Journal of Communications and Information Networks最新文献

{"title":"Coverage in Cooperative LEO Satellite Networks","authors":"Bodong Shang;Xiangyu Li;Caiguo Li;Zhuhang Li","doi":"10.23919/JCIN.2023.10387244","DOIUrl":"https://doi.org/10.23919/JCIN.2023.10387244","url":null,"abstract":"Low-earth orbit (LEO) satellite networks ignite global wireless connectivity. However, signal outages and co-channel interference limit the coverage in traditional LEO satellite networks where a user is served by a single satellite. This paper explores the possibility of satellite cooperation in the downlink transmissions. Using tools from stochastic geometry, we model and analyze the downlink coverage of a typical user with satellite cooperation under Nakagami fading channels. Moreover, we derive the joint distance distribution of cooperative LEO satellites to the typical user. Our model incorporates fading channels, cooperation among several satellites, satellites’ density and altitude, and co-channel interference. Extensive Monte Carlo simulations are performed to validate analytical results. Simulation and numerical results suggest that coverage with LEO satellites cooperation considerably exceeds coverage without cooperation. Moreover, there are optimal satellite density and satellite altitude that maximize the coverage probability, which gives valuable network design insights.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"8 4","pages":"329-340"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139406603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fundamental Limitation of Semantic Communications: Neural Estimation for Rate-Distortion","authors":"Dongxu Li;Jianhao Huang;Chuan Huang;Xiaoqi Qin;Han Zhang;Ping Zhang","doi":"10.23919/JCIN.2023.10387242","DOIUrl":"https://doi.org/10.23919/JCIN.2023.10387242","url":null,"abstract":"This paper studies the fundamental limit of semantic communications over the discrete memoryless channel. We consider the scenario to send a semantic source consisting of an observation state and its corresponding semantic state, both of which are recovered at the receiver. To derive the performance limitation, we adopt the semantic rate-distortion function (SRDF) to study the relationship among the minimum compression rate, observation distortion, semantic distortion, and channel capacity. For the case with unknown semantic source distribution, while only a set of the source samples is available, we propose a neural-network-based method by leveraging the generative networks to learn the semantic source distribution. Furthermore, for a special case where the semantic state is a deterministic function of the observation, we design a cascade neural network to estimate the SRDF. For the case with perfectly known semantic source distribution, we propose a general Blahut-Arimoto (BA) algorithm to effectively compute the SRDF. Finally, experimental results validate our proposed algorithms for the scenarios with ideal Gaussian semantic source and some practical datasets.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"8 4","pages":"303-318"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139406716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-Based Radio Map for MIMO-OFDM Downlink Precoding","authors":"Wei Wang;Bin Yang;Wei Zhang","doi":"10.23919/JCIN.2023.10272348","DOIUrl":"https://doi.org/10.23919/JCIN.2023.10272348","url":null,"abstract":"Radio map is an advanced technology that mitigates the reliance of multiple-input multiple-output (MIMO) beamforming on channel state information (CSI). In this paper, we introduce the concept of deep learning-based radio map, which is designed to be generated directly from the raw CSI data. In accordance with the conventional CSI acquisition mechanism of MIMO, we first introduce two baseline schemes of radio map, i.e., CSI prediction-based radio map and throughput prediction-based radio map. To fully leverage the powerful inference capability of deep neural networks, we further propose the end-to-end structure that outputs the beamforming vector directly from the location information. The rationale behind the proposed end-to-end structure is to design the neural network using a task-oriented approach, which is achieved by customizing the loss function that quantifies the communication quality. Numerical results show the superiority of the task-oriented design and confirm the potential of deep learning-based radio map in replacing CSI with location information.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"8 3","pages":"203-211"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49953032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Network Meets ChatGPT: Intent Autonomous Management, Control and Operation","authors":"Jingyu Wang;Lei Zhang;Yiran Yang;Zirui Zhuang;Qi Qi;Haifeng Sun;Lu Lu;Junlan Feng;Jianxin Liao","doi":"10.23919/JCIN.2023.10272352","DOIUrl":"https://doi.org/10.23919/JCIN.2023.10272352","url":null,"abstract":"Telecommunication has undergone significant transformations due to the continuous advancements in internet technology, mobile devices, competitive pricing, and changing customer preferences. Specifically, the most recent iteration of OpenAI's large language model chat generative pre-trained transformer (ChatGPT) has the potential to propel innovation and bolster operational performance in the telecommunications sector. Nowadays, the exploration of network resource management, control, and operation is still in the initial stage. In this paper, we propose a novel network artificial intelligence architecture named language model for network traffic (NetLM), a large language model based on a transformer designed to understand sequence structures in the network packet data and capture their underlying dynamics. The continual convergence of knowledge space and artificial intelligence (AI) technologies constitutes the core of intelligent network management and control. Multi-modal representation learning is used to unify the multi-modal information of network indicator data, traffic data, and text data into the same feature space. Furthermore, a NetLM-based control policy generation framework is proposed to refine intent incrementally through different abstraction levels. Finally, some potential cases are provided that NetLM can benefit the telecom industry.