IEEE Transactions on Cognitive Communications and Networking最新文献

{"title":"Aerial RIS-Aided Physical Layer Security: Optimal Deployment and Partitioning","authors":"Sultangali Arzykulov;Abdulkadir Celik;Galymzhan Nauryzbayev;Ahmed M. Eltawil","doi":"10.1109/TCCN.2024.3392798","DOIUrl":"10.1109/TCCN.2024.3392798","url":null,"abstract":"We propose a novel approach for enhancing physical layer security (PLS) in wireless networks by utilizing a combination of reconfigurable intelligent surfaces (RIS) and artificial noise (AN). The proposed aerial RIS (A-RIS) concept utilizes a RIS-attached unmanned aerial vehicle (UAV) that hovers over the network area to improve the signal quality for legitimate users and jam that of illegitimate ones. We propose a method of virtually partitioning the RIS, such that the partition phase shifts are configured to improve the intended signal at a legitimate user while simultaneously increasing the impact of AN on illegitimate users. Closed-form (CF) expressions for legitimate and illegitimate users’ ergodic secrecy capacity (ESC) are derived and validated. Then, optimization problems are formulated to maximize network ESC by optimizing the 3D deployment of the A-RIS and RIS portions for users subject to predefined quality-of-service constraints. Simulation results validate CF solutions and demonstrate that the proposed joint A-RIS deployment and partitioning framework can significantly improve network security compared to benchmarks where RIS and AN are separately used without deployment optimization. Additionally, the proposed deployment approaches converge in less than a second using CF optimal RIS portions, making it suitable for dynamic A-RIS deployment.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"10 5","pages":"1867-1882"},"PeriodicalIF":7.4,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140639857","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":"Influence of Noise Uncertainty Source and Model on the SNR Wall of Energy Detection","authors":"Lucas dos Santos Costa;Dayan Adionel Guimarães;Bartolomeu F. Uchôa-Filho","doi":"10.1109/TCCN.2024.3392261","DOIUrl":"10.1109/TCCN.2024.3392261","url":null,"abstract":"Recently, we conducted a comprehensive analysis of energy detection (ED) signal-to-noise ratio wall (SNRw) due to noise uncertainty (NU) in cognitive-radio (CR)-based non-cooperative spectrum sensing (nCSS) and cooperative spectrum sensing (CSS) with soft-decision (SD) and hard-decision (HD) fusion under the k-out-of-M rule. It derived the SNRw for a novel NU source and model adopting a truncated Gaussian NU distribution at the CRs and proposed empirical algorithms for SNRw estimation. Based on it, this article conducts another extensive ED study by deriving new closed-form SNRw expressions combining novel and traditional NU sources and models in nCSS and CSS with SD and HD k-out-of-M rule. Besides the conventional test statistic in CSS with SD, it also considers a more general one that, to our best knowledge, was never studied under NU. This new ED computation improves detection performance when CRs are under unequal noise powers and leads to a more conservative (higher) SNRw when CRs are under unequal NU levels in the novel NU source and model combination. Yet, this article maps new and previous derivations for easier comparisons involving any NU source and model combination, more easily highlighting its advantages. Simulations validate the theoretical findings.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"10 5","pages":"1897-1912"},"PeriodicalIF":7.4,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140634083","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 Intelligent and Adaptive Task Scheduling for 6G: An Intent-Driven Framework","authors":"Qingqing Wang;Sai Zou;Yanglong Sun;Minghui Liwang;Xianbin Wang;Wei Ni","doi":"10.1109/TCCN.2024.3391318","DOIUrl":"10.1109/TCCN.2024.3391318","url":null,"abstract":"A cloud network schedules diverse tasks to multi-access edge computing (MEC) or cloud platforms within dynamic industrial Internet of Things (IIoT). The scheduling is influenced by the diverse intents of different parties, including the time-sensitive nature of device-generated tasks and the energy efficiency of servers. The complexity of this problem under dynamic network conditions is underscored by its nature as a Markov state transition process, typically classified as NP-hard. We introduce an intent-driven intelligent task scheduling approach (IITSA), which models a partially observable Markov decision process (POMDP) and introduces a multi-agent proximal policy optimization (MAPPO) method. We introduce a dynamic adaptive mechanism to effectively address conflicts arising from the temporal requirements and energy limitations associated with various tasks on MEC servers. This mechanism enhances the reward function of MAPPO, for which we offer comprehensive mathematical analysis to validate its convergence performance. Simulation results showcase that our proposed IITSA effectively achieves a harmonious trade-off between time-sensitive demands and infrastructure energy efficiency while exhibiting high adaptability. Compared to state-of-the-art algorithms like MADDPG and QMIX, IITSA reduces energy consumption by 11.68% and 7.07%, and enhances on-time completion numbers for time-sensitive tasks by 18.33% and 12.17%, respectively.