823 research outputs found
Generative Adversarial Estimation of Channel Covariance in Vehicular Millimeter Wave Systems
Enabling highly-mobile millimeter wave (mmWave) systems is challenging
because of the huge training overhead associated with acquiring the channel
knowledge or designing the narrow beams. Current mmWave beam training and
channel estimation techniques do not normally make use of the prior beam
training or channel estimation observations. Intuitively, though, the channel
matrices are functions of the various elements of the environment. Learning
these functions can dramatically reduce the training overhead needed to obtain
the channel knowledge. In this paper, a novel solution that exploits machine
learning tools, namely conditional generative adversarial networks (GAN), is
developed to learn these functions between the environment and the channel
covariance matrices. More specifically, the proposed machine learning model
treats the covariance matrices as 2D images and learns the mapping function
relating the uplink received pilots, which act as RF signatures of the
environment, and these images. Simulation results show that the developed
strategy efficiently predicts the covariance matrices of the large-dimensional
mmWave channels with negligible training overhead.Comment: to appear in Asilomar Conference on Signals, Systems, and Computers,
Oct. 201
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.
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 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 different ADC resolutions are used we can achieve any desired
trade-offs between power consumption and rate close to those achieved with only
one ADC resolution.Comment: Submitted to JSTSP. arXiv admin note: text overlap with
arXiv:1610.0290
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