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"8 3","pages":"239-255"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49953035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cybertwin Based Cloud Native Networks","authors":"Quan Yu;Dandan Liang;Meng Qin;Jiacheng Chen;Haibo Zhou;Jing Ren;Ying Li;Jun Wu;Yue Gao;Wei Zhang","doi":"10.23919/JCIN.2023.10272347","DOIUrl":"https://doi.org/10.23919/JCIN.2023.10272347","url":null,"abstract":"With the emerging applications of the Internet of things, artificial intelligence, and satellite communications, the future network will be featured as the Internet of everything around the globe. The network heterogeneity, applications cloudification, and personalized user services demand a revolutionary change in the network architecture. With the rapid development of cloud native technology, the new network should support heterogeneous networks and personalized quality of services for users. In this paper, we propose a Cybertwin-based cloud native network (CCNN) that merges the radio access network (RAN), the IP bearer network, and the data center network and is based on the cloud native data center network using Kubernetes as a network operating system for unified virtualization of computing, storage, and network resources, unified scheduling and allocation, and unified operation and management. Then, we propose a fully decoupled RAN architecture that can flexibly and efficiently utilize the resource for personlized user services. We also propose a Cybertwin-based management framework built on Kubernetes for integrated networking, computing and storage resource scheduling. Finally, we design an immunology-inspired intrinsic security architecture with zero trust security system and adaptive defense system. The proposed CCNN is a new network architecture expected to address future generation communications and networks challenges.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"8 3","pages":"187-202"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49953051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance Analysis and Prediction for a Free-Space Optical Communication System under Foggy Absorption","authors":"Jialin Wang;Guanjun Xu;Xiaozong Yu;Zhaohui Song","doi":"10.23919/JCIN.2023.10272351","DOIUrl":"https://doi.org/10.23919/JCIN.2023.10272351","url":null,"abstract":"We analyzed the performance of a freespace optical (FSO) system in this study, considering the combined effects of atmospheric turbulence, fog absorption, and pointing errors. The impacts of atmospheric turbulence and foggy absorption were modeled using the Fisher-Snedecor F distribution and the Gamma distribution, respectively. Next, we derived the probability density function (PDF) and cumulative probability density function of the optical system under these combined effects. Based on these statistical findings, closed-form expressions for various system metrics, such as outage probability, average bit error rate (BER), and ergodic capacity, were derived. Furthermore, we used a deep neural network (DNN) to predict the ergodic capacity of the system, achieving reduced running time and improved accuracy. Finally, the accuracy of the prediction results was validated by comparing them with the analytical results.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"8 3","pages":"231-238"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49953036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Channel Knowledge Map (CKM)-Assisted Multi-UAV Wireless Network: CKM Construction and UAV Placement","authors":"Haoyun Li;Peiming Li;Gaoyuan Cheng;Jie Xu;Junting Chen;Yong Zeng","doi":"10.23919/JCIN.2023.10272353","DOIUrl":"https://doi.org/10.23919/JCIN.2023.10272353","url":null,"abstract":"Channel knowledge map (CKM) has recently emerged as a viable new solution to facilitate the placement and trajectory optimization for unmanned aerial vehicle (UAV) communications, by exploiting the site- and location-specific radio propagation information. This paper investigates a CKM-assisted multi-UAV wireless network, by focusing on the construction and utilization of CKMs for multi-UAV placement optimization. First, we consider the CKM construction problem when data measurements for only a limited number of points are available. Towards this end, we exploit a data-driven interpolation technique, namely the Kriging method, to construct CKMs to characterize the signal propagation environments. Next, we study the multi-UAV placement optimization problem by utilizing the constructed CKMs, in which the multiple UAVs aim to optimize their placement locations to maximize the weighted sum rate with their respectively associated ground base stations (GBSs). However, the weighted sum rate function