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
Efficient CNN-based Super Resolution Algorithms for mmWave Mobile Radar Imaging
Christos Vasileiou, Josiah W. Smith, Shiva Thiagarajan, Matthew Nigh, Yiorgos Makris, Murat Torlak
Efficient 3-D Near-Field MIMO-SAR Imaging for Irregular Scanning Geometries
Josiah Smith, Murat Torlak
Improved Static Hand Gesture Classification on Deep Convolutional Neural Networks using Novel Sterile Training Technique
Josiah Smith, Shiva Thiagarajan, Richard Willis, Yiorgos Makris, Murat Torlak