<p><div> <div>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.&nbsp;</div> <div><br /></div> <div>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.</div> <div><br /></div> <div>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.&nbsp; Professionals in industry who work in this field will find this book useful as a reference.</div> </div> <div><br /></div></p>

Millimeter-Wave Networks: Beamforming Design and Performance Analysis (Wireless Networks)

Computers & Technology

Yang, Peng

Wu, Wen

Shen, Xuemin

章, 凝

Computer Science

Springer

MAC Protocol Design in Full-Duplex Enabled Wireless Networks

Zhuang, Weihua

Fu, Liqun

Liu, Shengbo

Reference

Backdoor Attacks against Learning-Based Algorithms (Wireless Networks)

Shen, Xuemin Sherman

Li, Shaofeng

Zhu, Haojin

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