83 research outputs found
Representation Learning Method of Graph Convolutional Network Based on Structure Enhancement
Network representation learning has attracted widespread attention as a pre-processing process for some machine learning and deep learning tasks. However, most existing methods only consider influence of nodes' low-order neighbors to represent them. Either nodes' high-order neighbor or the intrinsic characteristic attributes of nodes are ignored, leading to the effect of network representation learning that needs to be improved. This paper proposes a novel model named Structure Enhanced Graph Convolutional Network (SEGCN) to address these limitations. SEGCN consists of the following components, i.e., the network structure enhancement to transform weak relationship into strong relationship, the node feature aggregation to fuse high-order neighbor information. Hence, the SEGCN model can simultaneously integrate network structure information, attribute information, and high-order neighbor relationships together. Experimental results for node classification and node clustering on six datasets show that SEGCN achieves better effectiveness and efficiency than state-of-the-art baselines
Achievable Diversity Order of HARQ-Aided Downlink NOMA Systems
The combination between non-orthogonal multiple access (NOMA) and hybrid
automatic repeat request (HARQ) is capable of realizing ultra-reliability, high
throughput and many concurrent connections particularly for emerging
communication systems. This paper focuses on characterizing the asymptotic
scaling law of the outage probability of HARQ-aided NOMA systems with respect
to the transmit power, i.e., diversity order. The analysis of diversity order
is carried out for three basic types of HARQ-aided downlink NOMA systems,
including Type I HARQ, HARQ with chase combining (HARQ-CC) and HARQ with
incremental redundancy (HARQ-IR). The diversity orders of three HARQ-aided
downlink NOMA systems are derived in closed-form, where an integration domain
partition trick is developed to obtain the bounds of the outage probability
specially for HARQ-CC and HARQ-IR-aided NOMA systems. The analytical results
show that the diversity order is a decreasing step function of transmission
rate, and full time diversity can only be achieved under a sufficiently low
transmission rate. It is also revealed that HARQ-IR-aided NOMA systems have the
largest diversity order, followed by HARQ-CC-aided and then Type I HARQ-aided
NOMA systems. Additionally, the users' diversity orders follow a descending
order according to their respective average channel gains. Furthermore, we
expand discussions on the cases of power-efficient transmissions and imperfect
channel state information (CSI). Monte Carlo simulations finally confirm our
analysis
A many-objective evolutionary algorithm based on rotated grid
Evolutionary optimization algorithms, a meta-heuristic approach, often encounter considerable challenges in many-objective optimization problems (MaOPs). The Pareto-based dominance loses its effectiveness in MaOPs, which are defined as having more than three objectives. Therefore, a more valid selection method is proposed to balance convergence and distribution. This paper proposes an algorithm using rotary grid technology to solve MaOPs (denoted by RGridEA). The algorithm uses the rotating grid to partition the objective space. Instead of using the Pareto non-dominated sorting strategy to layer the population a novel stratified method is used to enhance convergence effectively and make use of the grid to improve distribution and uniformity. Finally, with the other seven algorithm was tested on the test function DTLZ series analysis, confirming RGridEA is effective in resolving MaOPs
Outage Performance and Optimal Design of MIMO-NOMA Enhanced Small Cell Networks With Imperfect Channel-State Information
This paper focuses on boosting the performance of small cell networks (SCNs)
by integrating multiple-input multiple-output (MIMO) and non-orthogonal
multiple access (NOMA) in consideration of imperfect channel-state information
(CSI). The estimation error and the spatial randomness of base stations (BSs)
are characterized by using Kronecker model and Poisson point process (PPP),
respectively. The outage probabilities of MIMO-NOMA enhanced SCNs are first
derived in closed-form by taking into account two grouping policies, including
random grouping and distance-based grouping. It is revealed that the average
outage probabilities are irrelevant to the intensity of BSs in the
interference-limited regime, while the outage performance deteriorates if the
intensity is sufficiently low. Besides, as the channel uncertainty lessens, the
asymptotic analyses manifest that the target rates must be restricted up to a
bound to achieve an arbitrarily low outage probability in the absence of the
inter-cell interference.Moreover, highly correlated estimation error
ameliorates the outage performance under a low quality of CSI, otherwise it
behaves oppositely. Afterwards, the goodput is maximized by choosing
appropriate precoding matrix, receiver filters and transmission rates. In the
end, the numerical results verify our analysis and corroborate the superiority
of our proposed algorithm
How Long Non-Coding RNAs and MicroRNAs Mediate the Endogenous RNA Network of Head and Neck Squamous Cell Carcinoma: a Comprehensive Analysis
Background/Aims: Long non-coding RNAs (lncRNAs) act as competing endogenous RNAs (ceRNAs) to compete for microRNAs (miRNAs) in cancer metastasis. Head and neck squamous cell carcinoma (HNSCC) is one of the most common human cancers and rare biomarkers could predict the clinical prognosis of this disease and its therapeutic effect. Methods: Weighted gene co-expression network analysis (WGCNA) was performed to identify differentially expressed mRNAs (DEmRNAs) that might be key genes. GO enrichment and proteinâprotein interaction (PPI) analyses were performed to identify the principal functions of the DEmRNAs. An lncRNA-miRNA-mRNA network was constructed to understand the regulatory mechanisms in HNSCC. The prognostic signatures of mRNAs, miRNAs, and lncRNAs were determined by Gene Expression Profiling Interactive Analysis (GEPIA) and using KaplanâMeier survival curves for patients with lung squamous cell carcinoma. Results: We identified 2,023 DEmRNAs, 1,048 differentially expressed lncRNAs (DElncRNAs), and 82 differentially expressed miRNAs (DEmiRNAs). We found that eight DEmRNAs, 53 DElncRNAs, and 16 DEmiRNAs interacted in the ceRNA network. Three ceRNAs (HCG22, LINC00460 and STC2) were significantly correlated with survival. STC2 transcript levels were significantly higher in tumour tissues than in normal tissues, and the STC2 expression was slightly upregulated at different stages of HNSCC. Conclusion: LINC00460, HCG22 and STC2 exhibited aberrant levels of expression and may participate in the pathogenesis of HNSCC
Zero-Forcing Based Downlink Virtual MIMO-NOMA Communications in IoT Networks
To support massive connectivity and boost spectral efficiency for internet of
things (IoT), a downlink scheme combining virtual multiple-input
multiple-output (MIMO) and nonorthogonal multiple access (NOMA) is proposed.
