57 research outputs found
A Unified Approach for Beam-Split Mitigation in Terahertz Wideband Hybrid Beamforming
The sixth generation networks envision the deployment of terahertz (THz) band
as one of the key enabling property thanks to its abundant bandwidth. However,
the ultra-wide bandwidth in THz causes beam-split phenomenon due to the use of
a single analog beamformer (AB). Specifically, beam-split makes different
subcarriers to observe distinct directions since the same AB is adopted for all
subcarriers. Previous works mostly employ additional hardware components, e.g.,
time-delayer networks to mitigate beam-split by realizing virtual
subcarrier-dependent ABs. This paper introduces an efficient and unified
approach, called beam-split-aware (BSA) hybrid beamforming. In particular,
instead of virtually generating subcarrier-dependent ABs, a single AB is used
and the effect of beam-split is computed and passed into the digital
beamformers, which are subcarrier-dependent while maximizing spectral
efficiency. Hence, the proposed BSA approach effectively mitigates the impact
of beam-split and it can be applied to any hybrid beamforming architecture.
Manifold optimization and orthogonal matching pursuit techniques are considered
for the evaluation of the proposed approach in multi-user scenario. Numerical
simulations show that significant performance improvement can be achieved as
compared to the conventional techniques.Comment: This work has been submitted to the IEEE for publication. Copyright
may be transferred without notice, after which this version may no longer be
accessibl
Hybrid Precoding for Multiuser Millimeter Wave Massive MIMO Systems : A Deep Learning Approach
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In multi-user millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems, hybrid precoding is a crucial task to lower the complexity and cost while achieving a sufficient sum-rate. Previous works on hybrid precoding were usually based on optimization or greedy approaches. These methods either provide higher complexity or have sub-optimum performance. Moreover, the performance of these methods mostly relies on the quality of the channel data. In this work, we propose a deep learning (DL) framework to improve the performance and provide less computation time as compared to conventional techniques. In fact, we design a convolutional neural network for MIMO (CNN-MIMO) that accepts as input an imperfect channel matrix and gives the analog precoder and combiners at the output. The procedure includes two main stages. First, we develop an exhaustive search algorithm to select the analog precoder and combiners from a predefined codebook maximizing the achievable sum-rate. Then, the selected precoder and combiners are used as output labels in the training stage of CNN-MIMO where the input-output pairs are obtained. We evaluate the performance of the proposed method through numerous and extensive simulations and show that the proposed DL framework outperforms conventional techniques. Overall, CNN-MIMO provides a robust hybrid precoding scheme in the presence of imperfections regarding the channel matrix. On top of this, the proposed approach exhibits less computation time with comparison to the optimization and codebook based approaches.Peer reviewe
Federated Learning in Vehicular Networks
Machine learning (ML) has already been adopted in vehicular networks for such
applications as autonomous driving, road safety prediction and vehicular object
detection, due to its model-free characteristic, allowing adaptive fast
response. However, the training of the ML model brings significant overhead for
the data transmission between the parameter server and the edge devices in the
vehicles. Federated learning (FL) framework has been recently introduced as an
efficient tool with the goal of reducing this transmission overhead while also
achieving privacy through the transmission of only the model updates of the
learnable parameters rather than the whole dataset. In this article, we
investigate the usage of FL over ML in vehicular network applications to
develop intelligent transportation systems. We provide a comprehensive analysis
on the feasibility of FL for the ML based vehicular applications. Then, we
identify the major challenges from both learning perspective, i.e., data
labeling and model training, and from the communications point of view, i.e.,
data rate, reliability, transmission overhead/delay, privacy and resource
management. Finally, we highlight related future research directions for FL in
vehicular networks.Comment: 4 figures 7 pages. This work has been submitted to the IEEE for
publication. Copyright may be transferred without notice, after which this
version may no longer be accessibl
Near-field Hybrid Beamforming for Terahertz-band Integrated Sensing and Communications
Terahertz (THz) band communications and integrated sensing and communications
(ISAC) are two main facets of the sixth generation wireless networks. In order
