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

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Linear And Nonlinear Programming

In this chapter we deal with the following nonlinear fractional programming problem: 

$$P:\mathop{{\max }}\limits_{{x \in s}} q(x) = (f(x) + \alpha )/((x) + \beta )$$

where f, g: R n → R, α, β ∈ R, S ⊆ R n . To simplify things, and without restricting the generality of the problem, it is usually assumed that, g(x) + β + 0 for all x ∈ S,S is non-empty and that the objective function has a finite optimal value.

Nonlinear Fractional Programming

Visible Light Communication (VLC) is an emerging field in Optical Wireless Communication (OWC) which utilizes the superior modulation bandwidth of Light Emitting Diodes (LEDs) to transmit data. In modern day communication systems, the most popular frequency band is Radio Frequency (RF) mainly due to little interference and good coverage. However, the rapidly dwindling RF spectrum along with increasing wireless network traffic has substantiated the need for greater bandwidth and spectral relief. By combining illumination and communication, VLC provides ubiquitous communication while addressing the shortfalls and limitations of RF communication. This paper provides a comprehensive survey on VLC with an emphasis on challenges faced in indoor applications over the period 1979–2014. VLC is compared with infrared (IR) and RF systems and the necessity for using this beneficial technology in communication systems is justified. The advantages of LEDs compared to traditional lighting technologies are discussed and comparison is done between different types of LEDs currently available. Modulation schemes and dimming techniques for indoor VLC are discussed in detail. Methods needed to improve VLC system performance such as filtering, equalization, compensation, and beamforming are also presented. The recent progress made by various research groups in this field is discussed along with the possible applications of this technology. Finally, the limitations of VLC as well as the probable future directions are presented.

LED Based Indoor Visible Light Communications: State of the Art

Automatic modulation recognition (AMR) is an essential and challenging topic in the development of the cognitive radio (CR), and it is a cornerstone of CR adaptive modulation and demodulation capabilities to sense and learn environments and make corresponding adjustments. AMR is essentially a classification problem, and deep learning achieves outstanding performances in various classification tasks. So, this paper proposes a deep learning-based method, combined with two convolutional neural networks (CNNs) trained on different datasets, to achieve higher accuracy AMR. A CNN is trained on samples composed of in-phase and quadrature component signals, otherwise known as in-phase and quadrature samples, to distinguish modulation modes, that are relatively easy to identify. We adopt dropout instead of pooling operation to achieve higher recognition accuracy. A CNN based on constellation diagrams is also designed to recognize modulation modes that are difficult to distinguish in the former CNN, such as 16 quadratic-amplitude modulation (QAM) and 64 QAM, demonstrating the ability to classify QAM signals even in scenarios with a low signal-to-noise ratio.

Data-Driven Deep Learning for Automatic Modulation Recognition in Cognitive Radios

The Internet of Things (IoT) has attracted significant attentions in the fifth generation mobile networks and the smart cities. However, considering the large numbers of connectivity demands, it is vital to improve the spectrum efficiency (SE) of the IoT with an affordable power consumption. To improve the SE, the nonorthogonal multiple access (NOMA) technology is newly proposed through accommodating multiple users in the same spectrums. As a result, in this paper, an energy efficient resource allocation (RA) problem is introduced for the NOMA-based heterogeneous IoT. At first, we assume the successive interference cancelation (SIC) is imperfect for practical implementations. Then, based on the analyzing method for cognitive radio networks, we present a stepwise RA scheme for the mobile users and the IoT users with the mutual interference management. Third, we propose a deep recurrent neural network-based algorithm to solve the problem optimally and rapidly. Moreover, a priorities and rate demands-based user scheduling method is supplemented, to coordinate the access of the heterogeneous users with the limited radio resource. At last, the simulation results verify that the deep learning-based scheme is able to provide optimal RA results for the NOMA heterogeneous IoT with fast convergence and low computational complexity. Compared with the conventional orthogonal frequency division multiple access system, the NOMA system with imperfect SIC yields better performance on the SE and the scale of connectivity, at the cost of high power consumption and low energy efficiency.

Deep Cognitive Perspective: Resource Allocation for NOMA-Based Heterogeneous IoT With Imperfect SIC

Energy efficiency (EE) and spectrum efficiency (SE) have received significant attentions on optimizing the network performance in cognitive radio networks. In this paper, an EE+SE tradeoff based target is considered for the primary users (PUs) and the secondary users (SUs). First of all, considering the orthogonal frequency division multiple access-based resource allocation (RA) for the underlying SUs, we formulate an objective function through minimizing a weighted sum of the secondary interference power, where the network performance of both PUs and SUs are guaranteed by the constraints on quality of service, power consumption and data rate. However, it is a NP-hard problem. In order to solve it, we propose a damped three dimensional (D3D) message-passing algorithm (MPA) based on deep learning. Specifically, a feed-forward neural network is devised and an analogous back propagation algorithm is developed to learn the optimal parameters of the D3D-MPA. To improve the computational efficiency of the allocation and the learning, a suboptimal RA scheme is deduced based on a damped two dimensional MPA. Finally, simulation results are provided to confirm the effectiveness of our proposed scheme.

