H. Yan, and D. Cabric, "Compressive Initial Access and Beamforming Training for Millimeter-Wave Cellular Systems,” in IEEE Journal of Selected Topics in Signal Processing, July 2019
Cognitive Reconfigurable Embedded Systems Lab
Millimeter-wave Signal Processing Algorithm Design
Millimeter-wave (mmW) wireless communication will be a key component in the future cellular networks (5G and beyond). High frequency used for mmW systems create unique characteristics and challenges as compared to conventional communication systems below 6GHz. Due to severe propagation loss of the signal, the mmW wireless communication channels have sparse scattering. Furthermore, array architectures and physical layer procedures are dramatically different than in conventional systems, as mmW systems have an order of magnitude higher carrier frequency, processing bandwidth, and number of antenna elements. In this study, we have two goals:
- We provide an understanding of how unique features in mmW communication (e.g., channel sparsity, large antenna arrays, etc.) affect system performance of important physical layer procedures, including initial access, transceiver synchronization, channel tracking, beamforming, and multiplexing.
- We develop and analyze novel low-complexity digital signal processing algorithms to improve the performance of above physical layer procedures in mmW networks.
- Principal Investigator: Danijela Cabric
- Students: Han Yan, Veljko Boljanovic, Benjamin Domae
V. Boljanovic, H. Yan and D. Cabric, "Tracking Sparse mmWave Channel under Time Varying Multipath Scatterers," in 52nd Asilomar Conference on Signals, Systems, and Computers, Nov. 2018 (Invited Paper)
H. Yan, V. Boljanovic and D. Cabric, "Tracking Sparse mmWave Channel: Performance Analysis Under Intra-Cluster Angular Spread," in IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), July 2018
H. Yan, S. Chaudhari and D. Cabric, "Wideband Channel Tracking for mmWave MIMO System with Hybrid Beamforming Architecture: (Invited Paper)," in IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Dec. 2