Books in the Wireless Networks series

This book proposes the novel network envisions and framework design principles, in order to systematically expound the next generation vehicular networks, including the modelling, algorithms and practical applications.

It is divided into three main parts: The first part briefly discusses the fundamentals of FL for wireless networks, while the second part comprehensively examines the design and analysis of wireless FL, covering resource optimization, incentive mechanism, security and privacy.

This book provides a timely and comprehensive study of developing efficient network slicing frameworks in both 5G wireless and core networks.

This book covers connectivity and edge computing solutions for representative Internet of Things (IoT) use cases, including industrial IoT, rural IoT, Internet of Vehicles (IoV), and mobile virtual reality (VR).

Therefore, the emergence of edge computing has been recently developed as a new computing paradigm that can collect and process data at the edge of the network, which brings significant convenience to solving problems such as delay, bandwidth, and off-loading in the traditional cloud computing paradigm.

The cellular networks are necessarily committed to support the data transmission for UAVs, which the authors refer to as Cellular assisted UAV Sensing.

This book provides a timely and comprehensive study of dynamic resource management for network slicing in service-oriented fifth-generation (5G) and beyond core networks. This includes the perspective of developing efficient computation resource provisioning and scheduling solutions to guarantee consistent service performance in terms of end-to-end (E2E) data delivery delay. Network slicing is enabled by the software defined networking (SDN) and network function virtualization (NFV) paradigms. For a network slice with a target traffic load, the E2E service delivery is enabled by virtual network function (VNF) placement and traffic routing with static resource allocations. When data traffic enters the network, the traffic load is dynamic and can deviate from the target value, potentially leading to QoS performance degradation and network congestion. Data traffic has dynamics in different time granularities. For example, the traffic statistics (e.g., mean and variance) can be non-stationary and experience significant changes in a coarse time granularity, which are usually predictable. Within a long time duration with stationary traffic statistics, there are traffic dynamics in small timescales, which are usually highly bursty and unpredictable. To provide continuous QoS performance guarantee and ensure efficient and fair operation of the network slices over time, it is essential to develop dynamic resource management schemes for the embedded services in the presence of traffic dynamics during virtual network operation. Queueing theory is used in system modeling, and different techniques including optimization and machine learning are applied to solving the dynamic resource management problems.Based on a simplified M/M/1 queueing model with Poisson traffic arrivals, an optimization model for flow migration is presented to accommodate the large-timescale changes in the average traffic rates with average E2E delay guarantee, while addressing a trade-off between load balancing and flow migration overhead. To overcome the limitations of Poisson traffic model, the authors present a machine learning approach for dynamic VNF resource scaling and migration. The new solution captures the inherent traffic patterns in a real-world traffic trace with non-stationary traffic statistics in large timescale, predicts resource demands for VNF resource scaling, and triggers adaptive VNF migration decision making, to achieve load balancing, migration cost reduction, and resource overloading penalty suppression in the long run. Both supervised and unsupervised machine learning tools are investigated for dynamic resource management. To accommodate the traffic dynamics in small time granularities, the authors present a dynamic VNF scheduling scheme to coordinate the scheduling among VNFs of multiple services, which achieves network utility maximization with delay guarantee for each service. Researchers and graduate students working in the areas of electrical engineering, computing engineering and computer science will find this book useful as a reference or secondary text. Professionals in industry seeking solutions to dynamic resource management for 5G and beyond networks will also want to purchase this book.

