Wen Wu

This book thoroughly examines the design of full-duplex medium access control (MAC) protocols in wireless networks. It is organized into three main sections: (a) Fundamentals and preliminaries of full-duplex communications, (b) A comprehensive review of the existing full-duplex MAC protocols, and (c) Designs of full-duplex MAC protocols in three typical wireless networks, i.e., wireless local access networks (WLANs), multi-hop networks and millimeter-wave networks. Also, the authors extensively address key challenges in these three wireless full-duplex networks, such as the hidden-node problem, accumulative interference, and deafness and blockage problems. Solutions to these challenges are meticulously devised to enhance the overall network performance. The wireless full-duplex communication technique, facilitating simultaneous transmission and reception within the same frequency band, holds immense potential for enhancing spectrum efficiency and network capacity. It emerges as a pivotal technology in the sixth-generation (6G) networks. This book provides a comprehensive review and in-depth exploration of full-duplex MAC design across various networks, encompassing WLANs, multi-hop networks, and millimeter-wave networks. Acknowledging the challenges faced by full-duplex WLANs, particularly the hidden-node problem, it also introduces a pioneering hidden-node-free design and a MAC protocol design, which features a full-duplex enhanced carrier-sensing mechanism. Addressing the need to augment end-to-end throughput in multi-hop networks employing full-duplex relaying, the authors present an analytical model for end-to-end throughput and propose a multi-hop full-duplex MAC protocol designed to optimize network performance. Furthermore, the exploration extends to full-duplex millimeter-wave networks, delving into issues of deafness and blockage. A directional full-duplex MAC protocol is introduced to enhance network capacity and mitigate blockage problems. This book concludes by outlining prospective research directions within the related fields of study and offers valuable insights for future exploration. This book targets researchers and advanced level students in computer science and electrical engineering. It also caters to professionals engaged in the fields of wireless networks, full-duplex system, protocol design, and network optimization will also buy this book.

This book provides a comprehensive review and in-depth study on efficient beamforming design and rigorous performance analysis in mmWave networks, covering beam alignment, beamforming training and beamforming-aided caching. Due to significant beam alignment latency between the transmitter and the receiver in existing mmWave systems, this book proposes a machine learning based beam alignment algorithm for mmWave networks to determine the optimal beam pair with a low latency. Then, to analyze and enhance the performance of beamforming training (BFT) protocol in 802.11ad mmWave networks, an analytical model is presented to evaluate the performance of BFT protocol and an enhancement scheme is proposed to improve its performance in high user density scenarios. Furthermore, it investigates the beamforming-aided caching problem in mmWave networks, and proposes a device-to-device assisted cooperative edge caching to alleviate backhaul congestion and reduce content retrieval delay. This book concludes with future research directions in the related fields of study. The presented beamforming designs and the corresponding research results covered in this book, provides valuable insights for practical mmWave network deployment and motivate new ideas for future mmWave networking.This book targets researchers working in the fields of mmWave networks, beamforming design, and resource management as well as graduate students studying the areas of electrical engineering, computing engineering and computer science. Professionals in industry who work in this field will find this book useful as a reference.

This book provides a comprehensive review and in-depth study on efficient beamforming design and rigorous performance analysis in mmWave networks, covering beam alignment, beamforming training and beamforming-aided caching. Due to significant beam alignment latency between the transmitter and the receiver in existing mmWave systems, this book proposes a machine learning based beam alignment algorithm for mmWave networks to determine the optimal beam pair with a low latency. Then, to analyze and enhance the performance of beamforming training (BFT) protocol in 802.11ad mmWave networks, an analytical model is presented to evaluate the performance of BFT protocol and an enhancement scheme is proposed to improve its performance in high user density scenarios. Furthermore, it investigates the beamforming-aided caching problem in mmWave networks, and proposes a device-to-device assisted cooperative edge caching to alleviate backhaul congestion and reduce content retrieval delay. This book concludes with future research directions in the related fields of study. The presented beamforming designs and the corresponding research results covered in this book, provides valuable insights for practical mmWave network deployment and motivate new ideas for future mmWave networking.This book targets researchers working in the fields of mmWave networks, beamforming design, and resource management as well as graduate students studying the areas of electrical engineering, computing engineering and computer science. Professionals in industry who work in this field will find this book useful as a reference.

This book introduces a new type of data poisoning attack, dubbed, backdoor attack. In backdoor attacks, an attacker can train the model with poisoned data to obtain a model that performs well on a normal input but behaves wrongly with crafted triggers. Backdoor attacks can occur in many scenarios where the training process is not entirely controlled, such as using third-party datasets, third-party platforms for training, or directly calling models provided by third parties. Due to the enormous threat that backdoor attacks pose to model supply chain security, they have received widespread attention from academia and industry. This book focuses on exploiting backdoor attacks in the three types of DNN applications, which are image classification, natural language processing, and federated learning.Based on the observation that DNN models are vulnerable to small perturbations, this book demonstrates that steganography and regularization can be adopted to enhance the invisibility of backdoor triggers. Based on image similarity measurement, this book presents two metrics to quantitatively measure the invisibility of backdoor triggers. The invisible trigger design scheme introduced in this book achieves a balance between the invisibility and the effectiveness of backdoor attacks. In the natural language processing domain, it is difficult to design and insert a general backdoor in a manner imperceptible to humans. Any corruption to the textual data (e.g., misspelled words or randomly inserted trigger words/sentences) must retain context-awareness and readability to human inspectors. This book introduces two novel hidden backdoor attacks, targeting three major natural language processing tasks, including toxic comment detection, neural machine translation, and question answering, depending on whether the targeted NLP platform accepts raw Unicode characters.The emerged distributed training framework, i.e., federated learning, has advantages in preserving users' privacy. It has been widely used in electronic medical applications, however, it also faced threats derived from backdoor attacks. This book presents a novel backdoor detection framework in FL-based e-Health systems. We hope this book can provide insightful lights on understanding the backdoor attacks in different types of learning-based algorithms, including computer vision, natural language processing, and federated learning. The systematic principle in this book also offers valuable guidance on the defense of backdoor attacks against future learning-based algorithms.