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Millimeter wave (mmWave) communication has attracted considerable attention as a key technology for the next-generation wireless communications thanks to its exceptional advantages. MmWave leads the way to achieve a high transmission quality with directed narrow beams from source to multiple destinations by adopting different antenna beamforming (BF) techniques, which have a pivotal role in establishing and maintaining robust links. However, realizing such BF gains in practice requires overcoming several challenges, such as severe signal deterioration, hardware constraints, and design complexity. The elevated complexity of configuring mmWave BF vectors encourages researchers to leverage relevant machine learning (ML) techniques for better BF configurations deployment in 5G and beyond. In this article, we summarize mmWave BF strategies employed for future wireless networks. Then, we provide a comprehensive overview of ML techniques plus its applications and promising contributions toward efficient mmWave BF deployment. Furthermore, we discuss mmWave BF’s future research directions and challenges. Finally, we discuss a single and concurrent mmWave BF case study by applying multiarmed bandit to confirm the superiority of ML-based methods over conventional ones. [ABSTRACT FROM AUTHOR]

Facial expressions are an essential form of human nonverbal communication. Recognition of this nonverbal sign may enable developers to understand the feedbacks on smart device functionality and advertising. Existing approaches for facial expression recognition are mainly based on cameras or on-body sensors, which are either sensitive to lighting conditions or cumbersome for users to wear devices on their faces. In this paper, we propose a new facial expression recognition system based on Wi-Fi signals, named WiFace. Our fundamental intuition is that facial muscle movements in different expressions will induce distinctive waveform patterns in the time-series of channel state information (CSI) in Wi-Fi signals. We develop a series of algorithms to process the CSI signals and extract the most representative waveform patterns for facial expression classification. We build a fully-functional prototype of WiFace using commercial off-the-shelf devices, which can recognize six typical facial expressions. We conduct extensive experiments to evaluate the performance of WiFace, and the experimental results show that the average recognition accuracy is 94.80 percent. [ABSTRACT FROM AUTHOR]