347 research outputs found
A novel soft clustering algorithm
AbstractPaper clustering problems in citation network is one of the hottest spots in data mining. However, traditional paper clustering algorithm stresses on the keywords analysis while ignores the “refer-to” relationship, which results in the problem of high time complexity and low accuracy. In this paper, we come up with a novel soft clustering algorithm in accordance with the complex priority and thegrouth theorem, and classify our algorithm into two steps: refer-to relationship analysis and keywords comparison. Experimental results show that our algorithm is able to greatly improve the search accuracy and efficiency
Robust Power Allocation for UAV-aided ISAC Systems with Uncertain Location Sensing Errors
Unmanned aerial vehicle (UAV) holds immense potential in integrated sensing
and communication (ISAC) systems for the Internet of Things (IoT). In this
paper, we propose a UAV-aided ISAC framework and investigate three robust power
allocation schemes. First, we derive an explicit expression of the Cram\'er-Rao
bound (CRB) based on time-of-arrival (ToA) estimation, which serves as the
performance metric for location sensing. Then, we analyze the impact of the
location sensing error (LSE) on communications, revealing the inherent coupling
relationship between communication and sensing. Moreover, we formulate three
robust communication and sensing power allocation problems by respectively
characterizing the LSE as an ellipsoidal distributed model, a Gaussian
distributed model, and an arbitrary distributed model. Notably, the
optimization problems seek to minimize the CRB, subject to data rate and total
power constraints. However, these problems are non-convex and intractable. To
address the challenges related to the three aforementioned LSE models, we
respectively propose to use the -Procedure and alternating
optimization (-AO) method, Bernstein-type inequality and successive
convex approximation (BI-SCA) method, and conditional value-at-risk (CVaR) and
AO (CVaR-AO) method to solve these problems. Finally, simulation results
demonstrate the robustness of our proposed UAV-aided ISAC system against the
LSE by comparing with the non-robust design, and evaluate the trade-off between
communication and sensing in the ISAC system
Robust Power Allocation for Integrated Visible Light Positioning and Communication Networks
Integrated visible light positioning and communication (VLPC), capable of
combining advantages of visible light communications (VLC) and visible light
positioning (VLP), is a promising key technology for the future Internet of
Things. In VLPC networks, positioning and communications are inherently
coupled, which has not been sufficiently explored in the literature. We propose
a robust power allocation scheme for integrated VLPC Networks by exploiting the
intrinsic relationship between positioning and communications. Specifically, we
derive explicit relationships between random positioning errors, following both
a Gaussian distribution and an arbitrary distribution, and channel state
information errors. Then, we minimize the Cramer-Rao lower bound (CRLB) of
positioning errors, subject to the rate outage constraint and the power
constraints, which is a chance-constrained optimization problem and generally
computationally intractable. To circumvent the nonconvex challenge, we
conservatively transform the chance constraints to deterministic forms by using
the Bernstein-type inequality and the conditional value-at-risk for the
Gaussian and arbitrary distributed positioning errors, respectively, and then
approximate them as convex semidefinite programs. Finally, simulation results
verify the robustness and effectiveness of our proposed integrated VLPC design
schemes.Comment: 13 pages, 15 figures, accepted by IEEE Transactions on Communication
Optimal Discrete Constellation Inputs for Aggregated LiFi-WiFi Networks
In this paper, we investigate the performance of a practical aggregated
LiFi-WiFi system with the discrete constellation inputs from a practical view.
We derive the achievable rate expressions of the aggregated LiFi-WiFi system
for the first time. Then, we study the rate maximization problem via optimizing
the constellation distribution and power allocation jointly. Specifically, a
multilevel mercy-filling power allocation scheme is proposed by exploiting the
relationship between the mutual information and minimum mean-squared error
(MMSE) of discrete inputs. Meanwhile, an inexact gradient descent method is
proposed for obtaining the optimal probability distributions. To strike a
balance between the computational complexity and the transmission performance,
we further develop a framework that maximizes the lower bound of the achievable
rate where the optimal power allocation can be obtained in closed forms and the
constellation distributions problem can be solved efficiently by Frank-Wolfe
method. Extensive numerical results show that the optimized strategies are able
to provide significant gains over the state-of-the-art schemes in terms of the
achievable rate.Comment: 14 pages, 13 figures, accepted by IEEE Transactions on Wireless
Communication
Optimal Power Allocation for Integrated Visible Light Positioning and Communication System with a Single LED-Lamp
In this paper, we investigate an integrated visible light positioning and
communication (VLPC) system with a single LED-lamp. First, by leveraging the
fact that the VLC channel model is a function of the receiver's location, we
propose a system model that estimates the channel state information (CSI) based
on the positioning information without transmitting pilot sequences. Second, we
derive the Cramer-Rao lower bound (CRLB) on the positioning error variance and
a lower bound on the achievable rate with on-off keying modulation. Third,
based on the derived performance metrics, we optimize the power allocation to
minimize the CRLB, while satisfying the rate outage probability constraint. To
tackle this non-convex optimization problem, we apply the worst-case
distribution of the Conditional Value-at-Risk (CVaR) and the block coordinate
descent (BCD) methods to obtain the feasible solutions. Finally, the effects of
critical system parameters, such as outage probability, rate threshold, total
power threshold, are revealed by numerical results.Comment: 13 pages, 14 figures, accepted by IEEE Transactions on Communication
A comprehensive overview of exosome lncRNAs: emerging biomarkers and potential therapeutics in endometriosis
Endometriosis is a gynecological condition that significantly impacting women’s daily lives. In recent years, the incidence of endometriosis has been rising yearly and is now an essential contributor to female infertility. Exosomes are extracellular vesicles (EVs) that carry long noncoding RNA (lncRNA) and shield lncRNA from the outside environment thanks to their vesicle-like structure. The role of exosome-derived lncRNAs in endometriosis is also receiving more study as high-throughput sequencing technology develops. Several lncRNAs with variable expression may be crucial to the emergence and growth of endometriosis. The early diagnosis of endometriosis will be considerably improved by further high specificity and sensitivity Exosome lncRNA screening. Exosomes assist lncRNAs in carrying out their roles, offering a new target for creating endometriosis-specific medications. In order to serve as a reference for clinical research on the pathogenesis, diagnosis, and treatment options of endometriosis, this paper covers the role of exosome lncRNAs in endometriosis and related molecular mechanisms
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