers are designed to route traffic on a computer network. ADSL modems are used to access the Internet over a telephone line. Wi-Fi repeaters are used to expand the coverage of a wireless network using already installed equipment. Wi-Fi phones are wireless IP phones. Wi-Fi antennas are used to extend the range of a wireless network.

Load balancing edge server placement method with QoS requirements in wireless metropolitan area networks

Mobile edge computing (MEC) is concerned with moving complex tasks from data sources to nearby computing resources, which can reduce computing latency and remote cloud workload. Although there has been significant research in the field of MEC, research on edge server placement in wireless metropolitan area networks (WMANs) is overlooked, and the load balancing problem of edge servers is seldom discussed. From a practical perspective, how to place edge servers efficiently in WMANs while considering load balancing between edge servers is studied. A greedy algorithm is proposed that can balance the workload of edge servers more effectively. However, the performance of the greedy algorithm as the number of servers placed increases is not ideal. Therefore, the authors combine the greedy algorithm with a genetic algorithm (GA) to minimise the number of edge servers while ensuring load balancing between edge servers and quality of service (QoS) requirements for mobile users. Finally, they conduct simulation experiments and compare the proposed algorithms with other algorithms. The improved GA proposed is superior to the greedy algorithm in terms of load balancing and the number of servers. The experimental results demonstrate that the algorithm has excellent performance.

Research on term extraction technology in computer field based on wireless network technology

Wireless technology allows one or more devices to communicate without the need for a physical connection. Wireless technology and wired technology applications use a wireless frequency transmission device for transmitting data while using a cable. In the case of wireless medical data transmission, it can cover a large area of the home and domestic users. This guide evaluates and compares, and defines specific application scenarios for Wireless Personal Area Networks (WPAN), Wireless Local Area Networks (WLAN), and Wireless Metropolitan Area Networks (WAN) operating in a moderate computer field. The extraction of standard terms in the computer field provides a measurement and control network protocol for the home and residential communities that enables them to achieve green and smarter features. It specifies the data format of the interaction between the device and the system and provides a standardized definition of sensors, actuators, and devices, along with data communication processing. This standard includes method definition test configuration and management capabilities, deployment and orientation control-related features, conformance testing, and data format definitions for interoperability. Independent protocol is defined for discovering and establishing media access methods. This standard supports between ports connected to the same access network, including intercommunication between ports and ports General support specified in secure communication Architectural, functional elements, and protocols. Authentication with IEEE 802.1AE ™ MAC (Mobile Access Control) uses security associations securely.

Method for Effective Use of Cloudlet Network Resources

The article addresses the issue of balanced placement of mobile software applications of mobile users in cloudlets deployed near base stations of Wireless Metropolitan Area Networks (WMAN), taking into account their technical capabilities. It is noted that the proposed model is more efficient in meeting the demand for computing and memory resources of mobile devices, eliminating network delays and using a reliable communication channel. At the same time, a minimum of cloudlet-based communication channels with a mobile user was suggested, reducing the network load and reliability of the communication channel when using multimedia software on mobile devices. The article reviews the balanced distribution of the tasks in the cloudlet network. If a user offloads the task to the nearest cloud and resolves it there, then the delays and energy consumption will be less. When the cloudlet is far from the mobile device, as the number of communication channels increases the delays are observed. Moreover, the article discusses the issue of selecting the cloudlets that meet some of the user requirements. Using the possible values that determine the importance of cloudlets (vacant resources in cloudlets, closeness of cloudlets to the user, high reliability, etc.), the conditions, according to which the user's application is offloaded to the certain cloudlet, are studied and a method is proposed.

Modulation and RS-CC rate specifications in WiMAX IEEE 802.16 Standard with MATLAB Simulink model

The intensive use of broadband system leading to focusing of telecommunication development industries, which are characterized by high quality features. Forthisissue, high transmission abilities in a new technologies systems have been designed. the WiMAX concept were proposed where a wireless transmission infrastructure that allows a fast deployment as well as low maintenance costs. Based on the IEEE 802.16-2004 standard, in this paper an end-to-end baseband model of the physical layer of a wireless metropolitan area network (WMAN) have been implemented using MATALB Simulink, according to the IEEE® 802.16-2004 standard. Supporting all of the mandatory coding and modulation options. It also illustrates Space-Time Block Coding (STBC), an optional transmit diversity scheme specified for use on the downlink. it illustrates the use of digital pre-distortion, a technique for extending the linear range of a nonlinear amplifier. Also modulation sets have been changed and tested in the model to reduce running time and power consumption. Finally, measurements of Error vector magnitude EVM and modulation error ratio MER were performed.

