631 research outputs found
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
A Robust Time-Domain Beam Alignment Scheme for Multi-User Wideband mmWave Systems
Millimeter wave (mmWave) communication with large array gains is a key
ingredient of next generation (5G) wireless networks. Effective communication
in mmWaves usually depends on the knowledge of the channel. We refer to the
problem of finding a narrow beam pair at the transmitter and at the receiver,
yielding high Signal to Noise Ratio (SNR) as Beam Alignment (BA). Prior BA
schemes typically considered deterministic channels, where the instantaneous
channel coefficients are assumed to stay constant for a long time. In this
paper, in contrast, we propose a time-domain BA scheme for wideband mmWave
systems, where the channel is characterized by multi-path components, different
delays, Angle-of-Arrivals/Angle-of-Departures (AoAs/AoDs), and Doppler shifts.
In our proposed scheme, the Base Station (BS) probes the channel in the
downlink by some sequences with good autocorrelation property (e.g.,
Pseudo-Noise (PN) sequences), letting each user estimate its best AoA-AoD that
connects the user to the BS with two-sided high beamforming gain. We leverage
the sparse nature of mmWaves in the AoA-AoD-time domain, and formulate the BA
problem as a Compressed Sensing (CS) of a non-negative sparse vector. We use
the recently developed Non-Negative Least Squares (NNLS) technique to
efficiently find the strongest path connecting the BS and each user. Simulation
results show that the proposed scheme outperforms its counterpart in terms of
the training overhead and robustness to fast channel variations
Fully-/Partially-Connected Hybrid Beamforming Architectures for mmWave MU-MIMO
Hybrid digital analog (HDA) beamforming has attracted considerable attention
in practical implementation of millimeter wave (mmWave) multiuser
multiple-input multiple-output (MU-MIMO) systems due to the low power
consumption with respect to its fully digital baseband counterpart. The
implementation cost, performance, and power efficiency of HDA beamforming
depends on the level of connectivity and reconfigurability of the analog
beamforming network. In this paper, we investigate the performance of two
typical architectures that can be regarded as extreme cases, namely, the
fully-connected (FC) and the one-stream-per-subarray (OSPS) architectures. In
the FC architecture each RF antenna port is connected to all antenna elements
of the array, while in the OSPS architecture the RF antenna ports are connected
to disjoint subarrays. We jointly consider the initial beam acquisition and
data communication phases, such that the latter takes place by using the beam
direction information obtained by the former. We use the state-of-the-art beam
alignment (BA) scheme previously proposed by the authors and consider a family
of MU-MIMO precoding schemes well adapted to the beam information extracted
from the BA phase. We also evaluate the power efficiency of the two HDA
architectures taking into account the power dissipation at different hardware
components as well as the power backoff under typical power amplifier
constraints. Numerical results show that the two architectures achieve similar
sum spectral efficiency, while the OSPS architecture is advantageous with
respect to the FC case in terms of hardware complexity and power efficiency, at
the sole cost of a slightly longer BA time-to-acquisition due to its reduced
beam angle resolution
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
Compressive Initial Access and Beamforming Training for Millimeter-Wave Cellular Systems
Initial access (IA) is a fundamental physical layer procedure in cellular
systems where user equipment (UE) detects nearby base station (BS) as well as
acquire synchronization. Due to the necessity of using antenna array in
millimeter-wave (mmW) IA, the channel spatial information can also be inferred.
