Objectives

Our current focus is on three major areas: (1) identifying and addressing system-level challenges in realizing scalable and power-efficient multiuser massive MIMO at 140+ GHz; (2) design of robust line-of-sight THz MIMO using cost-effective front ends; (3) distributed sensing algorithms for high-resolution THz radar.

Approach

Our team employs signal processing and machine learning based approaches to identify and solve problems in communication and sensing. For mmWave and THz cellular communication, we are exploring all-digital architectures to take advantage of the massive available bandwidth and number of antennas.  We are developing analytical models for hardware impairments such as nonlinearities in the RF chain, low-precision ADC precision, phase noise, in order to quantify the impact of these impairments on system-level performance metrics such as capacity and QoS.  For wireless backhaul, we are exploring the design and impact of low-precision ADC for line-of-sight MIMO. For distributed sensing using mmWave and THz radar, we are developing low-complexity association algorithms for multi-target localization by exploiting geometric structure. We leverage our extensive experience with compressive and sparse estimation techniques for problems in THz imaging.

The team collaborates especially with four teams - Rodwell, Rangan, Cabric, and Studer – on channel modeling, channel estimation, and MIMO processing approaches. We aim to resolve front-end impairments and provide design prescriptions for hardware designers in cooperation with Rodwell’s team. In addition, we will develop demonstration vehicles for our techniques with Arbabian’s team and design robust waveforms for interference rejection between platforms.

Accomplishments

In the context of massive MIMO, we have shown that

(a) specifications for analog and mixed signal hardware front-ends can be significantly relaxed compared to conventional wisdom: increasing the number of antennas allows relaxing linearity requirements on RF front ends and reducing the precision of ADCs,

(b) large arrays can be realized effectively by tiling smaller arrays and distributing a common low-frequency oscillator, without stringent restrictions on phase noise specifications,

(c) preprocessing with spatial FFTs has significant potential for digital backends performing multiuser detection on a massive scale.

We have also obtained analytical insights on quantization for all-digital LoS MIMO, and low-complexity geometry-based target association techniques using distributed sensing.

Team Leader

Upamanyu Madhow

Upamanyu Madhow received the bachelor’s degree in electrical engineering from  IIT Kanpur in 1985 and the Ph.D. degree in electrical engineering from the University of Illinois at Urbana– Champaign, Champaign, IL, USA, in  1990.  He was a Research Scientist with Bell Communications Research, Morristown, NJ, USA. He was a Faculty Member with the University of Illinois at Urbana– Champaign. He is currently a Professor of electrical and computer engineering with the University of California at Santa Barbara, Santa Barbara, CA, USA. He has authored two textbooks, Fundamentals of Digital Communication (Cambridge University Press, 2008) and Introduction to Communication Systems (Cambridge University Press, 2014). His current research interests focus on next-generation communication, sensing and inference infrastructures centered around millimeter-wave systems, and on robust machine learning. He was a recipient of the 1996 NSF CAREER Award and a co-recipient of the 2012 IEEE Marconi Prize Paper Award in Wireless Communications. He served as an Associate Editor for the IEEE Transactions on Communications, the IEEE Transactions on Information Theory, and the IEEE Transactions on Information Forensics and Security.

Publications

Publications

M. E. Rasekh & U. Madhow, “Scaling Massive MIMO Radar via Compressive Signal Processing,” presented at the 2021 55th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, Oct 31- Nov 3, 2021.

B. Domae, R. Li, & D. Cabric, “Machine Learning Assisted Phase-less Millimeter- Wave Beam Alignment in Multipath Channels,” presented at the 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, December 7-11, 2021.

V. Boljanovic & D. Cabric, “Compressive Estimation of Wideband mmW Channel using Analog True-Time-Delay Array,” presented at the 2021 IEEE Workshop on Signal Processing Systems (SiPS), Coimbra, Portugal, October 19-21, 2021.

C-C. Lin, C. Puglisi, V. Boljanovic, S. Mohapatra, H. Yan, E. Ghaderi, D. Heo, D. Cabric, & S. Gupta, “A 4-Element 800MHz-BW 29mW True-Time-Delay Spatial Signal Processor Enabling Fast Beam-Training with Data Communications,” presented at the 2021 IEEE 47th European Solid State Circuits Conference (ESSCIRC), Grenoble, France, September 13-22, 2021.