to compensate the severe attenuation, the THz wireless systems employ large
arrays, wherein the near-field beam-squint severely degrades the beamforming
accuracy. Contrary to prior works that examine only either narrowband ISAC
beamforming or far-field models, we introduce an alternating optimization
technique for hybrid beamforming design in near-field THz-ISAC scenario. We
also propose an efficient approach to compensate near-field beam-squint via
baseband beamformers. Via numerical simulations, we show that the proposed
approach achieves satisfactory spectral efficiency performance while accurately
estimating the near-field beamformers and mitigating the beam-squint without
additional hardware components.Comment: Accepted Paper in 2023 IEEE Global Communications Conference
(GLOBECOM), Kuala Lumpur, Malaysia, 202
Spherical Wavefront Near-Field DoA Estimation in THz Automotive Radar
Automotive radar at terahertz (THz) band has the potential to provide compact
design. The availability of wide bandwidth at THz-band leads to high range
resolution. Further, very narrow beamwidth arising from large arrays yields
high angular resolution up to milli-degree level direction-of-arrival (DoA)
estimation. At THz frequencies and extremely large arrays, the signal wavefront
is spherical in the near-field that renders traditional far-field DoA
estimation techniques unusable. In this work, we examine near-field DoA
estimation for THz automotive radar. We propose an algorithm using multiple
signal classification (MUSIC) to estimate target DoAs and ranges while also
taking beam-squint in near-field into account. Using an array transformation
approach, we compensate for near-field beam-squint in noise subspace
computations to construct the beam-squint-free MUSIC spectra. Numerical
experiments show the effectiveness of the proposed method to accurately
estimate the target parameters
NBA-OMP: Near-field Beam-Split-Aware Orthogonal Matching Pursuit for Wideband THz Channel Estimation
The sixth-generation networks envision the terahertz (THz) band as one of the
key enabling technologies because of its ultrawide bandwidth. To combat the
severe attenuation, the THz wireless systems employ large arrays, wherein the
near-field beam-split (NB) severely degrades the accuracy of channel
acquisition. Contrary to prior works that examine only either narrowband
beamforming or far-field models, we estimate the wideband THz channel via an
NB-aware orthogonal matching pursuit (NBA-OMP) approach. We design an NBA
dictionary of near-field steering vectors by exploiting the corresponding
angular and range deviation. Our OMP algorithm accounts for this deviation
thereby ipso facto mitigating the effect of NB. Numerical experiments
demonstrate the effectiveness of the proposed channel estimation technique for
wideband THz systems.Comment: Accepted Paper in 2023 IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP
Machine Learning for Metasurfaces Design and Their Applications
Metasurfaces (MTSs) are increasingly emerging as enabling technologies to
meet the demands for multi-functional, small form-factor, efficient,
reconfigurable, tunable, and low-cost radio-frequency (RF) components because
of their ability to manipulate waves in a sub-wavelength thickness through
modified boundary conditions. They enable the design of reconfigurable
intelligent surfaces (RISs) for adaptable wireless channels and smart radio
environments, wherein the inherently stochastic nature of the wireless
environment is transformed into a programmable propagation channel. In
particular, space-limited RF applications, such as communications and radar,
that have strict radiation requirements are currently being investigated for
potential RIS deployment. The RIS comprises sub-wavelength units or meta-atoms,
which are independently controlled and whose geometry and material determine
the spectral response of the RIS. Conventionally, designing RIS to yield the
desired EM response requires trial and error by iteratively investigating a
large possibility of various geometries and materials through thousands of
full-wave EM simulations. In this context, machine/deep learning (ML/DL)
techniques are proving critical in reducing the computational cost and time of
RIS inverse design. Instead of explicitly solving Maxwell's equations, DL
models learn physics-based relationships through supervised training data. The
ML/DL techniques also aid in RIS deployment for numerous wireless applications,
which requires dealing with multiple channel links between the base station
(BS) and the users. As a result, the BS and RIS beamformers require a joint
design, wherein the RIS elements must be rapidly reconfigured. This chapter
provides a synopsis of DL techniques for both inverse RIS design and
RIS-assisted wireless systems.Comment: Book chapter, 70 pages, 12 figures, 2 tables. arXiv admin note:
substantial text overlap with arXiv:2101.09131, arXiv:2009.0254
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