Digital Communications and Networks最新文献_第6页

Joint user association and resource allocation for cost-efficient NOMA-enabled F-RANs 实现低成本NOMA的F-RAN的联合用户关联和资源分配

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2024-12-01 DOI: 10.1016/j.dcan.2023.08.001

Yuan Ai , Chenxi Liu , Mugen Peng

{"title":"Joint user association and resource allocation for cost-efficient NOMA-enabled F-RANs","authors":"Yuan Ai , Chenxi Liu , Mugen Peng","doi":"10.1016/j.dcan.2023.08.001","DOIUrl":"10.1016/j.dcan.2023.08.001","url":null,"abstract":"<div><div>Integrating Non-Orthogonal Multiple Access (NOMA) into Fog Radio Access Networks (F-RANs) has shown to be effective in boosting the spectral efficiency, energy efficiency, connectivity, and reducing the latency, thus attracting significant research attention. However, the performance improvement of the NOMA-enabled F-RANs is at the cost of computational overheads, which are commonly neglected in their design and deployment. To address this issue, in this paper, we propose a hybrid dynamic downlink framework for NOMA-enabled F-RANs. In this framework, we first develop a novel network utility function, which takes both the network throughput and computational overheads into consideration, thus enabling us to comprehensively evaluate the performance of different access schemes for F-RANs. Based on the developed network utility function, we further formulate a network utility maximization problem, subject to practical constraints on the decoding order, power allocation, and quality-of-service. To solve this NP-hard problem, we decompose it into two subproblems, namely, a user equipment association and subchannel assignment subproblem and a power allocation subproblem. Three-dimensional matching and sequential convex programming-based algorithms are designed to solve these two subproblems, respectively. Through numerical results, we show how our proposed algorithms can achieve a good balance between the network throughput and computational overheads by judiciously adjusting the maximum transmit power of fog access points. We also show that the proposed NOMA-enabled F-RAN framework can increase, by up to 89%, the network utility, compared to OMA-based F-RANs.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"10 6","pages":"Pages 1686-1697"},"PeriodicalIF":7.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48715483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A fusion deep learning framework based on breast cancer grade prediction 基于乳腺癌等级预测的融合深度学习框架

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2024-12-01 DOI: 10.1016/j.dcan.2023.12.003

Weijian Tao , Zufan Zhang , Xi Liu , Maobin Yang

{"title":"A fusion deep learning framework based on breast cancer grade prediction","authors":"Weijian Tao , Zufan Zhang , Xi Liu , Maobin Yang","doi":"10.1016/j.dcan.2023.12.003","DOIUrl":"10.1016/j.dcan.2023.12.003","url":null,"abstract":"<div><div>In breast cancer grading, the subtle differences between HE-stained pathological images and the insufficient number of data samples lead to grading inefficiency. With its rapid development, deep learning technology has been widely used for automatic breast cancer grading based on pathological images. In this paper, we propose an integrated breast cancer grading framework based on a fusion deep learning model, which uses three different convolutional neural networks as submodels to extract feature information at different levels from pathological images. Then, the output features of each submodel are learned by the fusion network based on stacking to generate the final decision results. To validate the effectiveness and reliability of our proposed model, we perform dichotomous and multiclassification experiments on the Invasive Ductal Carcinoma (IDC) pathological image dataset and a generated dataset and compare its performance with those of the state-of-the-art models. The classification accuracy of the proposed fusion network is 93.8%, the recall is 93.5%, and the F1 score is 93.8%, which outperforms the state-of-the-art methods.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"10 6","pages":"Pages 1782-1789"},"PeriodicalIF":7.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139195693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Survey on security aspects of distributed software-defined networking controllers in an enterprise SD-WLAN 企业SD-WLAN中分布式软件定义网络控制器的安全性研究

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2024-12-01 DOI: 10.1016/j.dcan.2023.09.004

Neena Susan Shaji, Raja Muthalagu

引用次数: 0

Partial observation learning-based task offloading and spectrum allocation in UAV collaborative edge computing 无人机协作边缘计算中基于部分观测学习的任务卸载和频谱分配

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2024-12-01 DOI: 10.1016/j.dcan.2024.01.001

