Millimeter Wave
Millimeter wave (mmWave) technology, operating at frequencies between 30 and 300 GHz, is a key enabler for high-bandwidth, low-latency communication and sensing applications. Current research heavily focuses on developing robust and efficient algorithms for mmWave signal processing and system design, employing deep learning models like convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and transformers, often integrated with traditional signal processing techniques. These advancements are driving progress in diverse areas such as vehicular communication, healthcare monitoring, human activity recognition, and security imaging, promising significant improvements in data throughput, sensing accuracy, and system efficiency.
Papers
Position Aided Beam Prediction in the Real World: How Useful GPS Locations Actually Are?
João Morais, Arash Behboodi, Hamed Pezeshki, Ahmed Alkhateeb
Deep Reinforcement Learning Based on Location-Aware Imitation Environment for RIS-Aided mmWave MIMO Systems
Wangyang Xu, Jiancheng An, Chongwen Huang, Lu Gan, Chau Yuen