Measurement-Based Prediction of mmWave Channel Parameters Using Deep Learning and Point Cloud by Hang Mi, Bo Ai, Ruisi He, Anuraag Bodi, Raied Caromi, Jian Wang, Jelena Senic, Camillo Gentile, Yang Miao

“…The parameters predicted include path loss, root-mean-square (RMS) delay spread, angular spread and Rician <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> factor. First, we introduce a novel measurement campaign for indoor mmWave channel at 60 GHz, where a light detection and ranging (LiDAR) sensor and panoramic camera were co-located with a channel sounder and then time-synchronized point clouds and images were captured to describe environmental information. …”
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An Ultra-Low-Power 2.4 GHz All-Digital Phase-Locked Loop With Injection-Locked Frequency Multiplier and Continuous Frequency Tracking by Muhammad Riaz Ur Rehman, Arash Hejazi, Imran Ali, Muhammad Asif, Seongjin Oh, Pervesh Kumar, Younggun Pu, Sang-Sun Yoo, Keum Cheol Hwang, Youngoo Yang, Yeonjae Jung, Hyungki Huh, Seokkee Kim, Joon-Mo Yoo, Kang-Yoon Lee

“…Inside the designed LC-DCO core, the power consumption is minimized by optimizing the <inline-formula> <tex-math notation="LaTeX">$\text{g}_{\mathrm {m}}~/\text{I}_{\mathrm {D}}$ </tex-math></inline-formula> and adjusting the power supply to 0.5 V. …”
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