228 research outputs found
Resource Allocation in the RIS Assisted SCMA Cellular Network Coexisting with D2D Communications
The cellular network coexisting with device-to-device (D2D) communications
has been studied extensively. Reconfigurable intelligent surface (RIS) and
non-orthogonal multiple access (NOMA) are promising technologies for the
evolution of 5G, 6G and beyond. Besides, sparse code multiple access (SCMA) is
considered suitable for next-generation wireless network in code-domain NOMA.
In this paper, we consider the RIS-aided uplink SCMA cellular network
simultaneously with D2D users. We formulate the optimization problem which aims
to maximize the cellular sum-rate by jointly designing D2D users resource block
(RB) association, the transmitted power for both cellular users and D2D users,
and the phase shifts at the RIS. The power limitation and users communication
requirements are considered. The problem is non-convex, and it is challenging
to solve it directly. To handle this optimization problem, we propose an
efficient iterative algorithm based on block coordinate descent (BCD) method.
The original problem is decoupled into three subproblems to solve separately.
Simulation results demonstrate that the proposed scheme can significantly
improve the sum-rate performance over various schemes.Comment: IEEE Acces
Thermoplastic Elastomers Based on Block, Graft, and Star Copolymers
In this book chapter, we focus on recent advances in thermoplastic elastomers based on synthetic polymers from the aspects of polymer architectures such as linear block, graft, and star copolymers. The first section is an introduction that covers a brief history and classification of thermoplastic elastomers (TPEs). The second section summarizes ABA triblock copolymers synthesized by various methods for TPE applications. The third section reviews TPEs based on graft copolymers, and the fourth section reviews TPEs based on star copolymers. The differences between TPE research in academia and industry are addressed in the last section as a perspective, with a view toward the generation of new, advanced, commercially viable TPEs
A novel prognostic model for patients with colon adenocarcinoma
BackgroundColon adenocarcinoma (COAD) is a highly heterogeneous disease, which makes its prognostic prediction challenging. The purpose of this study was to investigate the clinical epidemiological characteristics, prognostic factors, and survival outcomes of patients with COAD in order to establish and validate a predictive clinical model (nomogram) for these patients.MethodsUsing the SEER (Surveillance, Epidemiology, and End Results) database, we identified patients diagnosed with COAD between 1983 and 2015. Disease-specific survival (DSS) and overall survival (OS) were assessed using the log-rank test and Kaplan–Meier approach. Univariate and multivariate analyses were performed using Cox regression, which identified the independent prognostic factors for OS and DSS. The nomograms constructed to predict OS were based on these independent prognostic factors. The predictive ability of the nomograms was assessed using receiver operating characteristic (ROC) curves and calibration plots, while accuracy was assessed using decision curve analysis (DCA). Clinical utility was evaluated with a clinical impact curve (CIC).ResultsA total of 104,933 patients were identified to have COAD, including 31,479 women and 73,454 men. The follow-up study duration ranged from 22 to 88 months, with an average of 46 months. Multivariate Cox regression analysis revealed that age, gender, race, site_recode_ICD, grade, CS_tumor_size, CS_extension, and metastasis were independent prognostic factors. Nomograms were constructed to predict the probability of 1-, 3-, and 5-year OS and DSS. The concordance index (C-index) and calibration plots showed that the established nomograms had robust predictive ability. The clinical decision chart (from the DCA) and the clinical impact chart (from the CIC) showed good predictive accuracy and clinical utility.ConclusionIn this study, a nomogram model for predicting the individualized survival probability of patients with COAD was constructed and validated. The nomograms of patients with COAD were accurate for predicting the 1-, 3-, and 5-year DSS. This study has great significance for clinical treatments. It also provides guidance for further prospective follow-up studies
Uplink Transceiver Design and Optimization for Transmissive RMS Multi-Antenna Systems
In this paper, a novel uplink communication for the transmissive
reconfigurable metasurface (RMS) multi-antenna system is investigated.