based on the CKMs is generally non-differentiable, which renders the conventional optimization techniques relying on function derivatives inapplicable. To tackle this issue, we propose a novel iterative algorithm based on derivative-free optimization, in which a series of quadratic functions are iteratively constructed to approximate the objective function under a set of interpolation conditions, and accordingly, the UAVs' placement locations are updated by maximizing the approximate function subject to a trust region constraint. Finally, numerical results are presented to validate the performance of the proposed designs. It is shown that the Kriging method can construct accurate CKMs for UAVs. Furthermore, the proposed derivative-free placement optimization design based on the Kriging-constructed CKMs achieves a weighted sum rate that is close to the optimal exhaustive search design based on ground-truth CKMs, but with much lower implementation complexity. In addition, the proposed design is shown to significantly outperform other benchmark schemes.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"8 3","pages":"256-270"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49953049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CFCS: A Robust and Efficient Collaboration Framework for Automatic Modulation Recognition","authors":"Jian Shi;Xiaohui Yang;Jia Ma;Guangxue Yue","doi":"10.23919/JCIN.2023.10272355","DOIUrl":"https://doi.org/10.23919/JCIN.2023.10272355","url":null,"abstract":"Most of the existing automatic modulation recognition (AMR) studies focus on optimizing the network structure to improve performance, without fully considering cooperation among the basic networks to play their respective advantages. In this paper, we propose a robust and efficient collaboration framework based on the combination scheme (CFCS). This scheme effectively explores the spatial and temporal characteristics of complex signals by associating the advantages of convo- lutional neural network (CNN) and long and short-term memory (LSTM) network. In addition, the robustness of the CFCS is verified by transfer learning. Experiments demonstrate that the recognition rate of CFCS for highorder modulation signals such as 64QAM, 128QAM, and 256QAM is more than 90% at high signal-to-noise ratios (SNRs), and 24 modulation types are effectively identified. Moreover, CFCS was transferred from RML2018.01a to RML2016.10b using transfer learning, which can still be deployed efficiently while reducing the training time by 20%. The CFCS has strong generalization ability and excellent recognition performance.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"8 3","pages":"283-294"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49953047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Penetration Testing with Fine-Grained Control through Deep Reinforcement Learning","authors":"Xiaotong Guo;Jing Ren;Jiangong Zheng;Jianxin Liao;Chao Sun;Hongxi Zhu;Tongyu Song;Sheng Wang;Wei Wang","doi":"10.23919/JCIN.2023.10272349","DOIUrl":"https://doi.org/10.23919/JCIN.2023.10272349","url":null,"abstract":"Penetration testing (PT) is an active method of evaluating the security of a network by simulating various types of cyber attacks in order to identify and exploit vulnerabilities. Traditional PT involves a time-consuming and labor-intensive process that is prone to errors and cannot be easily formulated. Researchers have been investigating the potential of deep reinforcement learning (DRL) to develop automated PT (APT) tools. However, using DRL in APT is challenged by partial observability of the environment and the intractability problem of the huge action space. This paper introduces RLAPT, a novel DRL approach that directly overcomes these challenges and enables intelligent automation of the PT process with precise control. The proposed method exhibits superior efficiency, stability, and scalability in finding the optimal attacking policy on the simulated experiment scenario.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"8 3","pages":"212-220"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49953033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MobileNet-Based IoT Malware Detection with Opcode Features","authors":"Changren Mai;Riqing Liao;Jing Ren;Yuanxiang Gong;Kaibo Zhang;Chiya Zhang","doi":"10.23919/JCIN.2023.10272350","DOIUrl":"https://doi.org/10.23919/JCIN.2023.10272350","url":null,"abstract":"In recent years, with the rapid development of Internet and hardware technologies, the number of Internet of things (IoT) devices has grown exponentially. However, IoT devices are constrained by power consumption, making the security of IoT vulnerable. Malware such as Botnets and Worms poses significant security threats to users and enterprises alike. Deep learning models have demonstrated strong performance in various tasks across different domains, leading to their application in malicious software detection. Nevertheless, due to the power constraints of IoT devices, the well-performanced large models are not suitable for IoT malware detection. In this paper we propose a malware detection method based on Markov images and MobileNet, offering a cost-effective, efficient, and high-performing solution for malware detection. Additionally, this paper innovatively analyzes the robustness of opcode sequences.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"8 3","pages":"221-230"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49953034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}