M. Abdelghany, A. Farid, M. E. Rasekh, U. Madhow, & M. Rodwell (2021, April 5). A design framework for all-digital mmWave massive MIMO with per-antenna nonlinearitites. IEEE Xplore [Online]. Available: https://ieeexplore.ieee.org/document/9395372.

A. Farid, M. A. Abdelghany, U. Madhow, & M. Rodwell, “Dynamic Range Requirements of Digital vs. RF and Tiled Beamforming in mm-Wave Massive MIMO,” presented at the 2021 IEEE Radio and Wireless Symposium (RWS), San Diego, CA, January 17-22, 2021.

V. Boljanovic, H. Yan, C. Lin, S. Mohapatra, D. Heo, S. Gupta, & D. Cabric (2021, February 5). Fast Beam Training with True-Time-Delay Arrays in Wideband Millimeter-Wave Systems. IEEE.org [Online]. Available: https://ieeexplore.ieee.org/document/9349090.

H. Yan, B. Domae, & D. Cabric (2020, July 23). Implementation of Machine Learning assisted Noncoherent Compressive Millimeter-Wave Beam Alignment. arXiv.org [Online]. Available: https://arxiv.org/search/?query=Implementation+of+Machine+Learning+assisted+Noncoherent+Compressive+Millimeter-Wave+Beam+Alignment&searchtype=all&abstracts=show&order=-announced_date_first&size=50.

A. Gupta, U. Madhow, & A. D. Sezer (2020, July 12). Multi-sensor Spatial Association using Joint Range-Doppler Features. arXiv.org [Online]. Available: https://arxiv.org/search/?query=Multi-sensor+Spatial+Association+using+Joint+Range-Doppler+Features&searchtype=all&abstracts=show&order=-announced_date_first&size=50.

M. A. Abdelghany, M. E. Rasekh, & U. Madhow, “Scalable Nonlinear Multiuser Detection for mmWave Massive MIMO,” presented at the 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Atlanta, GA, May 26-29, 2020.

V. Boljanovic, H. Yan, E. Ghaderi, D. Heo, S. Gupta, & D. Cabric, “Design of Millimeter-Wave Single-Shot Beam Training for True-Time-Delay Array,” presented at the 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Atlanta, GA, May 26-29, 2020.

M. A. Abdelghany, U. Madhow, & A. Tolli, “Efficient Beamspace Downlink Precoding for mmWave Massive MIMO,” presented at the 2019 53rd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, November 3-6, 2019.

M. A. Abdelghany, A. Farid, U. Madhow, & M. Rodwell (2019, December 25). A design framework for all-digital mmWave massive MIMO with per-antenna nonlinearities. arXiv.org [Online]. Available: https://arxiv.org/abs/1912.11643.

A. Gupta, U. Madhow, A. Arbabian, & A. Sadri (2019, November 25). Design of Large Effective Apertures for Millimeter Wave Systems using a Sparse Array of Subarrays. IEEE Transactions on Signal Processing [Online]. 67(24). Available: https://ieeexplore.ieee.org/document/8911429.

M. E. Rasekh, U. Madhow, M. A. Abdelghany, & M. Rodwell (2019, October 21). Phase Noise in Modular Millimeter Wave Massive MIMO.  arXiv.org [Online]. Available: https://arxiv.org/abs/1910.09095

M. AbdelghanyU. Madhow, & A. Tölli, “Beamspace Local LMMSE: An Efficient Digital Backend for mmWave Massive MIMO,” presented at the IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Cannes, France, 2019.

M. E. Rasekh, M. Abdelghany, U. Madhow and M. Rodwell, "Phase noise analysis for mmwave massive MIMO: a design framework for scaling via tiled architectures," 2019 53rd Annual Conference on Information Sciences and Systems (CISS), Baltimore, MD, USA, 2019, pp. 1-6

M. Abdelghany, A. A. Farid, U. Madhow and M. J. W. Rodwell, "Towards All-digital mmWave Massive MIMO: Designing around Nonlinearities," 2018 52nd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2018, pp. 1552-1557

M.E. Rasekh and U. Madhow, "Noncoherent Compressive Channel Estimation for Mm-Wave Massive MIMO," presented at TECHCON 2018, Austin, TX, Sept. 16-18, 2018.