QoS-Aware Cloudlet Load Balancing in Wireless Metropolitan Area Networks

With advances in wireless communication technology, more and more people depend heavily on portable mobile devices for business, entertainments and social interactions. This poses a great challenge of building a seamless application experience across different computing platforms. A key issue is the resource limitations of mobile devices due to their portable size, however this can be overcome by offloading computation-intensive tasks from the mobile devices to clusters of nearby computers called cloudlets through wireless access points. As increasing numbers of people access the Internet via mobile devices, it is reasonable to envision in the near future that cloudlet services will be available for the public through easily accessible public wireless metropolitan area networks (WMANs). However, the outdated notion of treating cloudlets as isolated data-centers-in-boxes must be discarded as there are clear benefits to connecting multiple cloudlets together to form a network. In this paper we investigate how to balance the workload among cloudlets in an WMAN to optimize mobile application performance. We first introduce a novel system model to capture the response time delays of offloaded tasks and formulate an optimization problem with the aim to minimize the maximum response time of all offloaded tasks. We then propose two algorithms for the problem: one is a fast heuristic, and another is a distributed genetic algorithm that is capable of delivering a more accurate solution compared with the first algorithm, but at the expense of a much longer running time. We finally evaluate the performance of the proposed algorithms in realistic simulation environments. The experimental results demonstrate the significant potential of the proposed algorithms in reducing the user task response time, maximizing user experience.

A New Cloudlet Placement Method Based on Affinity Propagation for Cyber-Physical-Social Systems in Wireless Metropolitan Area Networks

Cyber-Physical-Social Systems (CPSS) integrates the cyber, physical and social spaces together. There are a large number of mobile users in CPSS that need low latency services. Fortunately, mobile edge computing (MEC) is a novel technology which can provide such services. The edge server plays a key role in MEC, but how to manage the edge server is an important challenge. For one thing, the number of cloudlets and the resource are limited. For another, the number of mobile devices (MDs) is very large and randomly distributed. And thus, how to determine the suitable number of cloudlets while serving the maximum number of MDs is significant. To this end, a new cloudlet placement method based on improved Affinity Propagation (AP) algorithm is proposed to solve the above problems. More specially, the improved AP algorithm can obtain the least number of cloudlets while covering the largest number of MDs. In addition, the load balancing strategy is used to ensure that the load of each cloudlet maintains a balanced state. Last but not the least, our proposed method can be used in scenarios where users move.

Testing different Channel Estimation Techniques in Real-Time Software Defined Radio Environment

In modern wireless communication to maximize spectral efficiency and to minimize the bit error rate OFDM (Orthogonal frequency-domain multiplexing) is used. OFDM is used broadly in networks using various protocols, including wireless vehicular environment IEEE 802.11p, IEEE 802.16d/e Wireless Metropolitan Area Networks, Long-Term Evolution 3GPP networks and IEEE 802.11a/g/n Wireless Local Area Networks. The main challenges involved when using OFDM for wireless communications are short channel-coherence bandwidth and the narrow coherence time, and both have a major effect on the reliability and latency of data packet communication. These properties increase the difficulty of channel equalization because the channel may change drastically over the period of a single packet. Spectral Temporal Averaging is an enhanced decision-directed channel equalization technique that improves communication performance (as far as the frame delivery ratio (FDR) and throughput) in typical channel conditions. This paper reports tests of Spectral Temporal Averaging channel equalization in an IEEE 802.11a network, compared with other channel equalization techniques in terms of the FDR in a real-time environment. Herein, a software defined Radio (SDR) platform was used for estimating the channel. This proves that the system can provide over 90% of delivery ratio at 25 db of Signal to Noise Ratio (SNR) for various digital modulation techniques. For this purpose, an experimental setup consisting of software-defined radio, Universal Software Radio Peripheral (USRP) N210 along with wide bandwidth daughter board as hardware and GNU radio is used.

