317 research outputs found
Joint resummation for pion wave function and pion transition form factor
We construct an evolution equation for the pion wave function in the
factorization theorem, whose solution sums the mixed logarithm
to all orders, with () being a parton momentum fraction (transverse
momentum). This joint resummation induces strong suppression of the pion wave
function in the small and large regions, being the impact parameter
conjugate to , and improves the applicability of perturbative QCD to hard
exclusive processes. The above effect is similar to those from the conventional
threshold resummation for the double logarithm and the conventional
resummation for . Combining the evolution equation for the
hard kernel, we are able to organize all large logarithms in the scattering, and to establish a scheme-independent
factorization formula. It will be shown that the significance of
next-to-leading-order contributions and saturation behaviors of this process at
high energy differ from those under the conventional resummations. It implies
that QCD logarithmic corrections to a process must be handled appropriately,
before its data are used to extract a hadron wave function. Our predictions for
the involved pion transition form factor, derived under the joint resummation
and the input of a non-asymptotic pion wave function with the second Gegenbauer
moment , match reasonably well the CLEO, BaBar, and Belle data.Comment: 31 pages, 7 figure
Copy-Paste Image Augmentation with Poisson Image Editing for Ultrasound Instance Segmentation Learning
Deep learning has shown great success in high-level image analysis problems;
yet its efficacy relies on the quality and diversity of the training data. In
this work, we introduce a copypaste image augmentation for ultrasound images.
The Poisson image editing technique is used to generate realistic and seamless
boundary transitions around the pasted image. Results showed that the proposed
image augmentation technique improves training performance in terms of higher
objective metrics and more stable training results
Five Facets of 6G: Research Challenges and Opportunities
Whilst the fifth-generation (5G) systems are being rolled out across the
globe, researchers have turned their attention to the exploration of radical
next-generation solutions. At this early evolutionary stage we survey five main
research facets of this field, namely {\em Facet~1: next-generation
architectures, spectrum and services, Facet~2: next-generation networking,
Facet~3: Internet of Things (IoT), Facet~4: wireless positioning and sensing,
as well as Facet~5: applications of deep learning in 6G networks.} In this
paper, we have provided a critical appraisal of the literature of promising
techniques ranging from the associated architectures, networking, applications
as well as designs. We have portrayed a plethora of heterogeneous architectures
relying on cooperative hybrid networks supported by diverse access and
transmission mechanisms. The vulnerabilities of these techniques are also
addressed and carefully considered for highlighting the most of promising
future research directions. Additionally, we have listed a rich suite of
learning-driven optimization techniques. We conclude by observing the
evolutionary paradigm-shift that has taken place from pure single-component
bandwidth-efficiency, power-efficiency or delay-optimization towards
multi-component designs, as exemplified by the twin-component ultra-reliable
low-latency mode of the 5G system. We advocate a further evolutionary step
towards multi-component Pareto optimization, which requires the exploration of
the entire Pareto front of all optiomal solutions, where none of the components
of the objective function may be improved without degrading at least one of the
other components
CoMP Enhanced Subcarrier and Power Allocation for Multi-Numerology based 5G-NR Networks
With proliferation of fifth generation (5G) new radio (NR) technology, it is
expected to meet the requirement of diverse traffic demands. We have designed a
coordinated multi-point (CoMP) enhanced flexible multi-numerology (MN) for
5G-NR networks to improve the network performance in terms of throughput and
latency. We have proposed a CoMP enhanced joint subcarrier and power allocation
(CESP) scheme which aims at maximizing sum rate under the considerations of
transmit power limitation and guaranteed quality-of-service (QoS) including
throughput and latency restrictions. By employing difference of two concave
functions (D.C.) approximation and abstract Lagrangian duality method, we
theoretically transform the original non-convex nonlinear problem into a
solvable maximization problem. Moreover, the convergence of our proposed CESP
algorithm with D.C. approximation is analytically derived with proofs, and is
further validated via numerical results. Simulation results demonstrated that
our proposed CESP algorithm outperforms the conventional non-CoMP and single
numerology mechanisms along with other existing benchmarks in terms of lower
latency and higher throughput under the scenarios of uniform and edge users
Reconfigurable Intelligent Surface-Empowered Self-Interference Cancellation for 6G Full-Duplex MIMO Communication Systems
With the advent of sixth-generation (6G) wireless communication networks, it
requires substantially increasing wireless traffic and extending serving
coverage. Reconfigurable intelligent surface (RIS) is widely considered as a
promising technique which is capable of improving the system data rate, energy
efficiency and coverage extension as well as the benefit of low power
consumption. Moreover, full-duplex (FD) transmission provides simultaneous
transmit and received signals, which theoretically enhances twice spectrum
efficiency. However, the self-interference (SI) in FD is a challenging task
requiring complex and high-overhead cancellation, which can be resolved by
configuring appropriate phase of RIS elements. This paper has proposed an
RIS-empowered full-duplex self-interference cancellation (RFSC) scheme to
alleviate the severe SI in an RIS-FD system. We consider the SI minimization of
RIS-FD uplink (UL) while guaranteeing quality-of-service (QoS) of UL users. The
closed-form solution is theoretically derived by exploiting Lagrangian method
under different numbers of RIS elements and receiving antennas. Simulation
results reveal that the proposed RFSC scheme outperforms the scenario without
RIS deployment in terms of higher signal-to-interference-plus-noise ratio
