IEEE Transactions on Cognitive Communications and Networking最新文献

{"title":"Priority-Aware Deployment of Autoscaling Service Function Chains Based on Deep Reinforcement Learning","authors":"Xue Yu;Ran Wang;Jie Hao;Qiang Wu;Changyan Yi;Ping Wang;Dusit Niyato","doi":"10.1109/TCCN.2024.3358565","DOIUrl":"10.1109/TCCN.2024.3358565","url":null,"abstract":"Communication networks are being restructured by means of network function virtualization (NFV) and service-based architecture (SBA) to embrace greater flexibility, agility, programmability and efficiency. The deployment of service function chains (SFCs) to flexibly offer diverse network services is considered essential in NFV-based networks. Beyond the fifth-generation (5G) and sixth-generation (6G) eras, SFC deployment should be capable of satisfying various quality of service (QoS) requirements, coping with dynamic network states and traffic, handling urgent business in a timely manner, and avoiding resource congestion, all of which present significant scheduling challenges. In this paper, we propose a priority-aware deployment framework for autoscaling and multi-objective SFCs, which mainly includes 2 parts. First, to guarantee the diverse QoS requirements (e.g., latency and request acceptance rate) of various network services, a multi-objective SFC deployment scheme is established to optimize the service latency, deployment cost and service acceptance rate. Second, a deep reinforcement learning (DRL) algorithm, named the autoscaling and priority-aware SFC deployment algorithm (APSD), is further designed to solve the multi-objective optimization problem, which is NP hard. In APSD, we first prioritize requests with varying real-time characteristics to ensure that urgent services can be processed in a timely manner; based on the resiliency characteristics of virtual network functions (VNFs), we propose a hybrid scaling strategy to scale VNFs both horizontally and vertically to respond to changes in service requests and workload. We report comprehensive experiments carried out to assess the effectiveness of the proposed SFC deployment framework and demonstrate its advantages over its counterparts. Thus, we show that APSD is time efficient in solving the multi-objective optimization problem and that the obtained strategy always consumes the least resources (e.g., central processing unit (CPU) and memory resources) and surpasses two baseline algorithms with a 29.5% and 12.36% lower latency on average.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"10 3","pages":"1050-1062"},"PeriodicalIF":8.6,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139946802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic Composite-Modulation Classification Using Ultra Lightweight Deep-Learning Network Based on Cyclic-Paw-Print","authors":"Xiao Yan;Pengfei Yang;Xunuo Zhong;Qian Wang;Hsiao-Chun Wu;Ling He","doi":"10.1109/TCCN.2024.3357850","DOIUrl":"10.1109/TCCN.2024.3357850","url":null,"abstract":"Automatic composite-modulation classification (ACMC) has been considered as an essential function in the next generation intelligent telemetry, tracking & command (TT&C), cognitive space communications, and space surveillance. This paper introduces a novel ACMC scheme using the cyclic-paw-print extracted from the composite-modulation (CM) signals. In this new framework, the cyclic-spectrum analysis is first invoked to acquire the polyspectra of the received CM signals corrupted by different fading channels. Then, a new feature, namely cyclic-paw-print (CPP), is established upon the image representation of the cyclic spectrum, which can be robust against channel noise. Then, a highly-efficient ultra lightweight deep-learning network (ULWNet), which takes the CPPs as the input features, is designed to identify the composite modulation type. Our proposed new scheme can greatly improve the computational efficiencies incurred by the existing deep-learning networks and capture more reliable features latent in CM signals to result in an excellent classification accuracy. Monte Carlo simulation results demonstrate the effectiveness and the superiority of our proposed new ACMC scheme to the existing deep-learning networks.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"10 3","pages":"866-879"},"PeriodicalIF":8.6,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139946808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DSIL: An Effective Spectrum Prediction Framework Against Spectrum Concept Drift","authors":"Lantu Guo;Jun Lu;Jianping An;Kai Yang","doi":"10.1109/TCCN.2024.3355430","DOIUrl":"10.1109/TCCN.2024.3355430","url":null,"abstract":"Predicting spectrum plays an importance role in cognitive networks, which is the key to address the issue of spectrum scarcity. Deep learning methods for spectrum prediction have attracted significant interests because of the exceptional accuracy. However, when dealing with radio frequency (RF) measurements from real data traffic, the precise distribution of the measurements is often unknown, making model mismatch an inevitable occurrence. This is known as spectrum concept drift, which presents