A Long-Term Cost-Oriented Cloudlet Planning Method in Wireless Metropolitan Area Networks

As an extension of remote cloud data centers, cloudlets process the workloads from mobile users at the network edge, thereby satisfying the requirements of resource-intensive and latency-sensitive applications. One of the fundamental yet important issues for cloudlet infrastructure providers (ISP) is how to plan the placement and capacities of cloudlets so that minimize their long-term cost with a guarantee on service delay. However, existing work mostly focuses on resource provision or resource management for mobile services on existing cloudlets, while very little attention has been paid to the cloudlet placement and capacity planning problem. In contrast to those studies, we aim to optimize the long-term total cost of cloudlets’ ISPs through intelligently planning the location and capacities of cloudlets under constraints on the service delay experienced by mobile users. This problem is then decomposed into two sub-problems and algorithms are devised to solve it. Evaluations on randomly generated traces and real traces exhibit the superior performance of the proposed solution on saving ISP’s long-term cost.

Energy-Efficient Virtual Machine Scheduling across Cloudlets in Wireless Metropolitan Area Networks

Nowadays, with the development of wireless communication, people are relying on mobile devices heavily due to various application deployment and plentiful service experience of customers in wireless metropolitan area network (WMAN). The computing resources of the mobile devices are limited as they are restricted with physical size, battery capacity, etc. In order to release the resource limitation, cloudlet, an effective paradigm, is introduced to host the computing tasks offloaded from the mobile devices. Compared with the cloud computing, the cloudlets are deployed closer to provide customers with fewer task offloading delay. Currently, the offloaded task scale is increasing, which generates large quantities of energy exposure for task implementation among cloudlets. Taking advantage of live virtual machine (VM) migration technology, the energy consumption of cloudlets could be definitely reduced. But such migration across cloudlets also decreases the implementation performance of the tasks. Therefore, it is still a challenge to jointly optimize the execution performance and the energy consumption for cloudlet management in WMAN. In this paper, a novel virtual machine (VM) scheduling method is proposed to balance the implantation time and the energy consumption to cope with the above challenge. Specifically, a collection of available migration polices are obtained through heuristically searching of destination cloudlets for the running computing tasks. Then, simple additive weighting (SAW) and multiple criteria decision making (MCDM) techniques are leveraged to select the optimal VM scheduling strategy across cloudlets in WMAN. Finally, experimental results and evaluations validate our proposed method is both effective and feasible.

An energy-aware computation offloading method for smart edge computing in wireless metropolitan area networks

Currently, the booming popularity and growth of mobile devices in urban cities leads to the surge of various computation-intensive mobile applications, such as virtual reality and online video, which are strict with the computing capability and battery life of the mobile devices. To address this issue, smart edge computing in Wireless Metropolitan Area Networks (WMAN) is proposed to enable mobile users to offload computation-intensive tasks to the edge computing nodes which deploys computing resources nearby the mobile devices. However, the normal operation of edge computing nodes consumes plenty of energy. Thus, it is still a challenge to be aware of energy consumption while the computing tasks are migrated to the Edge Computing Nodes (ECNs). In view of this challenge, an Energy-Aware Computation Offloading method, named EACO, is designed to reduce the energy consumption. Technically, we analyze all access point (AP) routings between the original AP to destination AP and select the shortest path to offload the computing tasks. Furthermore we adopt Non-dominated Sorting Genetic Algorithm II (NSGA-II) to realize multi-objective optimization to shorten the offloading time of the computing tasks and reduce the energy consumption of the ECNs. Besides, we exploit Multiple Criteria Decision Marking (MCDM) and Simple Additive Weighting (SAW) to select the optimal offloading solution. Finally, the simulation experimental results show that our proposed EACO outperforms other methods.

Procedure of Effective Use of Cloudlets in Wireless Metropolitan Area Network Environment

The article develops a method to ensure the efficient use of cloudlet resources by the mobile users. The article provides a solution to the problem of correct use of cloudlets located on the movement route of mobile users in Wireless Metropolitan Area Networks - WMAN environment. Conditions for downloading necessary applications to the appropriate cloudlet using the possible values that determine the importance and coordinates of the cloudlets were studied. The article provides a model of the mobile user's route model in metropolitan environments and suggests a method for solving the problem.