An Introduction to Network Simulator NS2 is a beginners’ guide for network simulator NS2, an open-source discrete event simulator designed mainly for networking research. NS2 has been widely accepted as a reliable simulation tool for computer communication networks both in academia and industry. This book will present two fundamental NS2 concepts:i) how objects (e.g., nodes, links, queues, etc.) are assembled to create a network and ii) how a packet flows from one object to another. Based on these concepts, this book will demonstrate through examples how new modules can be incorporated into NS2. The book will: -Give an overview on simulation and communication networks. -Provide general information (e.g., installation, key features, etc.) about NS2. -Demonstrate how to set up a simple network simulation scenario using Tcl scripting lanuage. -Explain how C++ and OTcl (Object oriented Tcl) are linked, and constitute NS2. -Show how Ns2 interprets a Tcl Script and executes it. -Suggest post simulation processing approaches and identify their pros and cons. -Present a number of NS2 extension examples. -Discuss how to incorporate MATLAB into NS2.
Introduction to Network Simulator NS2 is a primer providing materials for NS2 beginners, whether students, professors, or researchers for understanding the architecture of Network Simulator 2 (NS2) and for incorporating simulation modules into NS2. The authors discuss the simulation architecture and the key components of NS2 including simulation-related objects, network objects, packet-related objects, and helper objects. The NS2 modules included within are nodes, links, SimpleLink objects, packets, agents, and applications. Further, the book covers three helper modules: timers, random number generators, and error models. Also included are chapters on summary of debugging, variable and packet tracing, result compilation, and examples for extending NS2. Two appendices provide the details of scripting language Tcl, OTcl and AWK, as well object oriented programming used extensively in NS2.
Covering the fundamental theory together with the state of the art in research and development, this practical guide provides the techniques needed to design, analyze, and optimize device-to-device (D2D) communications in wireless networking. With an ever-increasing demand for higher data rate wireless access, D2D communication is set to become a key feature supported by next generation cellular networks. This book introduces D2D-based wireless communications from the physical, MAC, network, and application layer perspectives, providing all the key background information before moving on to discuss real-world applications as well as potential future developments. Key topics are discussed in detail, such as dynamic resource sharing (for example of spectrum and power) between cellular and ad hoc D2D communications to accommodate larger volumes of traffic and provide better service to users. Readers will understand the practical challenges of resource management, optimization, security, standardization, and network topology, and learn how the design principles are applied in practice.
Deep Reinforcement Learning for Wireless Communications and Networking Comprehensive guide to Deep Reinforcement Learning (DRL) as applied to wireless communication systems Deep Reinforcement Learning for Wireless Communications and Networking presents an overview of the development of DRL while providing fundamental knowledge about theories, formulation, design, learning models, algorithms and implementation of DRL together with a particular case study to practice. The book also covers diverse applications of DRL to address various problems in wireless networks, such as caching, offloading, resource sharing, and security. The authors discuss open issues by introducing some advanced DRL approaches to address emerging issues in wireless communications and networking. Covering new advanced models of DRL, e.g., deep dueling architecture and generative adversarial networks, as well as emerging problems considered in wireless networks, e.g., ambient backscatter communication, intelligent reflecting surfaces and edge intelligence, this is the first comprehensive book studying applications of DRL for wireless networks that presents the state-of-the-art research in architecture, protocol, and application design. Deep Reinforcement Learning for Wireless Communications and Networking covers specific topics such as: Deep reinforcement learning models, covering deep learning, deep reinforcement learning, and models of deep reinforcement learning Physical layer applications covering signal detection, decoding, and beamforming, power and rate control, and physical-layer security Medium access control (MAC) layer applications, covering resource allocation, channel access, and user/cell association Network layer applications, covering traffic routing, network classification, and network slicing With comprehensive coverage of an exciting and noteworthy new technology, Deep Reinforcement Learning for Wireless Communications and Networking is an essential learning resource for researchers and communications engineers, along with developers and entrepreneurs in autonomous systems, who wish to harness this technology in practical applications.
Enables engineers and researchers to understand the fundamentals and applications of device-to-device communications and its optimization in wireless networking.
