TY - GEN
T1 - Power-Efficient Hybrid MIMO Receiver with Task-Specific Beamforming using Low-Resolution ADCs
AU - Zirtiloglu, Timur
AU - Shlezinger, Nir
AU - Eldar, Yonina C.
AU - Yazicigil, Rabia Tugce
N1 - Publisher Copyright:
© 2022 IEEE
PY - 2022/1/1
Y1 - 2022/1/1
N2 - Multiple-input multiple-output (MIMO) systems utilize multiple antennas and signal acquisition chains, facilitating multiuser communications with increased spectral efficiency and better coverage via beamforming. MIMO systems are typically costly to implement and consume high power. A commonly used method to reduce the cost of MIMO receivers is to design hybrid analog/digital beamforming (HBF), which reduces the number of RF chains. However, the added analog circuitry involves active components whose consumed power may surpass that saved in RF chain reduction. An additional method to realize power-efficient MIMO systems is to use low-resolution analog-to-digital converters (ADCs), however, compromising signal recovery accuracy. In this work, we propose a power-efficient hybrid MIMO receiver with dedicated beamforming to mitigate spatial interferers in congested environments, utilizing low-quantization rate ADCs, jointly optimizing the analog and digital processing using task-specific quantization techniques. We present an efficient analog pre-processing hardware architecture utilizing sparse low-resolution vector modulators to reduce analog processing power while maintaining recovery accuracy. Supported by numerical simulations and power analysis, our power-efficient MIMO receiver achieves comparable signal recovery performance to power-hungry fully-digital MIMO receivers using high-resolution ADCs. Furthermore, our receiver outperforms the task-agnostic HBF receivers with low-quantization rate ADCs in recovery accuracy at lower power.
AB - Multiple-input multiple-output (MIMO) systems utilize multiple antennas and signal acquisition chains, facilitating multiuser communications with increased spectral efficiency and better coverage via beamforming. MIMO systems are typically costly to implement and consume high power. A commonly used method to reduce the cost of MIMO receivers is to design hybrid analog/digital beamforming (HBF), which reduces the number of RF chains. However, the added analog circuitry involves active components whose consumed power may surpass that saved in RF chain reduction. An additional method to realize power-efficient MIMO systems is to use low-resolution analog-to-digital converters (ADCs), however, compromising signal recovery accuracy. In this work, we propose a power-efficient hybrid MIMO receiver with dedicated beamforming to mitigate spatial interferers in congested environments, utilizing low-quantization rate ADCs, jointly optimizing the analog and digital processing using task-specific quantization techniques. We present an efficient analog pre-processing hardware architecture utilizing sparse low-resolution vector modulators to reduce analog processing power while maintaining recovery accuracy. Supported by numerical simulations and power analysis, our power-efficient MIMO receiver achieves comparable signal recovery performance to power-hungry fully-digital MIMO receivers using high-resolution ADCs. Furthermore, our receiver outperforms the task-agnostic HBF receivers with low-quantization rate ADCs in recovery accuracy at lower power.
KW - Analog-to-digital conversion
KW - MIMO
KW - beamforming
KW - hybrid architecture
KW - quantization
UR - http://www.scopus.com/inward/record.url?scp=85131242305&partnerID=8YFLogxK
U2 - 10.1109/ICASSP43922.2022.9746362
DO - 10.1109/ICASSP43922.2022.9746362
M3 - Conference contribution
AN - SCOPUS:85131242305
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 5338
EP - 5342
BT - 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers
T2 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022
Y2 - 22 May 2022 through 27 May 2022
ER -