2,476 research outputs found
Efficient Beam Training and Channel Estimation for Millimeter Wave Communications Under Mobility
In this paper, we propose an efficient beam training technique for
millimeter-wave (mmWave) communications. When some mobile users are under high
mobility, the beam training should be performed frequently to ensure the
accurate acquisition of the channel state information. In order to reduce the
resource overhead caused by frequent beam training, we introduce a dedicated
beam training strategy which sends the training beams separately to a specific
high mobility user (called a target user) without changing the periodicity of
the conventional beam training. The dedicated beam training requires small
amount of resources since the training beams can be optimized for the target
user. In order to satisfy the performance requirement with low training
overhead, we propose the optimal training beam selection strategy which finds
the best beamforming vectors yielding the lowest channel estimation error based
on the target user's probabilistic channel information. Such dedicated beam
training is combined with the greedy channel estimation algorithm that accounts
for sparse characteristics and temporal dynamics of the target user's channel.
Our numerical evaluation demonstrates that the proposed scheme can maintain
good channel estimation performance with significantly less training overhead
compared to the conventional beam training protocols.Comment: 3p pages, This paper was submitted to IEEE Trans. Wireless Commun. on
Oct. 6, 201
Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems
Supporting high mobility in millimeter wave (mmWave) systems enables a wide
range of important applications such as vehicular communications and wireless
virtual/augmented reality. Realizing this in practice, though, requires
overcoming several challenges. First, the use of narrow beams and the
sensitivity of mmWave signals to blockage greatly impact the coverage and
reliability of highly-mobile links. Second, highly-mobile users in dense mmWave
deployments need to frequently hand-off between base stations (BSs), which is
associated with critical control and latency overhead. Further, identifying the
optimal beamforming vectors in large antenna array mmWave systems requires
considerable training overhead, which significantly affects the efficiency of
these mobile systems. In this paper, a novel integrated machine learning and
coordinated beamforming solution is developed to overcome these challenges and
enable highly-mobile mmWave applications. In the proposed solution, a number of
distributed yet coordinating BSs simultaneously serve a mobile user. This user
ideally needs to transmit only one uplink training pilot sequence that will be
jointly received at the coordinating BSs using omni or quasi-omni beam
patterns. These received signals draw a defining signature not only for the
user location, but also for its interaction with the surrounding environment.
The developed solution then leverages a deep learning model that learns how to
use these signatures to predict the beamforming vectors at the BSs. This
renders a comprehensive solution that supports highly-mobile mmWave
applications with reliable coverage, low latency, and negligible training
overhead. Simulation results show that the proposed deep-learning coordinated
beamforming strategy approaches the achievable rate of the genie-aided solution
that knows the optimal beamforming vectors with no training overhead.Comment: 42 pages, 14 figures, accepted in IEEE Acces
Bandit Inspired Beam Searching Scheme for mmWave High-Speed Train Communications
High-speed trains (HSTs) are being widely deployed around the world. To meet
the high-rate data transmission requirements on HSTs, millimeter wave (mmWave)
HST communications have drawn increasingly attentions. To realize sufficient
link margin, mmWave HST systems employ directional beamforming with large
antenna arrays, which results in that the channel estimation is rather
time-consuming. In HST scenarios, channel conditions vary quickly and channel
estimations should be performed frequently. Since the period of each
transmission time interval (TTI) is too short to allocate enough time for
accurate channel estimation, the key challenge is how to design an efficient
beam searching scheme to leave more time for data transmission. Motivated by
the successful applications of machine learning, this paper tries to exploit
the similarities between current and historical wireless propagation
environments. Using the knowledge of reinforcement learning, the beam searching
problem of mmWave HST communications is formulated as a multi-armed bandit
(MAB) problem and a bandit inspired beam searching scheme is proposed to reduce
the number of measurements as many as possible. Unlike the popular deep
learning methods, the proposed scheme does not need to collect and store a
massive amount of training data in advance, which can save a huge amount of
resources such as storage space, computing time, and power energy. Moreover,
the performance of the proposed scheme is analyzed in terms of regret. The
regret analysis indicates that the proposed schemes can approach the
theoretical limit very quickly, which is further verified by simulation
results
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
Inverse Multipath Fingerprinting for Millimeter Wave V2I Beam Alignment
Efficient beam alignment is a crucial component in millimeter wave systems
with analog beamforming, especially in fast-changing vehicular settings. This
paper proposes a position-aided approach where the vehicle's position (e.g.,
available via GPS) is used to query the multipath fingerprint database, which
provides prior knowledge of potential pointing directions for reliable beam
alignment. The approach is the inverse of fingerprinting localization, where
the measured multipath signature is compared to the fingerprint database to
retrieve the most likely position. The power loss probability is introduced as
a metric to quantify misalignment accuracy and is used for optimizing candidate
beam selection. Two candidate beam selection methods are developed, where one
is a heuristic while the other minimizes the misalignment probability. The
proposed beam alignment is evaluated using realistic channels generated from a
commercial ray-tracing simulator. Using the generated channels, an extensive
investigation is provided, which includes the required measurement sample size
to build an effective fingerprint, the impact of measurement noise, the
sensitivity to changes in traffic density, and beam alignment overhead
comparison with IEEE 802.11ad as the baseline. Using the concept of beam
coherence time, which is the duration between two consecutive beam alignments,
and parameters of IEEE 802.11ad, the overhead is compared in the mobility
context. The results show that while the proposed approach provides increasing
