Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

The use of flying platforms such as unmanned aerial vehicles (UAVs), popularly known as drones, is rapidly growing. In particular, with their inherent attributes such as mobility, flexibility, and adaptive altitude, UAVs admit several key potential applications in wireless systems. On the one hand, UAVs can be used as aerial base stations to enhance coverage, capacity, reliability, and energy efficiency of wireless networks. On the other hand, UAVs can operate as flying mobile terminals within a cellular network. Such cellular-connected UAVs can enable several applications ranging from real-time video streaming to item delivery. In this paper, a comprehensive tutorial on the potential benefits and applications of UAVs in wireless communications is presented. Moreover, the important challenges and the fundamental tradeoffs in UAV-enabled wireless networks are thoroughly investigated. In particular, the key UAV challenges such as 3D deployment, performance analysis, channel modeling, and energy efficiency are explored along with representative results. Then, open problems and potential research directions pertaining to UAV communications are introduced. Finally, various analytical frameworks and mathematical tools, such as optimization theory, machine learning, stochastic geometry, transport theory, and game theory are described. The use of such tools for addressing unique UAV problems is also presented. In a nutshell, this tutorial provides key guidelines on how to analyze, optimize, and design UAV-based wireless communication systems.

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A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems

This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking. Modern networks, e.g., Internet of Things (IoT) and unmanned aerial vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, DRL, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of DRL from fundamental concepts to advanced models. Then, we review DRL approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks, such as 5G and beyond. Furthermore, we present applications of DRL for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying DRL.

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

The use of flying platforms such as unmanned aerial vehicles (UAVs), popularly known as drones, is rapidly growing. In particular, with their inherent attributes such as mobility, flexibility, and adaptive altitude, UAVs admit several key potential applications in wireless systems. On the one hand, UAVs can be used as aerial base stations to enhance coverage, capacity, reliability, and energy efficiency of wireless networks. On the other hand, UAVs can operate as flying mobile terminals within a cellular network. Such cellular-connected UAVs can enable several applications ranging from real-time video streaming to item delivery. In this paper, a comprehensive tutorial on the potential benefits and applications of UAVs in wireless communications is presented. Moreover, the important challenges and the fundamental tradeoffs in UAV-enabled wireless networks are thoroughly investigated. In particular, the key UAV challenges such as three-dimensional deployment, performance analysis, channel modeling, and energy efficiency are explored along with representative results. Then, open problems and potential research directions pertaining to UAV communications are introduced. Finally, various analytical frameworks and mathematical tools such as optimization theory, machine learning, stochastic geometry, transport theory, and game theory are described. The use of such tools for addressing unique UAV problems is also presented. In a nutshell, this tutorial provides key guidelines on how to analyze, optimize, and design UAV-based wireless communication systems.

Femtocells are being considered a promising technique to improve the capacity and coverage for indoor wireless users. However, the cross-tier interference in the spectrum-sharing deployment of femtocells can degrade the system performance seriously. The resource allocation problem in both the uplink and the downlink for two-tier networks comprising spectrum-sharing femtocells and macrocells is investigated. A resource allocation scheme for cochannel femtocells is proposed, aiming to maximize the capacity for both delay-sensitive users and delay-tolerant users subject to the delay-sensitive users' quality-of-service constraint and an interference constraint imposed by the macrocell. The subchannel and power allocation problem is modeled as a mixed-integer programming problem, and then, it is transformed into a convex optimization problem by relaxing subchannel sharing; finally, it is solved by the dual decomposition method. Subsequently, an iterative subchannel and power allocation algorithm considering heterogeneous services and cross-tier interference is proposed for the problem using the subgradient update. A practical low-complexity distributed subchannel and power allocation algorithm is developed to reduce the computational cost. The complexity of the proposed algorithms is analyzed, and the effectiveness of the proposed algorithms is verified by simulations.

