Sensor arrays are a key technology with several applications in radar, sonar, microwave imaging, medical ultrasound, and wireless communications, to list a few. Unfortunately, the hardware costs, power consumption, and computational load associated with signal processing for arrays with large antenna elements and dedicated transceiver chains are prohibitively large. These issues are especially pronounced for systems operating at millimeter-wave frequencies that usually have hundreds of elements in an area of only a few square centimeters to compensate for the propagation losses. Together with my collaborators and students, we have been working on signal processing algorithms (e.g., beamforming, channel estimation) for low-cost analog-digital hybrid beamforming and one-bit spatial sigma-delta quantizer architectures.
R. Rajamäki, R., S.P. Chepuri, and V. Koivunen. Hybrid beamforming for active sensing using sparse arrays. IEEE Transactions on Signal Processing, 68, pp.6402-6417, Oct. 2020
R.S. Prasobh and S.P. Chepuri. Millimeter Wave MIMO Channel Estimation with 1-bit Spatial Sigma-delta Analog-to-Digital Converters. In Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, Canada, June 2021.Faculty: Sundeep Prabhakar Chepuri, ECE