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"10 5","pages":"1975-1988"},"PeriodicalIF":7.4,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140621664","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":"Binarized ResNet: Enabling Robust Automatic Modulation Classification at the Resource-Constrained Edge","authors":"Nitin Priyadarshini Shankar;Deepsayan Sadhukhan;Nancy Nayak;Thulasi Tholeti;Sheetal Kalyani","doi":"10.1109/TCCN.2024.3391325","DOIUrl":"https://doi.org/10.1109/TCCN.2024.3391325","url":null,"abstract":"Recently, Deep Neural Networks (DNNs) have been used extensively for Automatic Modulation Classification (AMC). Due to their high complexity, DNNs are typically unsuitable for deployment at resource-constrained edge networks. They are also vulnerable to adversarial attacks, which is a significant security concern. This work proposes a Rotated Binary Large ResNet (RBLResNet) for AMC that can be deployed at the edge network because of its low complexity. The performance gap between the RBLResNet and existing architectures with floating-point weights and activations can be closed by two proposed ensemble methods: (i) Multilevel Classification (MC) and (ii) bagging multiple RBLResNets. The MC method achieves an accuracy of 93.39% at 10dB over all the 24 modulation classes of the Deepsig dataset. This performance is comparable to state-of-the-art performances, with 4.75 times lower memory and 1214 times lower computation. Furthermore, RBLResNet exhibits high adversarial robustness compared to existing DNN models. The proposed MC method employing RBLResNets demonstrates a notable adversarial accuracy of 87.25% across a diverse spectrum of Signal-to-Noise Ratios (SNRs), outperforming existing methods and well-established defense mechanisms to the best of our knowledge. Low memory, low computation, and the highest adversarial robustness make it a better choice for robust AMC in low-power edge devices.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"10 5","pages":"1913-1927"},"PeriodicalIF":7.4,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142409008","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":"Learning and Model-Based Approaches for Radar Target Detection","authors":"Ahmadreza Salehi;Maryam Imani;Amir Zaimbashi;Halim Yanikomeroglu","doi":"10.1109/TCCN.2024.3391327","DOIUrl":"10.1109/TCCN.2024.3391327","url":null,"abstract":"This paper addresses the active radar target detection problem using two different approaches: learning-based and model-based methods. The learning-based approach uses a convolutional neural network (CNN) to detect targets, while the model-based approach employs detection theory to design detectors. The detection theory framework is used to consider the subspace-based generalized likelihood ratio test (S-GLRT) and sample covariance matrix-based GLRT (SCM-GLRT) detectors. A new recursive implementation of the S-GLRT, called RS-GLRT, is proposed to address the possible ill-conditioning in the clutter cancelation stage of the S-GLRT detector. In addition, two new detectors are proposed by combining the detection theory and kernel theory frameworks, which enables the deployment of a richer feature space in the detection and improves the detection performance. A CNN-based detector is also presented, which provides a robust detector against diverse noise and clutter behaviors in various environments. To achieve this, a universal model is considered for receiver noise and clutter, known as the \u0000<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>\u0000-stable interference model, which allows for the correct definition of noise and clutter properties in the range of impulsive to Gaussian distributions. Extensive simulation results are presented, demonstrating the superior detection performance of the CNN-based method compared to the detection theory-based methods.