(SINR). Due to effective interference mitigation, the proposed RFSC can achieve
the highest SINR compared to other existing schemes in open literatures
Robust Active and Passive Beamforming for RIS-Assisted Full-Duplex Systems under Imperfect CSI
The sixth-generation (6G) wireless technology recognizes the potential of
reconfigurable intelligent surfaces (RIS) as an effective technique for
intelligently manipulating channel paths through reflection to serve desired
users. Full-duplex (FD) systems, enabling simultaneous transmission and
reception from a base station (BS), offer the theoretical advantage of doubled
spectrum efficiency. However, the presence of strong self-interference (SI) in
FD systems significantly degrades performance, which can be mitigated by
leveraging the capabilities of RIS. Moreover, accurately obtaining channel
state information (CSI) from RIS poses a critical challenge. Our objective is
to maximize downlink (DL) user data rates while ensuring quality-of-service
(QoS) for uplink (UL) users under imperfect CSI from reflected channels. To
address this, we introduce the robust active BS and passive RIS beamforming
(RAPB) scheme for RIS-FD, accounting for both SI and imperfect CSI. RAPB
incorporates distributionally robust design, conditional value-at-risk (CVaR),
and penalty convex-concave programming (PCCP) techniques. Additionally, RAPB
extends to active and passive beamforming (APB) with perfect channel
estimation. Simulation results demonstrate the UL/DL rate improvements achieved
considering various levels of imperfect CSI. The proposed RAPB/APB schemes
validate their effectiveness across different RIS deployment and RIS/BS
configurations. Benefited from robust beamforming, RAPB outperforms existing
methods in terms of non-robustness, deployment without RIS, conventional
successive convex approximation, and half-duplex systems
A New Paradigm for Device-free Indoor Localization: Deep Learning with Error Vector Spectrum in Wi-Fi Systems
The demand for device-free indoor localization using commercial Wi-Fi devices
has rapidly increased in various fields due to its convenience and versatile
applications. However, random frequency offset (RFO) in wireless channels poses
challenges to the accuracy of indoor localization when using fluctuating
channel state information (CSI). To mitigate the RFO problem, an error vector
spectrum (EVS) is conceived thanks to its higher resolution of signal and
robustness to RFO. To address these challenges, this paper proposed a novel
error vector assisted learning (EVAL) for device-free indoor localization. The
proposed EVAL scheme employs deep neural networks to classify the location of a
person in the indoor environment by extracting ample channel features from the
physical layer signals. We conducted realistic experiments based on OpenWiFi
project to extract both EVS and CSI to examine the performance of different
device-free localization techniques. Experimental results show that our
proposed EVAL scheme outperforms conventional machine learning methods and
benchmarks utilizing either CSI amplitude or phase information. Compared to
most existing CSI-based localization schemes, a new paradigm with higher
positioning accuracy by adopting EVS is revealed by our proposed EVAL system
BTS: Bifold Teacher-Student in Semi-Supervised Learning for Indoor Two-Room Presence Detection Under Time-Varying CSI
In recent years, indoor human presence detection based on supervised learning
(SL) and channel state information (CSI) has attracted much attention. However,
the existing studies that rely on spatial information of CSI are susceptible to
environmental changes, such as object movement, atmospheric factors, and
machine rebooting, which degrade prediction accuracy. Moreover, SL-based
methods require time-consuming labeling for retraining models. Therefore, it is
imperative to design a continuously monitored model life-cycle using a
semi-supervised learning (SSL) based scheme. In this paper, we conceive a
bifold teacher-student (BTS) learning approach for presence detection systems
that combines SSL by utilizing partially labeled and unlabeled datasets. The
proposed primal-dual teacher-student network intelligently learns spatial and
temporal features from labeled and unlabeled CSI. Additionally, the enhanced
penalized loss function leverages entropy and distance measures to distinguish
drifted data, i.e., features of new datasets affected by time-varying effects
and altered from the original distribution. The experimental results
demonstrate that the proposed BTS system sustains asymptotic accuracy after
retraining the model with unlabeled data. Furthermore, the label-free BTS
outperforms existing SSL-based models in terms of the highest detection
accuracy while achieving the asymptotic performance of SL-based methods
CRONOS: Colorization and Contrastive Learning for Device-Free NLoS Human Presence Detection using Wi-Fi CSI
In recent years, the demand for pervasive smart services and applications has
increased rapidly. Device-free human detection through sensors or cameras has
been widely adopted, but it comes with privacy issues as well as misdetection
for motionless people. To address these drawbacks, channel state information
(CSI) captured from commercialized Wi-Fi devices provides rich signal features
for accurate detection. However, existing systems suffer from inaccurate
classification under a non-line-of-sight (NLoS) and stationary scenario, such
as when a person is standing still in a room corner. In this work, we propose a
system called CRONOS (Colorization and Contrastive Learning Enhanced NLoS Human
Presence Detection), which generates dynamic recurrence plots (RPs) and
color-coded CSI ratios to distinguish mobile people from vacancy in a room,
respectively. We also incorporate supervised contrastive learning to retrieve
substantial representations, where consultation loss is formulated to
differentiate the representative distances between dynamic and stationary
cases. Furthermore, we propose a self-switched static feature enhanced
classifier (S3FEC) to determine the utilization of either RPs or color-coded
CSI ratios. Our comprehensive experimental results show that CRONOS outperforms
existing systems that apply machine learning, non-learning based methods, as
well as non-CSI based features in open literature. CRONOS achieves the highest
presence detection accuracy in vacancy, mobility, line-of-sight (LoS), and NLoS
scenarios
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