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Title: A Comparison of Hybrid Beamforming and Digital Beamforming with Low-Resolution ADCs for Multiple Users and Imperfect CSI
For 5G it will be important to leverage the available millimeter wave spectrum. To achieve an approximately omni- directional coverage with a similar effective antenna aperture compared to state-of-the-art cellular systems, an antenna array is required at both the mobile and basestation. Due to the large bandwidth and inefficient amplifiers available in CMOS for mmWave, the analog front-end of the receiver with a large number of antennas becomes especially power hungry. Two main solutions exist to reduce the power consumption: hybrid beam forming and digital beam forming with low resolution Analog to Digital Converters (ADCs). In this work we compare the spectral and energy efficiency of both systems under practical system constraints. We consider the effects of channel estimation, transmitter impairments and multiple simultaneous users for a wideband multipath model. Our power consumption model considers components reported in literature at 60 GHz. In contrast to many other works we also consider the correlation of the quantization error, and generalize the modeling of it to non- uniform quantizers and different quantizers at each antenna. The result shows that as the Signal to Noise Ratio (SNR) gets larger the ADC resolution achieving the optimal energy efficiency gets also larger. The energy efficiency peaks for 5 bit resolution at high SNR, since due to other limiting factors the achievable rate almost saturates at this resolution. We also show that in the multi- user scenario digital beamforming is in any case more energy efficient than hybrid beamforming. In addition we show that if mixed ADC resolutions are used we can achieve any desired trade-off between power consumption and rate close to those achieved with only one ADC resolution.  more » « less
Award ID(s):
1703635
NSF-PAR ID:
10057525
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
IEEE Journal of Selected Topics in Signal Processing
ISSN:
1932-4553
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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