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.