a formidable obstacle for traditional deep learning adapt to the dynamic spectrum environment. Considering spectrum concept drift, we proposed Deep Spectrum Incremental Learning (DSIL) method, a two stage framework including Concept Drift Detection module and Adaptive Spectrum Prediction module. In the first stage, we analysis concept drift detector mechanism and propose an effective spectrum concept drift method by leveraging Hoeffding drift detection method with averaging (HDDM-A). In the second stage, we propose Spectrum Incremental Learning Triple Net (SILTN) for spectrum incremental learning. SILTN, consisted of Multilayer Perceptron (MLP), ConvGRU and ConvLSTM, can effectively extract spectrum spatial and temporal features, and thus, improve spectrum prediction performance. Lastly, we introduce an Adaptive Spectrum Prediction Training (ASPT) method, designed to help SILTN achieve a better balance between past spectrum prediction tasks and incoming spectrum prediction tasks after finetune. The experimental results demonstrate that the DSIL framework can effectively address the issue of concept drift in common deep learning models for spectrum prediction. To the best of our knowledge, this is the first work considering spectrum concept drift detection and corresponding solution.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"10 3","pages":"794-806"},"PeriodicalIF":8.6,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139946964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward Efficient Neural Networks Through Predictor-Assisted NSGA-III for Anomaly Traffic Detection of IoT","authors":"Xinlei Wang;Mingshu He;Jiaxuan Wang;Xiaojuan Wang","doi":"10.1109/TCCN.2024.3355433","DOIUrl":"10.1109/TCCN.2024.3355433","url":null,"abstract":"Edge computing enhances intrusion detection by extending its reach to smaller Internet of Things (IoT) devices and edge nodes, improving real-time detection and data privacy. However, due to the limited processing power and storage of edge nodes, lightweight models with high detection accuracy are urgently needed. The Neural Architecture Search (NAS) technique based on multi-objective genetic algorithms can simultaneously balance model complexity and performance, thus automatically designing models for fast and accurate detection. However, NAS requires training each model from scratch during the optimization process to obtain its detection accuracy as one of the fitness evaluation criteria. To address this limitation, we propose an efficient predictor-assisted NSGA-III algorithm. It uses proxy models to swiftly predict architecture accuracy, eliminating the need for complete training and greatly improving optimization efficiency. Furthermore, we have designed an innovative search space that allows for the reduction of internal channels and output feature map dimensions within the model, resulting in the creation of lightweight models with minimal impact on classification performance. The proposed method is validated by searching a narrower Pareto-optimal model of the competitive F1 score of 95.17% with 38.10 MB FLOPs on the UNSW-NB15 dataset. By adding a predictor, the number of optimizations per iteration increased, leading to faster convergence. Additionally, when comparing the search spaces before and after our design in the most complex structure (with 7 cells), the model’s classification error rate increased from 4.31% to 4.36%, while the FLOPs decreased from 271.13MB to 186.9MB.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"10 3","pages":"982-995"},"PeriodicalIF":8.6,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139946954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Source to Multi-Target Decentralized Federated Domain Adaptation","authors":"Su Wang;Seyyedali Hosseinalipour;Christopher G. Brinton","doi":"10.1109/TCCN.2024.3352976","DOIUrl":"10.1109/TCCN.2024.3352976","url":null,"abstract":"Heterogeneity across devices in federated learning (FL) typically refers to statistical (e.g., non-i.i.d. data distributions) and resource (e.g., communication bandwidth) dimensions. In this paper, we focus on another important dimension that has received less attention: varying quantities/distributions of labeled and unlabeled data across devices. In order to leverage all data, we develop a decentralized federated domain adaptation methodology which considers the transfer of ML models from devices with high quality labeled data (called sources) to devices with low quality or unlabeled data (called targets). Our methodology, Source-Target Determination and Link Formation (ST-LF), optimizes both (i) classification of devices into sources and targets and (ii) source-target link formation, in a manner that considers the trade-off between ML model accuracy and communication energy efficiency. To obtain a concrete objective function, we derive a measurable generalization error bound that accounts for estimates of source-target hypothesis deviations and divergences between data distributions. The resulting optimization problem is a mixed-integer signomial program, a class of NP-hard problems, for which we develop an algorithm based on successive convex approximations to solve it tractably. Subsequent numerical evaluations of ST-LF demonstrate that it improves classification accuracy and energy efficiency over state-of-the-art baselines.