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

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

The 6G vision of creating authentic digital twin representations of the physical world calls for new sensing solutions to compose multi-layered maps of our environments. Radio sensing using the mobile communication network as a sensor has the potential to become an essential component of the solution. With the evolution of cellular systems to mmWave bands in 5G and potentially sub-THz bands in 6G, small cell deployments will begin to dominate. Large bandwidth systems deployed in small cell configurations provide an unprecedented opportunity to employ the mobile network for sensing. In this paper, we focus on the major design aspects of such a cellular joint communication and sensing (JCAS) system. We present an analysis of the choice of the waveform that points towards choosing the one that is best suited for communication also for radar sensing. We discuss several techniques for efficiently integrating the sensing capability into the JCAS system, some of which are applicable with NR air-interface for evolved 5G systems. Specifically, methods for reducing sensing overhead by appropriate sensing signal design or by configuring separate numerologies for communications and sensing are presented. Sophisticated use of the sensing signals is shown to reduce the signaling overhead by a factor of 2.67 for an exemplary road traffic monitoring use case. We then present a vision for future advanced JCAS systems building upon distributed massive MIMO and discuss various other research challenges for JCAS that need to be addressed in order to pave the way towards natively integrated JCAS in 6G.

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Joint Design of Communication and Sensing for Beyond 5G and 6G Systems

Radio frequency (RF) fingerprint is the inherent hardware characteristics and has been employed to classify and identify wireless devices in many Internet of Things applications. This paper extracts novel RF fingerprint features, designs a hybrid and adaptive classification scheme adjusting to the environment conditions, and carries out extensive experiments to evaluate the performance. In particular, four modulation features, namely differential constellation trace figure, carrier frequency offset, modulation offset and I/Q offset extracted from constellation trace figure, are employed. The feature weights under different channel conditions are calculated at the training stage. These features are combined smartly with the weights selected according to the estimated signal to noise ratio at the classification stage. We construct a testbed using universal software radio peripheral platform as the receiver and 54 ZigBee nodes as the candidate devices to be classified, which are the most ZigBee devices ever tested. Extensive experiments are carried out to evaluate the classification performance under different channel conditions, namely line-of-sight (LOS) and nonline-of-sight scenarios. We then validate the robustness by carrying out the classification process 18 months after the training, which is the longest time gap. We also use a different receiver platform for classification for the first time. The classification error rate is as low as 0.048 in LOS scenario, and 0.1105 even when a different receiver is used for classification 18 months after the training. Our hybrid classification scheme has thus been demonstrated effective in classifying a large amount of ZigBee devices.

Design of a Hybrid RF Fingerprint Extraction and Device Classification Scheme

Artificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.

Artificial Intelligence (AI) Methods in Optical Networks: A Comprehensive Survey

With the large-scale deployment of connected and autonomous vehicles, the demand on wireless communication spectrum increases rapidly in vehicular networks. Due to increased demand, the allocated spectrum at the 5.9 GHz band for vehicular communication cannot be used efficiently for larger payloads to improve cooperative sensing, safety, and mobility. To achieve higher data rates, the millimeter-wave (mmWave) automotive radar spectrum at 76–81 GHz band can be exploited for communication. However, instead of employing spectral isolation or interference mitigation schemes between communication and radar, we design a joint system for vehicles to perform both functions using the same waveform. In this paper, we propose radar processing methods that use pilots in the orthogonal frequency-division multiplexing (OFDM) waveform. While the radar receiver exploits pilots for sensing, the communication receiver can leverage pilots to estimate the time-varying channel. The simulation results show that proposed radar processing can be efficiently implemented and meet the automotive radar requirements. We also present joint system design problems to find optimal resource allocation between data and pilot subcarriers based on radar estimation accuracy and effective channel capacity.

https://par.nsf.gov/servlets/purl/10119316

OFDM Pilot-Based Radar for Joint Vehicular Communication and Radar Systems

This paper considers the end-to-end scheduling for all-optical data center networks with zero in-network buffer and non-negligible reconfiguration delay. It is known that in the regime where the scheduling reconfiguration delay is non-negligible, the rate of schedule reconfiguration should be limited in such a way as to minimize the impact of reduced duty-cycles and to ensure bounded delay. However, when the scheduling rate is restricted, the existing literature also tends to restrict the rate of monitoring and decision processes. We first present a framework for scheduling with reconfiguration delay that decouples the rate of scheduling from the rate of monitoring. Under this framework, we then present two scheduling algorithms for switches with reconfiguration delay, both based on the well-known MaxWeight scheduling policy. The first one is the Periodic MaxWeight (PMW), which is simpler in computation, but requires prior knowledge of traffic load. The other is the Adaptive MaxWeight (AMW), which, in contrast, requires no prior knowledge. We show the stability condition for both algorithms and evaluate their delay performance through simulations.

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End-to-end scheduling for all-optical data centers

Systems and methods according to present principles provide an architecture for data center networks with many, e.g., possibly up to thousands, top of rack (ToR) switches, by employing an architecture that relies on a separation of the data and the control planes. While the data is switched between the ToR switches in an all-optical high rate network, network state and control information is continuously transmitted and received from a central unit (also termed a control unit or centralized unit) over an ultra-low-latency wireless/wired network.

Architecture and control plane for data centers

The Internet of Vehicle (IoV) network refers to networking technologies that could support highly economical and effective communication channels for interactions between vehicles. The success of IoV relies on efficient vehicle-to-infrastructure (V2I) or vehicle-to-vehicle (V2V) communications. Among all existing communication standards, the IEEE 802.15.4 standard, which comprises a simple physical (PHY) layer and an energy-efficient medium access control, is a promising candidate for IoV networks. In this paper, we propose analytical models for IEEE 802.15.4 nonbeacon-enabled mode for IoV by considering two major features, i.e., non-saturated traffic pattern and large-scale network, in IoV applications. We investigate the performance of IEEE 802.15.4 nonbeacon-enabled mode for IoV. Our study can provide guidelines for vehicles to dynamically adjust the broadcasting rate and achieve a higher probability of success in such communications.

Performance Evaluation of IEEE 802.15.4 Nonbeacon-Enabled Mode for Internet of Vehicles

Federated Learning (FL) is a recent distributed technique to extract knowledge, i.e. an abstract understanding obtained from a set of information through experience and analysis. Vehicular networks are highly mobile networks in which a large spectrum of data types is distributed. So far, no mechanisms have been defined that distribute FL model updates in vehicular networks based on which nodes are likely to hold the right data for training, and when. In turn, this potentially limits FL model training speed and accuracy. In this paper, we describe protocols to exchange model-based training requirements based on the Vehicular Knowledge Networking framework. Based on this understanding, we define vehicular mobility and data distribution-aware FL orchestration mechanisms. An evaluation of the approach using a federated variant of the MNIST dataset shows training speed and model accuracy improvements compared to traditional FL training approaches.

Chang-Heng Wang

Papers

OFDM Pilot-Based Radar for Joint Vehicular Communication and Radar Systems

End-to-end scheduling for all-optical data centers

Architecture and control plane for data centers

Performance Evaluation of IEEE 802.15.4 Nonbeacon-Enabled Mode for Internet of Vehicles

On the Orchestration of Federated Learning through Vehicular Knowledge Networking