This book consists of the identification, characterization, and modeling of electromagnetic interferences in substations for the deployment of wireless sensor networks. The authors present in chapter 3 the measurement setup to record sequences of impulsive noise samples in the ISM band of interest. The setup can measure substation impulsive noise, in wide band, with enough samples per time window and enough precision to allow a statistical study of the noise. During the measurement campaign, the authors recorded around 120 noise sequences in different substations and for four ranges of equipment voltage, which are 25 kV, 230 kV, 315 kV and 735 kV. A characterization process is proposed, by which physical characteristics of partial discharge can be measured in terms of first- and second-order statistics. From the measurement campaign, the authors infer the characteristics of substation impulsive noise as a function of the substation equipment voltage, and can provide representative parameters for the four voltage ranges and for several existing impulsive noise models.The authors investigate in chapters 4 and 5 the modeling of electromagnetic interferences caused by partial discharge sources. First, the authors propose a complete and coherent approach model that links physical characteristics of high-voltage installations to the induced radio-interference spectra of partial discharge sources. The goodness-of-fit of the proposed physical model has been measured based on some interesting statistical metrics. This allows one to assess the effectiveness of the authors' approach in terms of first- and second-order statistics. Chapter 6 proposes a model based on statistical approach. Indeed, substation impulsive noise is composed of correlated impulses, which would require models with memory in order to replicate a similar correlation. Among different models, we have configured a Partitioned Markov Chain (PMC) with 19 states (one state for the background noise and 18 states for the impulse); this Markov-Gaussian model is able to generate impulsive noise with correlated impulse samples. The correlation is observable on the impulse duration and the power spectrum of the impulses. Our PMC model provides characteristics that are more similar to the characteristics of substation impulsive noise in comparison with other models, in terms of time and frequency response, as well as Probability Density Functions (PDF). Although PMC represents reliably substation impulsive noise, the model remains complex in terms of parameter estimation due to a large number of Markov states, which can be an obstacle for future wireless system design. In order to simplify the model, the authors decrease the number of states to 7 by assigning one state to the background noise and 6 states to the impulse and we call this model PMC-6. PMC-6 can generate realistic impulses and can be easily implemented in a receiver in order to mitigate substation impulsive noise. Representative parameters are provided in order to replicate substation impulsive noise for different voltage ranges (25-735 kV). Chapter 7, a generalized radio-noise model for substations is proposed, in which there are many discharges sources that are randomly distributed over space and time according to the Poisson field of interferers approach. This allows for the identification of some interesting statistical properties of moments, cumulants and probability distributions. These can, in turn, be utilized in signal processing algorithms for rapid partial discharge's identification, localization, and impulsive noise mitigation techniques in wireless communications in substations.The primary audience for this book is the electrical and power engineering industry, electricity providers and companies who are interested in substation automation systems using wireless communication technologies for smart grid applicati

The authors rely on estimation theory and signal processing to develop SI-cancellation algorithms by generating an estimate of the received SI and subtracting it from the received signal.The authors also cover two new SI-cancellation methods using the new concept of active signal injection (ASI) for full-duplex MIMO-OFDM systems.

This book presents the current research on safety message dissemination in vehicular networks, covering medium access control and relay selection for multi-hop safety message broadcast.

This book mainly discusses the most important issues in artificial intelligence-aided future networks, such as applying different ML approaches to investigate solutions to intelligently monitor, control and optimize networking.

This book addresses the fundamental design and technical challenges for fifth generation (5G) wireless channel models, including multi-frequency bands and multi-scenarios. The book presents a strong vision for 5G wireless communication networks based on current market trends, proven technologies, and future directions. The book helps enable researchers and industry professionals to come up with novel ideas in the area of wireless heterogeneity, to minimize traffic accidents, to improve traffic efficiency, and to foster the development of new applications such as mobile infotainment. The book acts as a comprehensive reference for students, instructors, researchers, engineers, and other professionals, building their understanding of 5G and in designing 5G systems.Addresses fundamental design and technical challenges for 5G wireless channel models;Presents how to create reliable statistical channel models to capture the propagation properties between transmitters and receivers;Pertinent to researchers, engineers, and professionals in 5G.

This book provides a comprehensive picture of mobile big data starting from data sources to mobile data driven applications.

This book provides a comprehensive picture of mobile big data starting from data sources to mobile data driven applications.

This book serves as a useful reference for researchers, graduate students, and practitioners seeking solutions to wireless security and privacy related issues Recent advances in wireless communication technologies have enabled the large-scale deployment of next-generation wireless networks, and many other wireless applications are emerging.

The coexistence and cooperation between terrestrial and satellite networks are of great potential in future communication networks, and satellite radio access networks has already been considered in the fifth-generation (5G) networks to be supported for phase 2.