TY - GEN
T1 - Dynamic Metasurface Antennas for Bit-Constrained MIMO-OFDM Receivers
AU - Wang, Hanqing
AU - Shlezinger, Nir
AU - Jin, Shi
AU - Eldar, Yonina C.
AU - Yoo, Insang
AU - Imani, Mohammadreza F.
AU - Smith, David R.
N1 - Funding Information:
This project has received funding from the Air Force Office of Scientific Research under grants No. FA9550-18-1-0187 and FA9550-18-1-0208. H. Wang and S. Jin are with the National Mobile Communications Research Lab, Southeast University, Nanjing, P. R. China (e-mail: {hqwanglyt; jin-shi}@seu.edu.cn). N. Shlezinger and Y. C. Eldar are with the Faculty of Math and CS, Weizmann Institute of Science, Rehovot, Israel (e-mail: {nir.shlezinger; yonina}@weizmann.ac.il). I. Yoo, M. F. Imani and D. R. Smith are with the Department of ECE, Duke University, Durham, NC (e-mail: insang.yoo@duke.edu, mohamad.imani@gmail.com; drsmith@duke.edu).
Publisher Copyright:
© 2020 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - The combination of orthogonal frequency modulation (OFDM) and multiple-input multiple-output (MIMO) systems plays an important role in modern communication systems. In order to meet the growing throughput demands, future MIMO-OFDM receivers are expected to utilize a massive number of antennas, operate in dynamic environments, and explore high frequency bands, while satisfying strict constraints in terms of cost, power, and size. An emerging technology to realize massive MIMO receivers of reduced cost and power consumption is based on dynamic metasurface antennas (DMAs), which inherently implement controllable compression in acquisition. In this work we study the application of DMAs for MIMO-OFDM receivers operating with bit-constrained analog-to-digital converters (ADCs). We exploit previous results in task-based quantization to show how DMAs can be configured to improve recovery in the presence of constrained ADCs, and propose an algorithm for adjusting the DMA parameters based on channel state information. Our numerical results demonstrate that the DMA-based receiver is capable of accurately recovering OFDM signals, and that its performance is comparable to receivers operating without bit limitations, while being significantly less costly and more power efficient.
AB - The combination of orthogonal frequency modulation (OFDM) and multiple-input multiple-output (MIMO) systems plays an important role in modern communication systems. In order to meet the growing throughput demands, future MIMO-OFDM receivers are expected to utilize a massive number of antennas, operate in dynamic environments, and explore high frequency bands, while satisfying strict constraints in terms of cost, power, and size. An emerging technology to realize massive MIMO receivers of reduced cost and power consumption is based on dynamic metasurface antennas (DMAs), which inherently implement controllable compression in acquisition. In this work we study the application of DMAs for MIMO-OFDM receivers operating with bit-constrained analog-to-digital converters (ADCs). We exploit previous results in task-based quantization to show how DMAs can be configured to improve recovery in the presence of constrained ADCs, and propose an algorithm for adjusting the DMA parameters based on channel state information. Our numerical results demonstrate that the DMA-based receiver is capable of accurately recovering OFDM signals, and that its performance is comparable to receivers operating without bit limitations, while being significantly less costly and more power efficient.
KW - MIMO-OFDM
KW - Metasurface antennas
KW - quantization
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U2 - 10.1109/ICASSP40776.2020.9054184
DO - 10.1109/ICASSP40776.2020.9054184
M3 - Conference contribution
AN - SCOPUS:85089237574
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 9155
EP - 9159
BT - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Y2 - 4 May 2020 through 8 May 2020
ER -