The state-of-the-art directional IA (DIA) uses sector sounding beams with
limited angular resolution, and thus requires additional dedicated radio
resources, access latency and overhead for refined beam training. To remedy the
problem of access latency and overhead in DIA, this work proposes to use a
quasi-omni pseudorandom sounding beam for IA, and develops a novel algorithm
for joint initial access and fine resolution initial beam training without
requiring extra radio resources. We provide the analysis of the proposed
algorithm miss detection rate under synchronization error, and further derive
Cram\'er-Rao lower bound of angular estimation under frequency offset. Using
QuaDRiGa simulator with mmMAGIC model at 28 GHz, the numerical results show
that the proposed approach is advantageous to DIA with hierarchical beam
training. The proposed algorithm offers up to two order of magnitude access
latency saving compared to DIA, when the same discovery, post training SNR, and
overhead performance are targeted. This conclusion holds true in various
propagation environments and 3D locations of a mmW pico-cell with up to 140m
radius.Comment: 14 pages, 7 figures, submitted to IEEE Journal of Selected Topics in
Signal Processin
Beamforming Algorithm for Multiuser Wideband Millimeter-Wave Systems with Hybrid and Subarray Architectures
We present a beamforming algorithm for multiuser wideband millimeter wave
(mmWave) communication systems where one access point uses hybrid
analog/digital beamforming while multiple user stations have phased-arrays with
a single RF chain. The algorithm operates in a more general mode than others
available in literature and has lower computational complexity and training
overhead. Throughout the paper, we describe: i) the construction of novel
beamformer sets (codebooks) with wide sector beams and narrow beams based on
the orthogonality property of beamformer vectors, ii) a beamforming algorithm
that uses training transmissions over the codebooks to select the beamformers
that maximize the received sumpower along the bandwidth, and iii) a numerical
validation of the algorithm in standard indoor scenarios for mmWave WLANs using
channels obtained with both statistical and raytracing models. Our algorithm is
designed to serve multiple users in a wideband OFDM system and does not require
channel matrix knowledge or a particular channel structure. Moreover, we
incorporate antenna-specific aspects in the analysis, such as antenna coupling,
element radiation pattern, and beam squint. Although there are no other
solutions for the general system studied in this paper, we characterize the
algorithm's achievable rate and show that it attains more than 70 percent of
the spectral efficiency (between 1.5 and 3 dB SNR loss) with respect to ideal
fully-digital beamforming in the analyzed scenarios. We also show that our
algorithm has similar sum-rate performance as other solutions in the literature
for some special cases, while providing significantly lower computational
complexity (with a linear dependence on the number of antennas) and shorter
training overhead
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
Channel Estimation and Hybrid Precoding for Distributed Phased Arrays Based MIMO Wireless Communications
Distributed phased arrays based multiple-input multiple-output (DPA-MIMO) is
a newly introduced architecture that enables both spatial multiplexing and
beamforming while facilitating highly reconfigurable hardware implementation in
millimeter-wave (mmWave) frequency bands. With a DPA-MIMO system, we focus on
channel state information (CSI) acquisition and hybrid precoding. As benefited
from a coordinated and open-loop pilot beam pattern design, all the sub-arrays
can perform channel sounding with less training overhead compared with the
traditional orthogonal operation of each sub-array. Furthermore, two sparse
channel recovery algorithms, known as joint orthogonal matching pursuit (JOMP)
and joint sparse Bayesian learning with reweighting (JSBL-),
are proposed to exploit the hidden structured sparsity in the beam-domain
channel vector. Finally, successive interference cancellation (SIC) based
hybrid precoding through sub-array grouping is illustrated for the DPA-MIMO
system, which decomposes the joint sub-array RF beamformer design into an
interactive per-sub-array-group handle. Simulation results show that the
proposed two channel estimators fully take advantage of the partial coupling
characteristic of DPA-MIMO channels to perform channel recovery, and the
proposed hybrid precoding algorithm is suitable for such array-of-sub-arrays
architecture with satisfactory performance and low complexity.Comment: accepted by IEEE Transactions on Vehicular Technolog
True-Time-Delay Arrays for Fast Beam Training in Wideband Millimeter-Wave Systems
The best beam steering directions are estimated through beam training, which
is one of the most important and challenging tasks in millimeter-wave and
sub-terahertz communications. Novel array architectures and signal processing
techniques are required to avoid prohibitive beam training overhead associated
with large antenna arrays and narrow beams. In this work, we leverage recent
developments in true-time-delay (TTD) arrays with large delay-bandwidth
products to accelerate beam training using frequency-dependent probing beams.
We propose and study two TTD architecture candidates, including analog and
hybrid analog-digital arrays, that can facilitate beam training with only one
wideband pilot. We also propose a suitable algorithm that requires a single
pilot to achieve high-accuracy estimation of angle of arrival. The proposed
array architectures are compared in terms of beam training requirements and
performance, robustness to practical hardware impairments, and power
consumption. The findings suggest that the analog and hybrid TTD arrays achieve
a sub-degree beam alignment precision with 66% and 25% lower power consumption
than a fully digital array, respectively. Our results yield important design
trade-offs among the basic system parameters, power consumption, and accuracy
of angle of arrival estimation in fast TTD beam training.Comment: Journal pape
Design of Millimeter-Wave Single-Shot Beam Training for True-Time-Delay Array
Beam training is one of the most important and challenging tasks in
millimeter-wave and sub-terahertz communications. Novel transceiver
architectures and signal processing techniques are required to avoid
prohibitive training overhead when large antenna arrays with narrow beams are
used. In this work, we leverage recent developments in wide range
true-time-delay (TTD) analog arrays and frequency dependent probing beams to
accelerate beam training. We propose an algorithm that achieves high-accuracy
angle of arrival estimation with a single training symbol. Further, the impact
of TTD front-end impairments on beam training accuracy is investigated,
including the impact of gain, phase, and delay errors. Lastly, the study on
impairments and required specifications of resolution and range of analog delay
taps are used to provide a design insight of energy efficient TTD array, which
employs a novel architecture with discrete-time sampling based TTD elements.Comment: SPAWC 202
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