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"10 3","pages":"1011-1025"},"PeriodicalIF":8.6,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139946806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Resource Allocation for Real-Time Cloud XR Video Transmission: A Reinforcement Learning Approach","authors":"Zhaocheng Wang;Rui Wang;Jun Wu;Wei Zhang;Chenxi Li","doi":"10.1109/TCCN.2024.3352982","DOIUrl":"10.1109/TCCN.2024.3352982","url":null,"abstract":"The extend reality (XR) applications are increasing rapidly alongside the development of mobile Internet. Wireless resource allocation faces a significant challenge due to the high reliability and ultra-low latency characteristics of XR applications. So it is crucial to implement a rational resource allocation program. However, the complex characteristics of multi-user channels, coupled with the huge solution space of the resource allocation optimization problem, prevent conventional methods from efficiently and reliably deriving resource block (RB) allocation schemes. Therefore, in this paper, we construct a low-latency, highly dynamic cloud XR video transmission model considering the randomness of video arrival misalignment for different users, and we resort to newly developed deep reinforcement learning (DRL) techniques for solutions. To deal with the dimensional disaster problem with exponential order of RB allocation, we propose a parallel multi-DRL framework as the foundation for introducing two dynamic RB allocation algorithms: multi noisy double dueling deep Q networks (M-Noisy-D3QN) and multi soft actor critic (M-SAC). Both of the proposed algorithms can improve resource utilization and can achieve the exploration ability and complexity trade-off. Moreover, to address the challenge that RB allocation actions and system goals are not directly related, we design a novel reward function combining external rewards and internal incentives to establish a coherent connection between the two, i.e., solve the reward sparsity problem in DRL. Simulation results show that the proposed dynamic RB allocation methods can successfully serve nearly twice as many users as other benchmarks in case of bandwidth resource constraints.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"10 3","pages":"996-1010"},"PeriodicalIF":8.6,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139946963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resource Knowledge-Driven Heterogeneous Graph Learning for Website Fingerprinting","authors":"Bo Gao;Weiwei Liu;Guangjie Liu;Fengyuan Nie","doi":"10.1109/TCCN.2024.3350531","DOIUrl":"10.1109/TCCN.2024.3350531","url":null,"abstract":"Website fingerprinting (WF) attacks play a crucial role in network traffic analysis for ensuring network security and management. Despite increasing TLS encryption for user privacy, HTTP traffic dominates phishing and pirate website. Fast flux service networks, round robin domain name system, and content delivery networks have rendered IP address or domain name-based WF attacks less effective. Manual feature-based machine learning and recent end-to-end deep learning methods have showed promise. Nevertheless, website content updates induce concept-drift, limiting their accuracy. This study exploits the fact that resource types and website layouts are usually consistent, whereas specific resources are dynamically changing. The resource knowledge extracted from HTTP request packets is utilized to construct a graph representation of website browsing traffic. Then, a heterogeneous graph neural network specifically designed for website fingerprinting using this representation is proposed. This resource knowledge-driven graph learning framework can retain valuable pattern information while mitigating the impact of the concept-drift. The proposed WF attack is evaluated using a real-world dataset comprising over 120,000 malicious and more than 940,000 benign website flows. It can achieve over 98% accuracy when determining benign-malicious websites and 97.6% in identifying website types. These results demonstrate a notable improvement over state-of-the-art WF attacks.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"10 3","pages":"968-981"},"PeriodicalIF":8.6,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139946962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trace Pheromone-Based Energy-Efficient UAV Dynamic Coverage Using Deep Reinforcement Learning","authors":"Xu Cheng;Rong Jiang;Hongrui Sang;Gang Li;Bin He","doi":"10.1109/TCCN.2024.3350590","DOIUrl":"10.1109/TCCN.2024.3350590","url":null,"abstract":"Unmanned aerial vehicles (UAVs) are widely used in disaster or remote areas to provide ubiquitous service. Due to the limited energy and communication range of UAVs, and the operation of UAVs is subject to high uncertainty, current coverage path planning algorithms are not sufficient. Therefore, autonomous dynamic and