2,182 research outputs found
Spatial Covariance Estimation for Millimeter Wave Hybrid Systems using Out-of-Band Information
In high mobility applications of millimeter wave (mmWave) communications,
e.g., vehicle-to-everything communication and next-generation cellular
communication, frequent link configuration can be a source of significant
overhead. We use the sub-6 GHz channel covariance as an out-of-band side
information for mmWave link configuration. Assuming: (i) a fully digital
architecture at sub-6 GHz; and (ii) a hybrid analog-digital architecture at
mmWave, we propose an out-of-band covariance translation approach and an
out-of-band aided compressed covariance estimation approach. For covariance
translation, we estimate the parameters of sub-6 GHz covariance and use them in
theoretical expressions of covariance matrices to predict the mmWave
covariance. For out-of-band aided covariance estimation, we use weighted sparse
signal recovery to incorporate out-of-band information in compressed covariance
estimation. The out-of-band covariance translation eliminates the in-band
training completely, whereas out-of-band aided covariance estimation relies on
in-band as well as out-of-band training. We also analyze the loss in the
signal-to-noise ratio due to an imperfect estimate of the covariance. The
simulation results show that the proposed covariance estimation strategies can
reduce the training overhead compared to the in-band only covariance
estimation.Comment: arXiv admin note: text overlap with arXiv:1702.0857
Frequency-domain Compressive Channel Estimation for Frequency-Selective Hybrid mmWave MIMO Systems
Channel estimation is useful in millimeter wave (mmWave) MIMO communication
systems. Channel state information allows optimized designs of precoders and
combiners under different metrics such as mutual information or
signal-to-interference-noise (SINR) ratio. At mmWave, MIMO precoders and
combiners are usually hybrid, since this architecture provides a means to
trade-off power consumption and achievable rate. Channel estimation is
challenging when using these architectures, however, since there is no direct
access to the outputs of the different antenna elements in the array. The MIMO
channel can only be observed through the analog combining network, which acts
as a compression stage of the received signal. Most of prior work on channel
estimation for hybrid architectures assumes a frequency-flat mmWave channel
model. In this paper, we consider a frequency-selective mmWave channel and
propose compressed-sensing-based strategies to estimate the channel in the
frequency domain. We evaluate different algorithms and compute their complexity
to expose trade-offs in complexity-overhead-performance as compared to those of
previous approaches
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
Hybrid Beamforming with Selection for Multi-user Massive MIMO Systems
This work studies a variant of hybrid beamforming, namely, hybrid beamforming
with selection (HBwS), as an attractive solution to reduce the hardware cost of
multi-user Massive Multiple-Input-Multiple-Output systems, while retaining good
performance. Unlike conventional hybrid beamforming, in a transceiver with
HBwS, the antenna array is fed by an analog beamforming matrix with
input ports, where is larger than the number of up/down-conversion
chains . A bank of switches connects the instantaneously best
out of the input ports to the up/down-conversion chains.
The analog beamformer is designed based on average channel statistics and
therefore needs to be updated only infrequently, while the switches operate
based on instantaneous channel knowledge. HBwS allows use of simpler hardware
in the beamformer that only need to adjust to the statistics, while also
enabling the effective analog beams to adapt to the instantaneous channel
variations via switching. This provides better user separability, beamforming
gain, and/or simpler hardware than some conventional hybrid schemes. In this
work, a novel design for the analog beamformer is derived and approaches to
reduce the hardware and computational cost of a multi-user HBwS system are
explored. In addition, we study how , the switch bank architecture,
the number of users and the channel estimation overhead impact system
performance.Comment: Accepted to Transactions on Signal Processin
Millimeter Wave communication with out-of-band information
Configuring the antenna arrays is the main source of overhead in millimeter
wave (mmWave) communication systems. In high mobility scenarios, the problem is
exacerbated, as achieving the highest rates requires frequent link
reconfiguration. One solution is to exploit spatial congruence between signals
at different frequency bands and extract mmWave channel parameters from side
information obtained in another band. In this paper we propose the concept of
out-of-band information aided mmWave communication. We analyze different
strategies to leverage information derived from sensors or from other
communication systems operating at sub-6 GHz bands to help configure the mmWave
communication link. The overhead reductions that can be obtained when
exploiting out-of-band information are characterized in a preliminary study.
