399 research outputs found
An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems
Communication at millimeter wave (mmWave) frequencies is defining a new era
of wireless communication. The mmWave band offers higher bandwidth
communication channels versus those presently used in commercial wireless
systems. The applications of mmWave are immense: wireless local and personal
area networks in the unlicensed band, 5G cellular systems, not to mention
vehicular area networks, ad hoc networks, and wearables. Signal processing is
critical for enabling the next generation of mmWave communication. Due to the
use of large antenna arrays at the transmitter and receiver, combined with
radio frequency and mixed signal power constraints, new multiple-input
multiple-output (MIMO) communication signal processing techniques are needed.
Because of the wide bandwidths, low complexity transceiver algorithms become
important. There are opportunities to exploit techniques like compressed
sensing for channel estimation and beamforming. This article provides an
overview of signal processing challenges in mmWave wireless systems, with an
emphasis on those faced by using MIMO communication at higher carrier
frequencies.Comment: Submitted to IEEE Journal of Selected Topics in Signal Processin
Joint Channel-Estimation/Decoding with Frequency-Selective Channels and Few-Bit ADCs
We propose a fast and near-optimal approach to joint channel-estimation,
equalization, and decoding of coded single-carrier (SC) transmissions over
frequency-selective channels with few-bit analog-to-digital converters (ADCs).
Our approach leverages parametric bilinear generalized approximate message
passing (PBiGAMP) to reduce the implementation complexity of joint channel
estimation and (soft) symbol decoding to that of a few fast Fourier transforms
(FFTs). Furthermore, it learns and exploits sparsity in the channel impulse
response. Our work is motivated by millimeter-wave systems with bandwidths on
the order of Gsamples/sec, where few-bit ADCs, SC transmissions, and fast
processing all lead to significant reductions in power consumption and
implementation cost. We numerically demonstrate our approach using signals and
channels generated according to the IEEE 802.11ad wireless local area network
(LAN) standard, in the case that the receiver uses analog beamforming and a
single ADC
Wideband Channel Estimation for Hybrid Beamforming Millimeter Wave Communication Systems with Low-Resolution ADCs
A potential tremendous spectrum resource makes millimeter wave (mmWave)
communications a promising technology. High power consumption due to a large
number of antennas and analog-to-digital converters (ADCs) for beamforming to
overcome the large propagation losses is problematic in practice. As a hybrid
beamforming architecture and low-resolution ADCs are considered to reduce power
consumption, estimation of mmWave channels becomes challenging. We evaluate
several channel estimation algorithms for wideband mmWave systems with hybrid
beamforming and low-resolution ADCs. Through simulation, we show that 1)
infinite bit ADCs with least-squares estimation have worse channel estimation
performance than do one-bit ADCs with orthogonal matching pursuit (OMP) in an
SNR range of interest, 2) three- and four-bit quantizers can achieve channel
estimation performance close to the unquantized case when using OMP, 3) a
receiver with a single RF chain can yield better estimates than that with four
RF chains if enough frames are exploited, and 4) for one-bit ADCs, exploitation
of higher transmit power and more frames for performance enhancement adversely
affects estimation performance after a certain point.Comment: 6 pages, 8 figures, submitted to ICC 201
A Hardware-Efficient Hybrid Beamforming Solution for mmWave MIMO Systems
In millimeter wave (mmWave) communication systems, existing hybrid
beamforming solutions generally require a large number of high-resolution phase
shifters (PSs) to realize analog beamformers, which still suffer from high
hardware complexity and power consumption. Targeting at this problem, this
article introduces a novel hardware-efficient hybrid precoding/combining
architecture, which only employs a limited number of simple phase over-samplers
(POSs) and a switch (SW) network to achieve maximum hardware efficiency while
maintaining satisfactory spectral efficiency performance. The POS can be
realized by a simple circuit and simultaneously outputs several parallel
signals with different phases. With the aid of a simple switch network, the
analog precoder/combiner is implemented by feeding the signals with appropriate
phases to antenna arrays or RF chains. We analyze the design challenges of this
POS-SW-based hybrid beamforming architecture and present potential solutions to
the fundamental issues, especially the precoder/combiner design and the channel
estimation strategy. Simulation results demonstrate that this
hardware-efficient structure can achieve comparable spectral efficiency but
much higher energy efficiency than that of the traditional structures
LEMO: Learn to Equalize for MIMO-OFDM Systems with Low-Resolution ADCs
This paper develops a new deep neural network optimized equalization
framework for massive multiple input multiple output orthogonal frequency
division multiplexing (MIMOOFDM) systems that employ low-resolution
analog-to-digital converters (ADCs) at the base station (BS). The use of
lowresolution ADCs could largely reduce hardware complexity and circuit power
consumption, however, it makes the channel station information almost blind to
the BS, hence causing difficulty in solving the equalization problem. In this
paper, we consider a supervised learning architecture, where the goal is to