Deep Learning-Inspired Message Passing Algorithm for Efficient Resource Allocation in Cognitive Radio Networks

In cognitive radio (CR) systems, the spectrum efficiency (SE) of the secondary users (SUs) is always limited by the interference temperature constraint imposed on the primary users (PUs). Intelligent reflecting surface (IRS) has been recently proposed as a revolutionary technique which can help to enhance the SE of wireless communications. In this paper, we propose to employ an IRS to assist the SUs’ data transmission in the multiple-input multiple-output (MIMO) CR system. By jointly optimizing the transmit precoding (TPC) of the SU transmitter (ST) and the phase shifts of the IRS, we aim to maximize the achievable weighted sum rate (WSR) of SUs subject to the ST's total power, the PU's interference temperature and unit modulus constraints. To solve this complicated optimization problem in which the variables are coupled, the block coordinate descent (BCD) algorithm is introduced to alternately solve the subproblems. For each subproblem, the Lagrange dual or inner approximation method is adopted with a low complexity. Simulation results confirm the benefits of employing IRS in a MIMO CR system. The performance comparisons of the proposed algorithm with several other benchmarks are carried out by evaluating the impacts of various parameters on the WSR.

Intelligent Reflecting Surface Aided MIMO Cognitive Radio Systems

Visible light communication (VLC) uses a vast unregulated and free light spectrum. It is considered to be a solution for overcoming the crowded radio spectrum for wireless communication systems. However, duplex communication, user mobility, and handover mechanisms are becoming challenging tasks in a VLC system. This paper proposes a hybrid network model of VLC and orthogonal frequency-division multiplexing access (OFDMA) in which the VLC channel is only used for downlink transmission, whereas OFDMA channels are served for uplinks in any situation or for downlinks only without VLC hotspots coverage. A novel protocol is proposed combined with access, horizontal, and vertical handover mechanisms for mobile terminal (MT) to resolve user mobility among different hotspots and OFDMA system. A new VLC network scheme and its frame format are presented to deal with the multiuser access problems in every hotspot. In addition, a new metric $\rho$ is defined to evaluate the capacity of this hybrid network as the spatial density of interarrival time of MT requests in $\hbox{s}^{-1}\hbox{m}^{-2}$ under the assumption of the homogenous Poisson point process (HPPP) distribution of MTs. Analytical and simulation results show improvements in capacity performance of the hybrid, when compared to OFDMA system.

Protocol Design and Capacity Analysis in Hybrid Network of Visible Light Communication and OFDMA Systems

In this paper, we investigate the physical-layer security of cooperative communications relying on multiple two-way relays using the decode-and-forward (DF) protocol in the presence of an eavesdropper, where the eavesdropper appears to tap the transmissions of both the source and of the relay. The design tradeoff to be resolved is that the throughput is improved by invoking two-way relaying, but the secrecy of wireless transmissions may be degraded, since the eavesdropper may overhear the signals transmitted by both the source and relay nodes. We conceive an artificial noise aided two-way opportunistic relay selection (ANaTWORS) scheme for enhancing the security of the pair of source nodes communicating with the assistance of multiple two-way relays. Furthermore, we analyze both the outage probability and intercept probability of the proposed ANaTWORS scheme, where the security and reliability are characterized in terms of the intercept probability and the security outage probability. For comparison, we also provide the security–reliability tradeoff (SRT) analysis of both the traditional direct transmission and of the one-way relaying schemes. It is shown that the proposed ANaTWORS scheme outperforms both the conventional direct transmission, as well as the one-way relay methods in terms of its SRT. More specifically, in the low main-user-to-eavesdropper ratio (MUER) region, the proposed ANaTWORS scheme is capable of guaranteeing secure transmissions, whereas no SRT gain is achieved by conventional one-way relaying. In fact, the one-way relaying scheme may even be inferior to the traditional direct transmission scheme in terms of its SRT.

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Tiecheng Song

Papers

Deep Cognitive Perspective: Resource Allocation for NOMA-Based Heterogeneous IoT With Imperfect SIC

Deep Learning-Inspired Message Passing Algorithm for Efficient Resource Allocation in Cognitive Radio Networks

Intelligent Reflecting Surface Aided MIMO Cognitive Radio Systems

Protocol Design and Capacity Analysis in Hybrid Network of Visible Light Communication and OFDMA Systems

Security-Reliability Tradeoff Analysis of Artificial Noise Aided Two-Way Opportunistic Relay Selection