All the single-antenna IoT devices in each cluster cooperate with each other to
establish a virtual MIMO entity, and multiple independent data streams are
requested by each cluster. NOMA is employed to superimpose all the requested
data streams, and each cluster leverages zero-forcing detection to de-multiplex
the input data streams. Only statistical channel state information (CSI) is
available at base station to avoid the waste of the energy and bandwidth on
frequent CSI estimations. The outage probability and goodput of the virtual
MIMO-NOMA system are thoroughly investigated by considering Kronecker model,
which embraces both the transmit and receive correlations. Furthermore, the
asymptotic results facilitate not only the exploration of physical insights but
also the goodput maximization. In particular, the asymptotic outage expressions
provide quantitative impacts of various system parameters and enable the
investigation of diversity-multiplexing tradeoff (DMT). Moreover, power
allocation coefficients and/or transmission rates can be properly chosen to
achieve the maximal goodput. By favor of Karush-Kuhn-Tucker conditions, the
goodput maximization problems can be solved in closed-form, with which the
joint power and rate selection is realized by using alternately iterating
optimization.Besides, the optimization algorithms tend to allocate more power
to clusters under unfavorable channel conditions and support clusters with
higher transmission rate under benign channel conditions
RepBNN: towards a precise Binary Neural Network with Enhanced Feature Map via Repeating
Binary neural network (BNN) is an extreme quantization version of
convolutional neural networks (CNNs) with all features and weights mapped to
just 1-bit. Although BNN saves a lot of memory and computation demand to make
CNN applicable on edge or mobile devices, BNN suffers the drop of network
performance due to the reduced representation capability after binarization. In
this paper, we propose a new replaceable and easy-to-use convolution module
RepConv, which enhances feature maps through replicating input or output along
channel dimension by times without extra cost on the number of
parameters and convolutional computation. We also define a set of RepTran rules
to use RepConv throughout BNN modules like binary convolution, fully connected
layer and batch normalization. Experiments demonstrate that after the RepTran
transformation, a set of highly cited BNNs have achieved universally better
performance than the original BNN versions. For example, the Top-1 accuracy of
Rep-ReCU-ResNet-20, i.e., a RepBconv enhanced ReCU-ResNet-20, reaches 88.97% on
CIFAR-10, which is 1.47% higher than that of the original network. And
Rep-AdamBNN-ReActNet-A achieves 71.342% Top-1 accuracy on ImageNet, a fresh
state-of-the-art result of BNNs. Code and models are available
at:https://github.com/imfinethanks/Rep_AdamBNN.Comment: This paper has absolutely nothing to do with repvgg, rep means
repeatin
Subcarrier and Power Allocation for the Downlink of Multicarrier NOMA Systems
International audienceThis paper investigates the joint subcarrier and power allocation problem for the downlink of a multi-carrier non-orthogonal multiple access (MC-NOMA) system. A novel three-step resource allocation framework is designed to deal with the sum rate maximization problem. In Step 1, we relax the problem by assuming each of the users can use all subcarriers simultaneously. With this assumption, we prove the convexity of the resultant power control problem and solve it via convex programming tools to get a power vector for each user; In Step 2, we allocate subcarriers to users by a heuristic greedy manner with the obtained power vectors in Step 1; In Step 3, the proposed power control schemes used in Step 1 are applied once more to further improve the system performance with the obtained sub-carrier assignment of Step 2. To solve the maximization problem with fixed subcarrier assignments in both Step 1 and Step 3, a centralized power allocation method based on projected gradient descent algorithm and two distributed power control strategies based respectively on pseudo-gradient algorithm and iterative waterfilling algorithm are investigated. Numerical results show that our proposed three-step resource allocation algorithm could achieve comparable sum rate performance to the existing near-optimal solution with much lower computational complexity and outperforms power controlled OMA scheme. Besides, a tradeoff between user fairness and sum rate performance can be achieved via applying different user power constraint strategies in the proposed algorithm
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