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"10 5","pages":"1817-1830"},"PeriodicalIF":7.4,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140621535","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":"Integrated Sensing and Communication Signal Processing Based on Compressed Sensing Over Unlicensed Spectrum Bands","authors":"Haotian Liu;Zhiqing Wei;Fengyun Li;Yuewei Lin;Hanyang Qu;Huici Wu;Zhiyong Feng","doi":"10.1109/TCCN.2024.3391307","DOIUrl":"10.1109/TCCN.2024.3391307","url":null,"abstract":"As a promising key technology of 6th generation (6G) mobile communication system, integrated sensing and communication (ISAC) technology aims to make full use of spectrum resources to enable the functional integration of communication and sensing. The ISAC-enabled mobile communication system regularly operate in non-continuous spectrum bands due to crowded licensed frequency bands. However, the conventional sensing algorithms over non-continuous spectrum bands have disadvantages such as reduced peak-to-sidelobe ratio (PSLR) and degraded anti-noise performance. Facing this challenge, we propose a high-precision ISAC signal processing algorithm based on compressed sensing (CS) in this paper. By integrating the resource block group (RBG) configuration information in 5th generation new radio (5G NR) and channel information matrices, we can dynamically and accurately obtain power estimation spectra. Moreover, we employ the fast iterative shrinkage-thresholding algorithm (FISTA) to address the reconstruction problem and utilize K-fold cross validation (KCV) to obtain optimal parameters. Simulation results show that the proposed algorithm has lower sidelobes or even zero sidelobes compared with conventional sensing algorithms. Meanwhile, compared with the improved 2D FFT algorithm and conventional 2D FFT algorithm, the proposed algorithms in this paper have a maximum improvement of 54.66% and 84.36% in range estimation accuracy, and 41.54% and 97.09% in velocity estimation accuracy, respectively.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"10 5","pages":"1801-1816"},"PeriodicalIF":7.4,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140621625","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":"Collaborative Learning-Based Spectrum Sensing Under Partial Observations","authors":"Weishan Zhang;Yue Wang;Xiang Chen;Lingjia Liu;Zhi Tian","doi":"10.1109/TCCN.2024.3391320","DOIUrl":"10.1109/TCCN.2024.3391320","url":null,"abstract":"To deal with the complex wireless cognitive radios, data-driven learning technologies have been advocated for spectrum sensing. While the existing learning-based methods are designed for basic single-band circumstances, they may not work well in practical wideband regimes. Due to the limited sensing capability and hardware constraints of practical secondary users (SUs) devices, individual SUs can only collect limited training data to observe a narrowband part of the entire wideband spectrum pool. It is known as the issue of partial observations, which leads to a heterogeneous multi-task learning problem. To overcome these challenges, this work proposes a novel framework of cooperative spectrum sensing via collaborative learning among distributed SUs. Capitalizing on the hierarchical nature of neurons of deep neural networks (DNN) in heterogeneous feature extraction, we propose a novel multi-task DNN architecture to detect wideband spectrum occupancy accurately and efficiently. By decoupling the large multi-band DNN into smaller band-specific sub-networks, these sub-networks can be jointly trained among distributed SUs even with heterogeneous local data. Simulation results indicate that our proposed method outperforms existing benchmarks in small-data regimes by achieving higher learning accuracy with less model complexity and computational cost.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"10 5","pages":"1843-1855"},"PeriodicalIF":7.4,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140621640","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":"A Novel Privacy-Preserving Incentive Mechanism for Multi-Access Edge Computing","authors":"Feiran You;Xin Yuan;Wei Ni;Abbas Jamalipour","doi":"10.1109/TCCN.2024.3391303","DOIUrl":"10.1109/TCCN.2024.3391303","url":null,"abstract":"Multi-access Edge Computing (MEC) has emerged as a promising solution for computation-intensive and latency-sensitive applications. Existing studies have often overlooked the critical aspect of users’ privacy, hindering users from offloading their computation. This paper proposes a novel privacy-preserving mechanism for a two-level auction game aimed at incentivizing cloudlets and users to engage in computation offloading while safeguarding users’ privacy. A many-to-many auction is designed between Data Center Operators (DCOs) and cloudlets to associate the cloudlets with the DCOs, where the perceivable privacy levels of users are parameterized as part of a DCO’s utility. A many-to-one user-DCO auction is also designed, leveraging differential privacy (DP) to protect the users’ private bid information. An exponential mechanism is developed, obfuscating intermediate reference prices disclosed during auctions by the DCOs, thereby safeguarding users’ valuations, bid prices, and bidding behaviors. We prove that the proposed approach can guarantee DP, truthfulness, and equilibriums. Simulations demonstrate the superiority of the privacy-preserving two-layer auction game in reducing time delay and energy consumption while protecting the privacy of the users, surpassing the benchmark. The proposed mechanism effectively incentivizes computation offloading, making it a compelling choice for facilitating computation-intensive tasks.