Journal of Communications and Information Networks最新文献_第3页

Passive Ubiquitous Perception System Oriented to Aerospace-Ground Internet of Things Communication 面向空地物联网通信的无源泛在感知系统

Journal of Communications and Information Networks Pub Date : 2024-09-01 DOI: 10.23919/JCIN.2024.10707113

Hong Hong;Wei Gong;Qiwei Wang

{"title":"Passive Ubiquitous Perception System Oriented to Aerospace-Ground Internet of Things Communication","authors":"Hong Hong;Wei Gong;Qiwei Wang","doi":"10.23919/JCIN.2024.10707113","DOIUrl":"https://doi.org/10.23919/JCIN.2024.10707113","url":null,"abstract":"Because the aerospace-ground Internet no longer relies on deploying infrastructure such as base stations, it has the advantage of all-weather full coverage services that traditional terrestrial networks do not have. However, the traditional global navigation satellite system does not support communication services. The newly developing aerospace network system is still in the construction stage, and there is no applicable solution yet. Passive communication technology is an important method to solve the contradiction between the low battery capacity of the Internet of things (IoT) node and the high energy consumption of communication. It is the development trend of the IoT. However, the current passive technology based on Wi-Fi and other signals cannot achieve arbitrary communication due to the excitation signal acquisition problem. To solve the above two major problems, this paper proposes a passive system design for aerospace-ground IoT communication. The system can use the global navigation signal as excitation signal for backscatter communication. Because the global navigation signal has the characteristics of all-weather and full coverage, this design solves the carrier acquisition problem in previous work. In addition, this paper also proposes a low-power signal detection technology that can detect navigation signals with high precision on passive devices. We evaluate system performance through simulation experiments. The experimental results show that the backscatter system based on global navigation satellite signals can realize efficient communication of IoT nodes.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"9 3","pages":"277-285"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397330","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}

引用次数: 0

StableFP: NN-Based Hardware Fingerprint Extractor for LoRa Device Identification StableFP：用于 LoRa 设备识别的基于 NN 的硬件指纹提取器

Journal of Communications and Information Networks Pub Date : 2024-09-01 DOI: 10.23919/JCIN.2024.10707097

Qianwu Chen;Mingqi Xie;Meng Jin;Xiaohua Tian

{"title":"StableFP: NN-Based Hardware Fingerprint Extractor for LoRa Device Identification","authors":"Qianwu Chen;Mingqi Xie;Meng Jin;Xiaohua Tian","doi":"10.23919/JCIN.2024.10707097","DOIUrl":"https://doi.org/10.23919/JCIN.2024.10707097","url":null,"abstract":"Hardware fingerprint is a new dimension of security mechanisms in low power wide area networks (LPWANs). It is hard to emulate for attackers and does not increase the computing and energy burden of transmitters. long range (LoRa) is a long-range communication technology designed for battery-powered devices. In practice, LoRa is vulnerable to malicious attacks such as replace attack. Therefore, the hardware fingerprint is an excellent supplementary mechanism of LoRa security. However, the variable wireless environment contaminates the extracted fingerprints. The long wireless channel adds a large amount of the environment dependent information to the hardware features extracted from LoRa devices. In this paper, we propose StableFP which is a neural network (NN) based device identifier for long range wide area network (LoRaWAN). StableFP extracts stable and representative hardware features from channel frequency response (CFR) as the fingerprint, and it eliminates the environment dependent information caused by wireless environments. We implement StableFP on a software defined radio (SDR) testbed which consists of 4 commercial LoRa nodes. The result demonstrates that StableFP achieves over 90% identification accuracy in unseen environments under an over 5 dB signal to noise ratio (SNR).","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"9 3","pages":"244-250"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397331","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}

引用次数: 0

Energy-Efficient Dynamic Configurable Datapath Architecture for IoT Devices 面向物联网设备的高能效动态可配置数据路径架构

Journal of Communications and Information Networks Pub Date : 2024-09-01 DOI: 10.23919/JCIN.2024.10707103

Ruizhe Zhang;Junhui Liu;Han Wang;Li Lu

引用次数: 0

Digital-Twin Enabled Time Ahead Resource Allocation for Integrated Fiber-Wireless Connected Vehicular Network 为集成式光纤-无线互联车载网络提供数字-孪生支持的超前时间资源分配

Journal of Communications and Information Networks Pub Date : 2024-09-01 DOI: 10.23919/JCIN.2024.10707104

Akshita Gupta;Saurabh Jaiswal;Martin Maier;Vivek Ashok Bohara;Anand Srivastava

{"title":"Digital-Twin Enabled Time Ahead Resource Allocation for Integrated Fiber-Wireless Connected Vehicular Network","authors":"Akshita Gupta;Saurabh Jaiswal;Martin Maier;Vivek Ashok Bohara;Anand Srivastava","doi":"10.23919/JCIN.2024.10707104","DOIUrl":"https://doi.org/10.23919/JCIN.2024.10707104","url":null,"abstract":"The digital twin (DT) is envisaged as a catalyst for pioneering ecosystems of service provision within an immersive environment born from the convergence of virtual and physical realms. Specifically, DT could enhance the performance of edge-intelligent connected vehicular networks by allocating network resources efficiently based on the key performance indicators (KPIs) of vehicular data traffic. Consequently, this work addresses the key challenge of computation and spectrum resource allocation for vehicular networks. To allocate the optimal resource allocation, we subdivided the problem into: traffic classification, collective learning, and resource allocation scheme. In order to do so, this paper concentrates on two crucial vehicular applications: brake application and lane-change application. We utilize a random forest model to collectively learn vehicular data traffic in the upcoming time slot. Thereafter, a time-ahead resource allocation algorithm is proposed to improve the quality of service (QoS) by intelligently offloading vehicular data traffic to a DT-based integrated fiber-wireless (Fi-Wi) connected vehicular network. We evaluate the performance of the resource allocation strategy in terms of resources required by the network alongside the packet loss rate. It was observed that there was a 44.74% increase in cost as the total computation resources increased from F = 50 to 100 GHz, whereas the PLR of the network decreased by 71.43%.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"9 3","pages":"296-308"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397327","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}