Chaoqiong Fan , Xinyu Wu , Bin Li , Chenglin Zhao

{"title":"Partial observation learning-based task offloading and spectrum allocation in UAV collaborative edge computing","authors":"Chaoqiong Fan , Xinyu Wu , Bin Li , Chenglin Zhao","doi":"10.1016/j.dcan.2024.01.001","DOIUrl":"10.1016/j.dcan.2024.01.001","url":null,"abstract":"<div><div>Capable of flexibly supporting diverse applications and providing computation services, the Mobile Edge Computing (MEC)-assisted Unmanned Aerial Vehicle (UAV) network is emerging as an innovational paradigm. In this paradigm, the heterogeneous resources of the network, including computing and communication resources, should be allocated properly to reduce computation and communication latency as well as energy consumption. However, most existing works solely focus on the optimization issues with global information, which is generally difficult to obtain in real-world scenarios. In this paper, fully considering the incomplete information resulting from diverse types of tasks, we study the joint task offloading and spectrum allocation problem in UAV network, where free UAV nodes serve as helpers for cooperative computation. The objective is to jointly optimize offloading mode, collaboration pairing, and channel allocation to minimize the weighted network cost. To achieve the purpose with only partial observation, an extensive-form game is introduced to reformulate the problem, and a regret learning-based scheme is proposed to achieve the equilibrium solution. With retrospective improvement property and <em>information set</em> concept, the designed algorithm is capable of combating incomplete information and obtaining more precise allocation patterns for diverse tasks. Numerical results show that our proposed algorithm outperforms the benchmarks across various settings.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"10 6","pages":"Pages 1635-1643"},"PeriodicalIF":7.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139395144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FedACT: An adaptive chained training approach for federated learning in computing power networks FedACT：用于计算能力网络联合学习的自适应链式训练方法

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2024-12-01 DOI: 10.1016/j.dcan.2023.12.007

Min Wei , Qianying Zhao , Bo Lei , Yizhuo Cai , Yushun Zhang , Xing Zhang , Wenbo Wang

{"title":"FedACT: An adaptive chained training approach for federated learning in computing power networks","authors":"Min Wei , Qianying Zhao , Bo Lei , Yizhuo Cai , Yushun Zhang , Xing Zhang , Wenbo Wang","doi":"10.1016/j.dcan.2023.12.007","DOIUrl":"10.1016/j.dcan.2023.12.007","url":null,"abstract":"<div><div>Federated Learning (FL) is a novel distributed machine learning methodology that addresses large-scale parallel computing challenges while safeguarding data security. However, the traditional FL model in communication scenarios, whether for uplink or downlink communications, may give rise to several network problems, such as bandwidth occupation, additional network latency, and bandwidth fragmentation. In this paper, we propose an adaptive chained training approach (FedACT) for FL in computing power networks. First, a Computation-driven Clustering Strategy (CCS) is designed. The server clusters clients by task processing delays to minimize waiting delays at the central server. Second, we propose a Genetic-Algorithm-based Sorting (GAS) method to optimize the order of clients participating in training. Finally, based on the table lookup and forwarding rules of the Segment Routing over IPv6 (SRv6) protocol, the sorting results of GAS are written into the SRv6 packet header, to control the order in which clients participate in model training. We conduct extensive experiments on two datasets of CIFAR-10 and MNIST, and the results demonstrate that the proposed algorithm offers improved accuracy, diminished communication costs, and reduced network delays.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"10 6","pages":"Pages 1576-1589"},"PeriodicalIF":7.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139393177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cost-aware cloud workflow scheduling using DRL and simulated annealing 利用 DRL 和模拟退火进行成本感知云工作流调度

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2024-12-01 DOI: 10.1016/j.dcan.2023.12.009

Yan Gu , Feng Cheng , Lijie Yang , Junhui Xu , Xiaomin Chen , Long Cheng

{"title":"Cost-aware cloud workflow scheduling using DRL and simulated annealing","authors":"Yan Gu , Feng Cheng , Lijie Yang , Junhui Xu , Xiaomin Chen , Long Cheng","doi":"10.1016/j.dcan.2023.12.009","DOIUrl":"10.1016/j.dcan.2023.12.009","url":null,"abstract":"<div><div>Cloud workloads are highly dynamic and complex, making task scheduling in cloud computing a challenging problem. While several scheduling algorithms have been proposed in recent years, they are mainly designed to handle batch tasks and not well-suited for real-time workloads. To address this issue, researchers have started exploring the use of Deep Reinforcement Learning (DRL). However, the existing models are limited in handling independent tasks and cannot process workflows, which are prevalent in cloud computing and consist of related subtasks. In this paper, we propose SA-DQN, a scheduling approach specifically designed for real-time cloud workflows. Our approach seamlessly integrates the Simulated Annealing (SA) algorithm and Deep Q-Network (DQN) algorithm. The SA algorithm is employed to determine an optimal execution order of subtasks in a cloud server, serving as a crucial feature of the task for the neural network to learn. We provide a detailed design of our approach and show that SA-DQN outperforms existing algorithms in terms of handling real-time cloud workflows through experimental results.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"10 6","pages":"Pages 1590-1599"},"PeriodicalIF":7.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139457516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rate distortion optimization for adaptive gradient quantization in federated learning 联合学习中自适应梯度量化的速率失真优化

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2024-12-01 DOI: 10.1016/j.dcan.2024.01.005

Guojun Chen , Kaixuan Xie , Wenqiang Luo , Yinfei Xu , Lun Xin , Tiecheng Song , Jing Hu