Specifically, a transmissive RMS-based receiver equipped with a single
receiving antenna is first proposed, and a far-near field channel model is also
given. Then, in order to maximize the system sum-rate, we formulate a joint
optimization problem over subcarrier allocation, power allocation and RMS
transmissive coefficient design. Since the coupling of optimization variables,
the problem is non-convex, so it is challenging to solve it directly. In order
to tackle this problem, the alternating optimization (AO) algorithm is used to
decouple the optimization variables and divide the problem into two subproblems
to solve. Numerical results verify that the proposed algorithm has good
convergence performance and can improve system sum-rate compared with other
benchmark algorithms.Comment: arXiv admin note: text overlap with arXiv:2109.0546
Throughput Maximization for UAV-enabled Integrated Periodic Sensing and Communication
Unmanned aerial vehicle (UAV) is expected to revolutionize the existing
integrated sensing and communication (ISAC) system and promise a more flexible
joint design. Nevertheless, the existing works on ISAC mainly focus on
exploring the performance of both functionalities simultaneously during the
entire considered period, which may ignore the practical asymmetric sensing and
communication requirements. In particular, always forcing sensing along with
communication may make it is harder to balance between these two
functionalities due to shared spectrum resources and limited transmit power. To
address this issue, we propose a new integrated periodic sensing and
communication mechanism for the UAV-enabled ISAC system to provide a more
flexible trade-off between two integrated functionalities. Specifically, the
system achievable rate is maximized via jointly optimizing UAV trajectory, user
association, target sensing selection, and transmit beamforming, while meeting
the sensing frequency and beam pattern gain requirement for the given targets.
Despite that this problem is highly non-convex and involves closely coupled
integer variables, we derive the closed-form optimal beamforming vector to
dramatically reduce the complexity of beamforming design, and present a tight
lower bound of the achievable rate to facilitate UAV trajectory design. Based
on the above results, we propose a penalty-based algorithm to efficiently solve
the considered problem. The optimal achievable rate and the optimal UAV
location are analyzed under a special case of infinity number of antennas.
Furthermore, we prove the structural symmetry between the optimal solutions in
different ISAC frames without location constraints and propose an efficient
algorithm for solving the problem with location constraints.Comment: 32 pages, This work has been submitted to the IEEE for possible
publicatio
Design of Two-Level Incentive Mechanisms for Hierarchical Federated Learning
Hierarchical Federated Learning (HFL) is a distributed machine learning
paradigm tailored for multi-tiered computation architectures, which supports
massive access of devices' models simultaneously. To enable efficient HFL, it
is crucial to design suitable incentive mechanisms to ensure that devices
actively participate in local training. However, there are few studies on
incentive mechanism design for HFL. In this paper, we design two-level
incentive mechanisms for the HFL with a two-tiered computing structure to
encourage the participation of entities in each tier in the HFL training. In
the lower-level game, we propose a coalition formation game to joint optimize
the edge association and bandwidth allocation problem, and obtain efficient
coalition partitions by the proposed preference rule, which can be proven to be
stable by exact potential game. In the upper-level game, we design the
Stackelberg game algorithm, which not only determines the optimal number of
edge aggregations for edge servers to maximize their utility, but also optimize
the unit reward provided for the edge aggregation performance to ensure the
interests of cloud servers. Furthermore, numerical results indicate that the
proposed algorithms can achieve better performance than the benchmark schemes
Reconfigurable Intelligent Surface Assisted Free Space Optical Information and Power Transfer
Free space optical (FSO) transmission has emerged as a key candidate
technology for 6G to expand new spectrum and improve network capacity due to
its advantages of large bandwidth, low electromagnetic interference, and high
energy efficiency. Resonant beam operating in the infrared band utilizes
spatially separated laser cavities to enable safe and mobile high-power energy
and high-rate information transmission but is limited by line-of-sight (LOS)
channel. In this paper, we propose a reconfigurable intelligent surface (RIS)
assisted resonant beam simultaneous wireless information and power transfer
(SWIPT) system and establish an optical field propagation model to analyze the
channel state information (CSI), in which LOS obstruction can be detected
sensitively and non-line-of-sight (NLOS) transmission can be realized by
changing the phased of resonant beam in RIS. Numerical results demonstrate
that, apart from the transmission distance, the NLOS performance depends on
both the horizontal and vertical positions of RIS. The maximum NLOS energy
efficiency can achieve 55% within a transfer distance of 10m, a translation
distance of 4mm, and rotation angle of 50{\deg}
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