Resource Allocation in Spectrum-Sharing OFDMA Femtocells With Heterogeneous Services

The femtocell networks that use Home eNodeB and existing networks as backhaul connectivity can fulfill the upcoming demand of high data rate for wireless communication system as well as can extend the coverage area. It is also of interest to minimize operational effort by introducing self-optimizing mechanisms, and the optimization of the Home eNodeB involved handover is an important goal of LTE-Advanced. Since the different network architecture and functionality between Home eNodeB and LTE eNodeB, the handover procedure between the femtocell and macrocell should be modified in LTE network. In this paper, modified signaling procedure of handover is presented in the Home eNodeB gateway based femtocell network architecture. A new handover algorithm based on the UE’s speed and QoS is proposed. The comparison between the proposed algorithm and the traditional handover algorithm shows that the algorithms proposed in this paper have a better performance in the reducing of unnecessary handovers and the number of handovers.

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A Novel Handover Mechanism Between Femtocell and Macrocell for LTE Based Networks

For fifth-generation wireless communication systems, network slicing has emerged as a key concept to meet the diverse requirements of various use cases. By slicing an infrastructure network into multiple dedicated logical networks, wireless networks can support a wide range of services. However, how to fast deploy the end-to-end slices is the main issue in a multi-domain wireless network infrastructure. In this paper, a mathematical model is used to construct network slice requests and map them to the infrastructure network. The mapping process consists of two steps: the placement of virtual network functions and the selection of link paths chaining them. To efficiently utilize the limited physical resources, we pay attention to the service-oriented deployment by offering different deployment policies for three typical slices: eMBB slices, mMTC slices, and uRLLC slices. Furthermore, we adopt complex network theory to obtain the topological information of slices and infrastructure network. With the topological information, we define a node importance metric to rank the nodes in node mapping. To evaluate the performance of deployment policy we proposed, extensive simulations have been conducted. The results have shown that our algorithm performed better in terms of resource efficiency and acceptance ratio. In addition, the average execute time of our algorithm is in a linear growth with the increase of infrastructure network size.

A Service-Oriented Deployment Policy of End-to-End Network Slicing Based on Complex Network Theory

Network function virtualization (NFV) has already been a new paradigm for network architectures. By migrating NFs from dedicated hardware to virtualization platform, NFV can effectively improve the flexibility to deploy and manage service function chains (SFCs). However, resource allocation for requested SFC in NFV-based infrastructures is not trivial as it mainly consists of three phases: virtual network functions (VNFs) chain composition, VNFs forwarding graph embedding, and VNFs scheduling. The decision of these three phases can be mutually dependent, which also makes it a tough task. Therefore, a coordinated approach is studied in this paper to jointly optimize NFV resource allocation in these three phases. We apply a general cost model to consider both network costs and service performance. The coordinate NFV-RA is formulated as a mixed-integer linear programming, and a heuristic-based algorithm (JoraNFV) is proposed to get the near optimal solution. To make the coordinated NFV-RA more tractable, JoraNFV is divided into two sub-algorithms, one-hop optimal traffic scheduling and a multi-path greedy algorithm for VNF chain composition and VNF forwarding graph embedding. Last, extensive simulations are performed to evaluate the performance of JoraNFV, and results have shown that JoraNFV can get a solution within 1.25 times of the optimal solution with reasonable execution time, which indicates that JoraNFV can be used for online NFV planning.

Joint Optimization of Service Function Chaining and Resource Allocation in Network Function Virtualization

Femtocell is a small access point using the wire broadband connections or wireless technologies to access the mobile operator's network for the user equipment(UE), which can provide better indoor coverage and satisfy the upcoming demand of high data rate for wireless communication system.Femtocell related handover cost reduction is one of the important targets in LTE-Advanced SON (Self-Organising Networks). In this paper, a handover optimization algorithm based on the UE's mobility state is proposed. An analytical model was presented for the handover signalling cost analysis. Numerical results are provided to compare the signalling cost of different handover management schemes. The comparison between the proposed algorithm and the traditional handover control algorithm shows that the algorithms proposed in this paper have a signiﬁcant reduction in the signalling overhead.

Xiangming Wen

Papers

Resource Allocation in Spectrum-Sharing OFDMA Femtocells With Heterogeneous Services

A Novel Handover Mechanism Between Femtocell and Macrocell for LTE Based Networks

A Service-Oriented Deployment Policy of End-to-End Network Slicing Based on Complex Network Theory

Joint Optimization of Service Function Chaining and Resource Allocation in Network Function Virtualization

Signalling Cost Evaluation of Handover Management Schemes in LTE-Advanced Femtocell