{"title":"Rate distortion optimization for adaptive gradient quantization in federated learning","authors":"Guojun Chen , Kaixuan Xie , Wenqiang Luo , Yinfei Xu , Lun Xin , Tiecheng Song , Jing Hu","doi":"10.1016/j.dcan.2024.01.005","DOIUrl":"10.1016/j.dcan.2024.01.005","url":null,"abstract":"<div><div>Federated Learning (FL) is an emerging machine learning framework designed to preserve privacy. However, the continuous updating of model parameters over uplink channels with limited throughput leads to a huge communication overload, which is a major challenge for FL. To address this issue, we propose an adaptive gradient quantization approach that enhances communication efficiency. Aiming to minimize the total communication costs, we consider both the correlation of gradients between local clients and the correlation of gradients between communication rounds, namely, in the time and space dimensions. The compression strategy is based on rate distortion theory, which allows us to find an optimal quantization strategy for the gradients. To further reduce the computational complexity, we introduce the Kalman filter into the proposed approach. Finally, numerical results demonstrate the effectiveness and robustness of the proposed rate-distortion optimization adaptive gradient quantization approach in significantly reducing the communication costs when compared to other quantization methods.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"10 6","pages":"Pages 1813-1825"},"PeriodicalIF":7.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139823537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust beamforming design for energy harvesting efficiency maximization in RIS-aided SWIPT system 在 RIS 辅助 SWIPT 系统中实现能量收集效率最大化的稳健波束成形设计

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2024-12-01 DOI: 10.1016/j.dcan.2024.01.004

Xingquan Li , Hongxia Zheng , Chunlong He , Xiaowen Tian , Xin Lin

引用次数: 0

Deep radio signal clustering with interpretability analysis based on saliency map 基于显著性图的具有可解释性分析的深度无线电信号聚类

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2024-10-01 DOI: 10.1016/j.dcan.2023.01.010

Huaji Zhou , Jing Bai , Yiran Wang , Junjie Ren , Xiaoniu Yang , Licheng Jiao

{"title":"Deep radio signal clustering with interpretability analysis based on saliency map","authors":"Huaji Zhou , Jing Bai , Yiran Wang , Junjie Ren , Xiaoniu Yang , Licheng Jiao","doi":"10.1016/j.dcan.2023.01.010","DOIUrl":"10.1016/j.dcan.2023.01.010","url":null,"abstract":"<div><div>With the development of information technology, radio communication technology has made rapid progress. Many radio signals that have appeared in space are difficult to classify without manually labeling. Unsupervised radio signal clustering methods have recently become an urgent need for this situation. Meanwhile, the high complexity of deep learning makes it difficult to understand the decision results of the clustering models, making it essential to conduct interpretable analysis. This paper proposed a combined loss function for unsupervised clustering based on autoencoder. The combined loss function includes reconstruction loss and deep clustering loss. Deep clustering loss is added based on reconstruction loss, which makes similar deep features converge more in feature space. In addition, a features visualization method for signal clustering was proposed to analyze the interpretability of autoencoder utilizing Saliency Map. Extensive experiments have been conducted on a modulated signal dataset, and the results indicate the superior performance of our proposed method over other clustering algorithms. In particular, for the simulated dataset containing six modulation modes, when the SNR is 20 dB, the clustering accuracy of the proposed method is greater than 78%. The interpretability analysis of the clustering model was performed to visualize the significant features of different modulated signals and verified the high separability of the features extracted by clustering model.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"10 5","pages":"Pages 1448-1458"},"PeriodicalIF":7.5,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43183277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Game-theoretic physical layer authentication for spoofing detection in internet of things 物联网中用于欺骗检测的博弈论物理层认证

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2024-10-01 DOI: 10.1016/j.dcan.2022.12.016

Yue Wu, Tao Jing, Qinghe Gao, Yingzhen Wu, Yan Huo

{"title":"Game-theoretic physical layer authentication for spoofing detection in internet of things","authors":"Yue Wu, Tao Jing, Qinghe Gao, Yingzhen Wu, Yan Huo","doi":"10.1016/j.dcan.2022.12.016","DOIUrl":"10.1016/j.dcan.2022.12.016","url":null,"abstract":"<div><div>The Internet of Things (IoT) has permeated various fields relevant to our lives. In these applications, countless IoT devices transmit vast amounts of data, which often carry important and private information. To prevent malicious users from spoofing these information, the first critical step is effective authentication. Physical Layer Authentication (PLA) employs unique characteristics inherent to wireless signals and physical devices and is promising in the IoT due to its flexibility, low complexity, and transparency to higher layer protocols. In this paper, the focus is on the interaction between multiple malicious spoofers and legitimate receivers in the PLA process. First, the interaction is formulated as a static spoof detection game by including the spoofers and receivers as players. The best authentication threshold of the receiver and the attack rate of the spoofers are consideblack as Nash Equilibrium (NE). Then, closed-form expressions are derived for all NEs in the static environment in three cases: multiplayer games, zero-sum games with collisions, and zero-sum games without collisions. Considering the dynamic environment, a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm is proposed to analyze the interactions of receiver and spoofers. Last, comprehensive simulation experiments are conducted and demonstrate the impact of environmental parameters on the NEs, which provides guidance to design effective PLA schemes.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"10 5","pages":"Pages 1394-1404"},"PeriodicalIF":7.5,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49221548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0