rates with larger antenna arrays, IEEE 802.11ad has decreasing rates due to the
larger beam training overhead that eats up a large portion of the beam
coherence time, which becomes shorter with increasing mobility.Comment: 16 pages, 19 figures, Submitted to IEEE Transactions on Vehicular
Technolog
A Survey of Millimeter Wave (mmWave) Communications for 5G: Opportunities and Challenges
With the explosive growth of mobile data demand, the fifth generation (5G)
mobile network would exploit the enormous amount of spectrum in the millimeter
wave (mmWave) bands to greatly increase communication capacity. There are
fundamental differences between mmWave communications and existing other
communication systems, in terms of high propagation loss, directivity, and
sensitivity to blockage. These characteristics of mmWave communications pose
several challenges to fully exploit the potential of mmWave communications,
including integrated circuits and system design, interference management,
spatial reuse, anti-blockage, and dynamics control. To address these
challenges, we carry out a survey of existing solutions and standards, and
propose design guidelines in architectures and protocols for mmWave
communications. We also discuss the potential applications of mmWave
communications in the 5G network, including the small cell access, the cellular
access, and the wireless backhaul. Finally, we discuss relevant open research
issues including the new physical layer technology, software-defined network
architecture, measurements of network state information, efficient control
mechanisms, and heterogeneous networking, which should be further investigated
to facilitate the deployment of mmWave communication systems in the future 5G
networks.Comment: 17 pages, 8 figures, 7 tables, Journal pape
Beam Acquisition and Training in Millimeter Wave Networks with Narrowband Pilots
This paper studies initial beam acquisition in a millimeter wave network
consisting of multiple access points (APs) and mobile devices. A training
protocol for joint estimation of transmit and receive beams is presented with a
general frame structure consisting of an initial access sub-frame followed by
data transmission sub-frames. During the initial subframe, APs and mobiles
sweep through a set of beams and determine the best transmit and receive beams
via a handshake. All pilot signals are narrowband (tones), and the mobiles are
distinguished by their assigned pilot frequencies. Both non-coherent and
coherent beam estimation methods based on, respectively, power detection and
maximum likelihood (ML) are presented. To avoid exchanging information about
beamforming vectors between APs and mobiles, a local maximum likelihood (LML)
algorithm is also presented. An efficient fast Fourier transform implementation
is proposed for ML and LML to achieve high-resolution. A system-level
optimization is performed in which the frame length, training time, and
training bandwidth are selected to maximize a rate objective taking into
account blockage and mobility. Simulation results based on a realistic network
topology are presented to compare the performance of different estimation
methods and training codebooks, and demonstrate the effectiveness of the
proposed protocol.Comment: 28 pages, 11 figure
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
Efficient Beam Alignment for mmWave Single-Carrier Systems with Hybrid MIMO Transceivers
Communication at millimeter wave (mmWave) bands is expected to become a key
ingredient of next generation (5G) wireless networks. Effective mmWave
communications require fast and reliable methods for beamforming at both the
User Equipment (UE) and the Base Station (BS) sides, in order to achieve a
sufficiently large Signal-to-Noise Ratio (SNR) after beamforming. We refer to
the problem of finding a pair of strongly coupled narrow beams at the
transmitter and receiver as the Beam Alignment (BA) problem. In this paper, we
propose an efficient BA scheme for single-carrier mmWave communications. In the
proposed scheme, the BS periodically probes the channel in the downlink via a
pre-specified pseudo-random beamforming codebook and pseudo-random spreading
codes, letting each UE estimate the Angle-of-Arrival / Angle-of-Departure
(AoA-AoD) pair of the multipath channel for which the energy transfer is
maximum. We leverage the sparse nature of mmWave channels in the AoA-AoD domain
to formulate the BA problem as the estimation of a sparse non-negative vector.
Based on the recently developed Non-Negative Least Squares (NNLS) technique, we
efficiently find the strongest AoA-AoD pair connecting each UE to the BS. We
evaluate the performance of the proposed scheme under a realistic channel
model, where the propagation channel consists of a few multipath scattering
components each having different delays, AoAs-AoDs, and Doppler shifts.The
channel model parameters are consistent with experimental channel measurements.
Simulation results indicate that the proposed method is highly robust to fast
channel variations caused by the large Doppler spread between the multipath
components. Furthermore, we also show that after achieving BA the beamformed
channel is essentially frequency-flat, such that single-carrier communication
needs no equalization in the time domain
Codebook-Based Beam Tracking for Conformal ArrayEnabled UAV MmWave Networks
Millimeter wave (mmWave) communications can potentially meet the high
data-rate requirements of unmanned aerial vehicle (UAV) networks. However, as
the prerequisite of mmWave communications, the narrow directional beam tracking
is very challenging because of the three-dimensional (3D) mobility and attitude
variation of UAVs. Aiming to address the beam tracking difficulties, we propose
to integrate the conformal array (CA) with the surface of each UAV, which
enables the full spatial coverage and the agile beam tracking in highly dynamic
UAV mmWave networks. More specifically, the key contributions of our work are
three-fold. 1) A new mmWave beam tracking framework is established for the
CA-enabled UAV mmWave network. 2) A specialized hierarchical codebook is
constructed to drive the directional radiating element (DRE)-covered
cylindrical conformal array (CCA), which contains both the angular beam pattern
and the subarray pattern to fully utilize the potential of the CA. 3) A
codebook-based multiuser beam tracking scheme is proposed, where the Gaussian
process machine learning enabled UAV position/attitude predication is developed
to improve the beam tracking efficiency in conjunction with the tracking-error
aware adaptive beamwidth control. Simulation results validate the effectiveness
of the proposed codebook-based beam tracking scheme in the CA-enabled UAV
mmWave network, and demonstrate the advantages of CA over the conventional
planner array in terms of spectrum efficiency and outage probability in the
highly dynamic scenarios
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