Jingjing Wang

In an effort to overcome the spectral efficiency loss and high latency by half-duplex (HD), full-duplex (FD) has attracted extensive attention in industry and academia. With signal transmitted and received simultaneously over the same frequency, FD can approximately double the SE over HD. More than that, by enabling the capability of simultaneous transmission-and-reception for communication nodes, full duplex (FD) is a key enabler for many 5G techniques, including but not limited to integrated sensing and communication, low-latency relaying, concurrent bi-directional uplink and downlink transmission, ect. Nevertheless, in FD communications, both transmit and receive radio frequency (RF) chains are activated for exchanging data, and additional power is triggered by self-interference cancellation. As FD requires much higher power consumption than that of half duplex (HD), it is against the green evolution requirement proposed by the future communication systems. To address the critical high-power challenge in applying FD communications, this book will introduce the fundamentals and algorithm designs for energy-efficient FD design. This book will first discuss the principle of energy-efficient communications, which aims to make a good balance between communication performance and system energy consumption. Then, this book will discuss different self-interference cancellation schemes, including passive suppression, analogue cancellation and digital cancellation for FD communications, from the prospective of energy efficiency. Subsequently, this book will present some edge-cutting energy efficiency-oriented FD solutions, including adaptive transmission power adaptation, wireless power transfer FD relaying, bi-directional FD distributed antenna systems, from the perspective of algorithm design and performance analysis.

Relying on unmanned autonomous flight control programs, unmanned aerial vehicles (UAVs) equipped with radio communication devices have been actively developed around the world. Given their low cost, flexible maneuvering and unmanned operation, UAVs have been widely used in both civilian operations and military missions, including environmental monitoring, emergency communications, express distribution, even military surveillance and attacks, for example. Given that a range of standards and protocols used in terrestrial wireless networks are not applicable to UAV networks, and that some practical constraints such as battery power and no-fly zone hinder the maneuverability capability of a single UAV, we need to explore advanced communication and networking theories and methods for the sake of supporting future ultra-reliable and low-latency applications. Typically, the full potential of UAV network’s functionalities can be tapped with the aid of the cooperation of multiple drones relying on their ad hoc networking, in-network communications and coordinated control. Furthermore, some swarm intelligence models and algorithms conceived for dynamic negotiation, path programming, formation flight and task assignment of multiple cooperative drones are also beneficial in terms of extending UAV’s functionalities and coverage, as well as of increasing their efficiency. We call the networking and cooperation of multiple drones as the terminology ‘flying ad hoc network (FANET)’, and there indeed are numerous new challenges to be overcome before the idespread of so-called heterogeneous FANETs. In this book, we examine a range of technical issues in FANETs, from physical-layer channel modeling to MAC-layer resource allocation, while also introducing readers to UAV aided mobile edge computing techniques.

Relying on unmanned autonomous flight control programs, unmanned aerial vehicles (UAVs) equipped with radio communication devices have been actively developed around the world. Given their low cost, flexible maneuvering and unmanned operation, UAVs have been widely used in both civilian operations and military missions, including environmental monitoring, emergency communications, express distribution, even military surveillance and attacks, for example. Given that a range of standards and protocols used in terrestrial wireless networks are not applicable to UAV networks, and that some practical constraints such as battery power and no-fly zone hinder the maneuverability capability of a single UAV, we need to explore advanced communication and networking theories and methods for the sake of supporting future ultra-reliable and low-latency applications. Typically, the full potential of UAV network’s functionalities can be tapped with the aid of the cooperation of multiple drones relying on their ad hoc networking, in-network communications and coordinated control. Furthermore, some swarm intelligence models and algorithms conceived for dynamic negotiation, path programming, formation flight and task assignment of multiple cooperative drones are also beneficial in terms of extending UAV’s functionalities and coverage, as well as of increasing their efficiency. We call the networking and cooperation of multiple drones as the terminology ‘flying ad hoc network (FANET)’, and there indeed are numerous new challenges to be overcome before the idespread of so-called heterogeneous FANETs. In this book, we examine a range of technical issues in FANETs, from physical-layer channel modeling to MAC-layer resource allocation, while also introducing readers to UAV aided mobile edge computing techniques.

Comprehensive reference covering signal detection for random access in IoT systems from the beginner to expert level With a carefully balanced blend of theoretical elements and applications, IoT Signal Detection is an easy-to-follow presentation on signal detection for IoT in terms of device activity detection, sparse signal detection, collided signal detection, round-trip delay estimation, and backscatter signal division, building progressively from basic concepts and important background material up to an advanced understanding of the subject. Various signal detection and estimation techniques are explained, e.g., variational inference algorithm and compressive sensing reconstruction algorithm, and a number of recent research outcomes are included to provide a review of the state of the art in the field. Written by four highly qualified academics, IoT Signal Detection discusses sample topics such as: ML, ZF, and MMSE detection, Markov chain Monte Carlo-based detection, variational inference-based detection, compressive sensing-based detectionSparse signal detection for multiple access, covering Bayesian compressive sensing algorithm and structured subspace pursuit algorithmCollided signal detection for multiple access using automatic modulation classification algorithm, round-trip delay estimation for collided signalsSignal detection for backscatter signals, covering central limited theorem-based detection including detection algorithms, performance analysis, and simulation resultsSignal design for multi-cluster coordination, covering successive interference cancellation design, device grouping and power control, and constructive interference-aided multi-cluster coordination With seamless coverage of the subject presented in a linear and easy-to-understand way, IoT Signal Detection is an ideal reference for both graduate students and practicing engineers in wireless communications.

It is generally understood that some effective leadership behaviors of Chinese managers differ from those of Western managers. It has also been debated controversially whether Chinese learners can benefit from Western learning approaches. Taking these two aspects into consideration, Jingjing Wang examines whether a global leadership development program from Western countries has as much impact on Chinese managers as on Western managers. She conducts the empirical study within one global corporation originating from Germany and the data were collected from Germany and China. Based on the core results of the study, implications for the globalization of leadership development are discussed.