energy-efficient path planning is still an important research direction for improving coverage efficiency, especially involving multiagent. To address this problem, we introduce a novel trace pheromone into multi-agent reinforcement learning framework for energy-efficient UAV dynamic coverage control, which is termed trace pheromone-based UAV energy-efficient dynamic coverage (TP-EDC). First, we combine multi-agent deep deterministic policy gradient (MADDPG) with a trace pheromone model to serve as a strong tool for building our TP-EDC framework. Meanwhile, the trace pheromones model is integrated into stigmergy mechanism to simulate natural pheromones, which enhances the inner indirect communications among distributed UAVs and avoids network delay. Finally, the intensive simulation results demonstrate that the proposed method can maximize the coverage efficiency by comprehensively considering the coverage rate and energy consumption. Our method also shows significant dynamic coverage performance compared to two well-known baselines methods.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"10 3","pages":"1063-1074"},"PeriodicalIF":8.6,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139946788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint Broadcast and Unicast Transmission Based on RSMA and Spectrum Sharing for Integrated Satellite–Terrestrial Network","authors":"Shuai Han;Zhiqiang Li;Qiang Xue;Weixiao Meng;Cheng Li","doi":"10.1109/TCCN.2024.3350596","DOIUrl":"10.1109/TCCN.2024.3350596","url":null,"abstract":"The integrated satellite-terrestrial network (ISTN) is gaining attention for seamless communication services, which can provide diverse services to terminals, i.e., broadcast and unicast services. However, it is challenging to address massive terminal access and meet diverse information services under limited spectrum resources and strong multiple access interference in ISTN. Dynamic spectrum sharing and rate-splitting multiple access (RSMA) have emerged as promising technologies, where RSMA offers non-orthogonal transmission and robust interference management. Motivated by this, we establish a downlink non-orthogonal broadcast and unicast (NOBU) model using the 1-layer rate-splitting strategy for ISTN, which encodes broadcast data and unicast data into common and private streams. Then, we propose four NOBU transmission schemes based on different spectrum sharing modes to maximize the max-min rate (MMR), where schemes based on hybrid spectrum sharing consider the unevenly distributed and time-varying spectrum resources and the number of terminals. Furthermore, we formulate joint MMR optimization problems while satisfying the broadcast information rate requirement in ISTN. To tackle these non-convex problems, we introduce an improved alternating optimization algorithm based on weighted minimum mean square error. Simulation results verify that the RSMA-based NOBU schemes have significant performance gains compared with various baseline schemes.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"10 3","pages":"1090-1103"},"PeriodicalIF":8.6,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139946758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-Assisted Adaptive Dynamic-SCLF Decoding of Polar Codes","authors":"Jun Li;Lejia Zhou;Zhengquan Li;Weidong Gao;Ru Ji;Jintao Zhu;Ziyi Liu","doi":"10.1109/TCCN.2024.3349450","DOIUrl":"10.1109/TCCN.2024.3349450","url":null,"abstract":"Recently, the dynamic-successive cancellation list flip (D-SCLF) decoder has been proposed to improve the high-order flipping performance of existing successive cancellation list flip (SCLF) decoders in polar codes decoding. However, the D-SCLF decoder involves a large number of exponential and logarithmic operations, resulting in an exponential increase in computational complexity. To further improve the performance and reduce the average complexity of D-SCLF decoding, the deep learning-assisted adaptive dynamic-SCLF (DL-AD-SCLF) decoding is proposed in this paper. The error metric of D-SCLF decoding is re-derived, and an approximation scheme is proposed to reduce computational complexity. To compensate the loss of performance due to approximation, two learnable parameters are introduced. Customized neural network structures are proposed to optimize these learnable parameters according to the improved error metric by employing deep learning (DL), and the deep learning-assisted dynamic-SCLF (DL-D-SCLF) decoding is proposed. Furthermore, the adaptive list is introduced into the DL-D-SCLF decoding to further reduce decoding complexity. Simulation results show that the proposed decoder performance is improved up to 0.35dB and 0.25dB, the average complexity is reduced by up to 57.65% and 51.48% for single-bit and multi-bit flipping, respectively. Additionally, the proposed decoder exhibits good robustness to changes in code rates, code lengths, and channel conditions.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"10 3","pages":"836-851"},"PeriodicalIF":8.6,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139946757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}