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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]

Traffic information is of great importance for urban cities, and accurate prediction of urban traffics has been pursued for many years. Urban traffic prediction aims to exploit sophisticated models to capture hidden traffic characteristics from substantial historical mobility data and then makes use of trained models to predict traffic conditions in the future. Due to the powerful capabilities of representation learning and feature extraction, emerging deep learning becomes a potent alternative for such traffic modeling. In this article, we envision the potential and broard usage of deep learning in predictions of various traffic indicators, for example, traffic speed, traffic flow, and accident risk. In addition, we summarize and analyze some early attempts that have achieved notable performance. By discussing these existing advances, we propose two future research directions to improve the accuracy and efficiency of urban traffic prediction on a large scale. [ABSTRACT FROM AUTHOR]