An all-inclusive introduction to this revolutionary technology, presenting the key research issues and state-of-the-art design, analysis, and optimization techniques.
Providing an extensive overview of the radio resource management problem in femtocell networks, this invaluable book considers both code division multiple access femtocells and orthogonal frequency-division multiple access femtocells. In addition to incorporating current research on this topic, the book also covers technical challenges in femtocell deployment, provides readers with a variety of approaches to resource allocation and a comparison of their effectiveness, explains how to model various networks using Stochastic geometry and shot noise theory, and much more.
This book collects articles featuring recent advances in the theory and applications of wireless mesh networking technology. The contributed articles, from the leading experts in the field, cover both theoretical concepts and system-level implementation issues. The book starts with the essential background on the basic concepts and architectures of wireless mesh networking and then presents advanced level materials in a step-by-step fashion.
Deep Reinforcement Learning for Wireless Communications and Networking Comprehensive guide to Deep Reinforcement Learning (DRL) as applied to wireless communication systems Deep Reinforcement Learning for Wireless Communications and Networking presents an overview of the development of DRL while providing fundamental knowledge about theories, formulation, design, learning models, algorithms and implementation of DRL together with a particular case study to practice. The book also covers diverse applications of DRL to address various problems in wireless networks, such as caching, offloading, resource sharing, and security. The authors discuss open issues by introducing some advanced DRL approaches to address emerging issues in wireless communications and networking. Covering new advanced models of DRL, e.g., deep dueling architecture and generative adversarial networks, as well as emerging problems considered in wireless networks, e.g., ambient backscatter communication, intelligent reflecting surfaces and edge intelligence, this is the first comprehensive book studying applications of DRL for wireless networks that presents the state-of-the-art research in architecture, protocol, and application design. Deep Reinforcement Learning for Wireless Communications and Networking covers specific topics such as: Deep reinforcement learning models, covering deep learning, deep reinforcement learning, and models of deep reinforcement learning Physical layer applications covering signal detection, decoding, and beamforming, power and rate control, and physical-layer security Medium access control (MAC) layer applications, covering resource allocation, channel access, and user/cell association Network layer applications, covering traffic routing, network classification, and network slicing With comprehensive coverage of an exciting and noteworthy new technology, Deep Reinforcement Learning for Wireless Communications and Networking is an essential learning resource for researchers and communications engineers, along with developers and entrepreneurs in autonomous systems, who wish to harness this technology in practical applications.
Heterogeneous wireless networking, which is sometimes referred to as the fourth-generation (4G) wireless, is a new frontier in the future wireless communications technology and there has been a growing interest on this topic among researchers and engineers in both academia and industry. This book will include a set of research and survey articles featuring the recent advances in theory and applications of heterogeneous wireless networking technology for the next generation (e.g., fourth generation) wireless communications systems. With the rapid growth in the number of wireless applications, services and devices, using a single wireless technology such as a second generation (2G) and third generation (3G) wireless system would not be efficient to deliver high speed data rate and quality-of-service (QoS) support to mobile users in a seamless way. Fourth generation (4G) wireless systems are devised with the vision of heterogeneity in which a mobile user/device will be able to connect to multiple wireless networks (e.g., WLAN, cellular, WMAN) simultaneously. This book intends to provide a unified view on the state-of-the-art of protocols and architectures for heterogeneous wireless networking. The contributed articles will cover both the theoretical concepts and system-level implementation issues related to design, analysis, and optimization of architectures and protocols for heterogeneous wireless access networks.
Providing an extensive overview of the radio resource management problem in femtocell networks, this invaluable book considers both code division multiple access femtocells and orthogonal frequency-division multiple access femtocells. In addition to incorporating current research on this topic, the book also covers technical challenges in femtocell deployment, provides readers with a variety of approaches to resource allocation and a comparison of their effectiveness, explains how to model various networks using Stochastic geometry and shot noise theory, and much more.
One-stop reference to state-of-the-art theory, key strategies/protocols, deployment aspects, standardization activities and experimental studies of technologies for the smart grid.
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