Finally, the challenges associated with using out-of-band signals as a source
of side information at mmWave are analyzed in detail.Comment: 14 pages, 6 figure
Channel Tracking and Hybrid Precoding for Wideband Hybrid Millimeter Wave MIMO Systems
A major source of difficulty when operating with large arrays at mmWave
frequencies is to estimate the wideband channel, since the use of hybrid
architectures acts as a compression stage for the received signal. Moreover,
the channel has to be tracked and the antenna arrays regularly reconfigured to
obtain appropriate beamforming gains when a mobile setting is considered. In
this paper, we focus on the problem of channel tracking for frequency-selective
mmWave channels, and propose two novel channel tracking algorithms that
leverage prior statistical information on the angles-of-arrival and
angles-of-departure. Exploiting this prior information, we also propose a
precoding and combining design method to increase the received SNR during
channel tracking, such that near-optimum data rates can be obtained with
low-overhead. In our numerical results, we analyze the performance of our
proposed algorithms for different system parameters. Simulation results show
that, using channel realizations extracted from the 5G New Radio channel model,
our proposed channel tracking framework is able to achieve near-optimum data
rates
Passive Radar at the Roadside Unit to Configure Millimeter Wave Vehicle-to-Infrastructure Links
Millimeter wave (mmWave) vehicular channels are highly dynamic, and the
communication link needs to be reconfigured frequently. In this work, we
propose to use a passive radar receiver at the roadside unit to reduce the
training overhead of establishing an mmWave communication link. Specifically,
the passive radar will tap the transmissions from the automotive radars of the
vehicles on the road. The spatial covariance of the received radar signals will
be estimated and used to establish the communication link. We propose a
simplified radar receiver that does not require the transmitted waveform as a
reference. To leverage the radar information for beamforming, the radar azimuth
power spectrum (APS) and the communication APS should be similar. We outline a
radar covariance correction strategy to increase the similarity between the
radar and communication APS. We also propose a metric to compare the similarity
of the radar and communication APS that has a connection with the achievable
rate. We present simulation results based on ray-tracing data. The results show
that: (i) covariance correction improves the similarity of radar and
communication APS, and (ii) the radar-assisted strategy significantly reduces
the training overhead, being particularly useful in non-line-of-sight
scenarios
Framework of Channel Estimation for Hybrid Analog-and-Digital Processing Enabled Massive MIMO Communications
We investigate a general channel estimation problem in the massive
multiple-input multiple-output (MIMO) system which employs the hybrid
analog/digital precoding structure with limited radio-frequency (RF) chains. By
properly designing RF combiners and performing multiple trainings, the proposed
channel estimation can approach the performance of fully-digital estimations
depending on the degree of channel spatial correlation and the number of RF
chains. Dealing with the hybrid channel estimation, the optimal combiner is
theoretically derived by relaxing the constant-magnitude constraint in a
specific single-training scenario, which is then extended to the design of
combiners for multiple trainings by Sequential and Alternating methods.
Further, we develop a technique to generate the phase-only RF combiners based
on the corresponding unconstrained ones to satisfy the constant-magnitude
constraints. The performance of the proposed hybrid channel estimation scheme
is examined by simulations under both nonparametric and spatial channel models.
The simulation results demonstrate that the estimated CSI can approach the
performance of fully-digital estimations in terms of both mean square error and
spectral efficiency. Moreover, a practical spatial channel covariance
estimation method is proposed and its effectiveness in hybrid channel
estimation is verified by simulations
Energy-Efficient Power and Bandwidth Allocation in an Integrated Sub-6 GHz -- Millimeter Wave System
In mobile millimeter wave (mmWave) systems, energy is a scarce resource due
to the large losses in the channel and high energy usage by analog-to-digital
converters (ADC), which scales with bandwidth. In this paper, we consider a
communication architecture that integrates the sub-6 GHz and mmWave
technologies in 5G cellular systems. In order to mitigate the energy scarcity
in mmWave systems, we investigate the rate-optimal and energy-efficient
physical layer resource allocation jointly across the sub-6 GHz and mmWave
interfaces. First, we formulate an optimization problem in which the objective
is to maximize the achievable sum rate under power constraints at the
transmitter and receiver. Our formulation explicitly takes into account the
energy consumption in integrated-circuit components, and assigns the optimal
power and bandwidth across the interfaces. We consider the settings with no
channel state information and partial channel state information at the
transmitter and under high and low SNR scenarios. Second, we investigate the
energy efficiency (EE) defined as the ratio between the amount of data
transmitted and the corresponding incurred cost in terms of power. We use
fractional programming and Dinkelbach's algorithm to solve the EE optimization
problem. Our results prove that despite the availability of huge bandwidths at
the mmWave interface, it may be optimal (in terms of achievable sum rate and
energy efficiency) to utilize it partially. Moreover, depending on the sub-6
GHz and mmWave channel conditions and total power budget, it may be optimal to
activate only one of the interfaces.Comment: A shorter version to appear in Asilomar Conference on Signals,
Systems, and Computer
Hybrid Analog-Digital Channel Estimation and Beamforming: Training-Throughput Tradeoff
This paper designs hybrid analog-digital channel estimation and beamforming
techniques for multiuser massive multiple input multiple output (MIMO) systems
with limited number of radio frequency (RF) chains. For these systems, first we
design novel minimum mean square error (MMSE) hybrid analog-digital channel
estimator by considering both perfect and imperfect channel covariance matrix
knowledge cases. Then, we utilize the estimated channels to enable beamforming
for data transmission. When the channel covariance matrices of all user
equipments (UEs) are known perfectly, we show that there is a tradeoff between
the training duration and throughput. Specifically, we exploit that the optimal
training duration that maximizes the throughput depends on the covariance
matrices of all UEs, number of RF chains and channel coherence time (). We
also show that the training time optimization problem can be formulated as a
concave maximization problem {for some system parameter settings} where its
global optimal solution is obtained efficiently using existing tools. In
particular, when the base station equipped with antennas and RF chain
is serving one single antenna UE, symbol periods () and signal
to noise ratio of dB, we have found that the optimal training durations are
and for highly correlated and uncorrelated Rayleigh fading
channel coefficients, respectively. The analytical expressions are validated by
performing numerical and extensive Monte Carlo simulations.Comment: IEEE Transactions on Communication (To appear
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