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"10 5","pages":"1928-1943"},"PeriodicalIF":7.4,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140621467","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-Agent Reinforcement Learning-Based Joint Caching and Routing in Heterogeneous Networks","authors":"Meiyi Yang;Deyun Gao;Chuan Heng Foh;Wei Quan;Victor C. M. Leung","doi":"10.1109/TCCN.2024.3391322","DOIUrl":"10.1109/TCCN.2024.3391322","url":null,"abstract":"In this paper, we explore the problem of minimizing transmission cost among cooperative nodes by jointly optimizing caching and routing in a hybrid network with vital support of service differentiation. We show that the optimal routing policy is a \u0000<italic>route-to-least cost-cache</i>\u0000 (RLC) policy for fixed caching policy. We formulate the cooperative caching problem as a multi-agent Markov decision process (MDP) with the goal of maximizing the long-term expected caching reward, which is NP-complete even when assuming users’ demand is perfectly known. To solve this problem, we propose C-MAAC, a partially decentralized multi-agent deep reinforcement learning (MADRL)-based collaborative caching algorithm employing actor-critic learning model. C-MAAC has a key characteristic of centralized training and decentralized execution, with which the challenge from unstable training process caused by simultaneous decision made by all agents can be addressed. Furthermore, we develop an optimization method as a criterion for our MADRL framework when assuming the content popularity is stationary and prior known. Our experimental results demonstrate that compared with the prior art, C-MAAC increases an average of 21.7% caching reward in dynamic environment when user request traffic changes rapidly.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"10 5","pages":"1959-1974"},"PeriodicalIF":7.4,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140621486","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":"Coded Distributed Computing for Resilient, Secure and Private Matrix-Vector Multiplication in Edge-Enabled Metaverse","authors":"Houming Qiu;Kun Zhu;Nguyen Cong Luong;Dusit Niyato;Qiang Wu","doi":"10.1109/TCCN.2024.3391317","DOIUrl":"10.1109/TCCN.2024.3391317","url":null,"abstract":"Metaverse is an immersive and photorealistic shared virtual world that requires efficient rendering and processing of millions of virtual objects and scenes. This leads to the requirements of computing time-sensitive and computation-intensive tasks, primarily focused on matrix multiplication. Cloud computing can be leveraged to process computation-intensive tasks. However, it is not able to meet the ultra-low latency requirements of immersive experiences due to the remote servers. In this paper, we propose an effectively distributed edge computing framework to compute high-dimensional matrix multiplication for the Metaverse. With the distributed edge computing, the high-dimensional matrix multiplication task is divided into multiple smaller subtasks, which are then assigned to nearby edge servers (workers). However, leveraging distributed edge servers raises emerging issues due to the existence of stragglers, malicious, and colluding servers, which limits the applications of distributed edge computing in the Metaverse system. Thus, we design a resilient, secure, and private coded distributed computing (RSPCDC) scheme to jointly address the aforementioned issues. Firstly, the RSPCDC scheme reduces overall computation latency by lowering the recovery threshold. Secondly, to identify malicious (e.g., Byzantine) workers, a verification approach is embedded in the scheme to promptly detect the Byzantine attack without requiring additional workers. Thirdly, the RSPCDC scheme provides (information-theoretic) privacy protection for the input data against the collusion of workers. Fourthly, the results of subtasks computed by stragglers are fully utilized to enhance the computation performance during the recovery of the final result. In addition, the RSPCDC scheme is designed and deployed in practical scenarios in which the computing resources of the workers are heterogeneous. Extensive performance evaluations are provided to demonstrate the improvement and effectiveness of the proposed RSPCDC scheme in comparison to existing schemes.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"10 5","pages":"1944-1958"},"PeriodicalIF":7.4,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140621689","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}