learn a representative function that can predict the targets (constellation
points) from the inputs (outputs of the low-resolution ADCs) based on the
labeled training data (pilot signals). Especially, our main contributions are
two-fold: 1) First, we design a new activation function, whose outputs are
close to the constellation points when the parameters are finally optimized, to
help us fully exploit the stochastic gradient descent method for the discrete
optimization problem. 2) Second, an unsupervised loss is designed and then
added to the optimization objective, aiming to enhance the representation
ability (so-called generalization). Lastly, various experimental results
confirm the superiority of the proposed equalizer over some existing ones,
particularly when the statistics of the channel state information are unclear
Joint CFO and Channel Estimation in Millimeter Wave Systems with One-Bit ADCs
We develop a method to jointly estimate the carrier frequency offset (CFO)
and the narrowband channel in millimeter wave (mmWave) MIMO systems operating
with one-bit analog-to-digital converters (ADCs). We assume perfect timing
synchronization and transform the underlying CFO-channel optimization problem
to a higher dimensional space using lifting techniques. Exploiting the sparsity
of mmWave MIMO channels in the angle domain, we perform joint estimation by
solving a noisy quantized compressed sensing problem of the lifted version,
using generalized approximate message passing. Simulation results show that our
method is able to recover both the channel and the CFO using one-bit
measurements.Comment: 4 pages, 4 figures, submitted to the 7th IEEE International Workshop
on Computational Advances in Multi-Sensor Adaptive Processin
Wideband mmWave Channel Estimation for Hybrid Massive MIMO with Low-Precision ADCs
In this article, we investigate channel estimation for wideband
millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) under
hybrid architecture with lowprecision analog-to-digital converters (ADCs). To
design channel estimation for the hybrid structure, both analog processing
components and frequency-selective digital combiners need to be optimized. The
proposed channel estimator follows the typical linear-minimum-mean-square-error
(LMMSE) structure and applies for an arbitrary channel model. Moreover, for
sparsity channels as in mmWave, the proposed estimator performs more
efficiently by incorporating orthogonal matching pursuit (OMP) to mitigate
quantization noise caused by low-precision ADCs. Consequently, the proposed
estimator outperforms conventional ones as demonstrated by computer simulation
results
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 Architectures with Few-Bit ADC Receivers: Achievable Rates and Energy-Rate Tradeoffs
Hybrid analog/digital architectures and receivers with low-resolution
analog-to-digital converters (ADCs) are two low power solutions for wireless
systems with large antenna arrays, such as millimeter wave and massive MIMO
systems. Most prior work represents two extreme cases in which either a small
number of RF chains with full-resolution ADCs, or low resolution ADC with a
number of RF chains equal to the number of antennas is assumed. In this paper,
a generalized hybrid architecture with a small number of RF chains and finite
number of ADC bits is proposed. For this architecture, achievable rates with
channel inversion and SVD based transmission methods are derived. Results show
that the achievable rate is comparable to that obtained by full-precision ADC
receivers at low and medium SNRs. A trade-off between the achievable rate and
power consumption for different numbers of bits and RF chains is devised. This
enables us to draw some conclusions on the number of ADC bits needed to
maximize the system energy efficiency. Numerical simulations show that coarse
ADC quantization is optimal under various system configurations. This means
that hybrid combining with coarse quantization achieves better energy-rate
trade-off compared to both hybrid combining with full-resolutions ADCs and
1-bit ADC combining.Comment: 30 pages, 8 figures, submitted to IEEE Transactions on Wireless
Communication
One-Bit OFDM Receivers via Deep Learning
This paper develops novel deep learning-based architectures and design
methodologies for an orthogonal frequency division multiplexing (OFDM) receiver
under the constraint of one-bit complex quantization. Single bit quantization
greatly reduces complexity and power consumption, but makes accurate channel
estimation and data detection difficult. This is particularly true for
multicarrier waveforms, which have high peak-to-average ratio in the time
domain and fragile subcarrier orthogonality in the frequency domain. The severe
distortion for one-bit quantization typically results in an error floor even at
moderately low signal-to-noise-ratio (SNR) such as 5 dB. For channel estimation
(using pilots), we design a novel generative supervised deep neural network
(DNN) that can be trained with a reasonable number of pilots. After channel
estimation, a neural network-based receiver -- specifically, an autoencoder --
jointly learns a precoder and decoder for data symbol detection. Since
quantization prevents end-to-end training, we propose a two-step sequential
training policy for this model. With synthetic data, our deep learning-based
channel estimation can outperform least squares (LS) channel estimation for
unquantized (full-resolution) OFDM at average SNRs up to 14 dB. For data
detection, our proposed design achieves lower bit error rate (BER) in fading
than unquantized OFDM at average SNRs up to 10 dB
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