引用次数: 0

Research on Indoor Positioning Technology of WSN based on T-RL Partition Path Model 基于 T-RL 分区路径模型的 WSN 室内定位技术研究

Journal of Communications and Information Networks Pub Date : 2024-09-01 DOI: 10.23919/JCIN.2024.10707101

Wei Wang;Xinlin Wang;Yutong Liu;Yulin Ren;Maozhen Li;Asoke K. Nandi

{"title":"Research on Indoor Positioning Technology of WSN based on T-RL Partition Path Model","authors":"Wei Wang;Xinlin Wang;Yutong Liu;Yulin Ren;Maozhen Li;Asoke K. Nandi","doi":"10.23919/JCIN.2024.10707101","DOIUrl":"https://doi.org/10.23919/JCIN.2024.10707101","url":null,"abstract":"To address the issues of unstable received signal strength indicator (RSSI) and low indoor positioning accuracy caused by walls and obstacles, the propagation conditions of the wireless communication system are categorized into two distinct environments: line-of-sight (LOS) and non-line-of-sight (NLOS). In the LOS environment, the traditional logarithmic path loss model is applied. For the NLOS environment, the impact of walls on signal transmission is considered, leading to the development of a multi-wall path loss model based on the T-RL method, with improvements made to the key parameter, the Fresnel coefficient R. The breakpoint value d = 2.3m in the partitioned model is determined, and the positional coordinates of the unknown nodes are calculated using the trilateration algorithm. Experimental results indicate that the T-RL based multi-wall model improves localization accuracy by 47% in NLOS environments compared to the traditional logarithmic path loss model. The average localization error using the T-RL partitioned path loss model is 0.702 1 m, representing a 55.9% improvement over the logarithmic path loss model and a 16.8% enhancement over the T-RL attenuation multi-wall model, thereby providing better environmental adaptability.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"9 3","pages":"219-232"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397325","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}

引用次数: 0

Front and back cover 封面和封底

Journal of Communications and Information Networks Pub Date : 2024-06-01 DOI: 10.23919/JCIN.2024.10582832

引用次数: 0

Grouping Decision Algorithm for Dynamic Terminals in Random Access Networks 随机接入网络中动态终端的分组决策算法

Journal of Communications and Information Networks Pub Date : 2024-06-01 DOI: 10.23919/JCIN.2024.10582828

Hongliang Sun;Tongfei Chen;Chuangye Zhao;Mengxin Chen

引用次数: 0

WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence WirelessLLM：增强大型语言模型，实现无线智能

Journal of Communications and Information Networks Pub Date : 2024-06-01 DOI: 10.23919/JCIN.2024.10582827

Jiawei Shao;Jingwen Tong;Qiong Wu;Wei Guo;Zijian Li;Zehong Lin;Jun Zhang

{"title":"WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence","authors":"Jiawei Shao;Jingwen Tong;Qiong Wu;Wei Guo;Zijian Li;Zehong Lin;Jun Zhang","doi":"10.23919/JCIN.2024.10582827","DOIUrl":"https://doi.org/10.23919/JCIN.2024.10582827","url":null,"abstract":"The rapid evolution of wireless technologies and the growing complexity of network infrastructures necessitate a paradigm shift in how communication networks are designed, configured, and managed. Recent advancements in large language models (LLMs) have sparked interest in their potential to revolutionize wireless communication systems. However, existing studies on LLMs for wireless systems are limited to a direct application for telecom language understanding. To empower LLMs with knowledge and expertise in the wireless domain, this paper proposes WirelessLLM, a comprehensive framework for adapting and enhancing LLMs to address the unique challenges and requirements of wireless communication networks. We first identify three foundational principles that underpin WirelessLLM: knowledge alignment, knowledge fusion, and knowledge evolution. Then, we investigate the enabling technologies to build WirelessLLM, including prompt engineering, retrieval augmented generation, tool usage, multi-modal pre-training, and domain-specific fine-tuning. Moreover, we present three case studies to demonstrate the practical applicability and benefits of WirelessLLM for solving typical problems in wireless networks. Finally, we conclude this paper by highlighting key challenges and outlining potential avenues for future research.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"9 2","pages":"99-112"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10582827","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141494921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance Analysis of the Reconfigurable Intelligent Surfaces Communication Systems 可重构智能表面通信系统的性能分析

Journal of Communications and Information Networks Pub Date : 2024-06-01 DOI: 10.23919/JCIN.2024.10582831

Omar Abu Ella

引用次数: 0

Deep Learning based Efficient Edge Slicing for System Cost Minimization in Wireless Networks 基于深度学习的高效边缘切片技术，实现无线网络中的系统成本最小化

Journal of Communications and Information Networks Pub Date : 2024-06-01 DOI: 10.23919/JCIN.2024.10582894

Wei Jiang;Daquan Feng;Liping Qian;Yao Sun

{"title":"Deep Learning based Efficient Edge Slicing for System Cost Minimization in Wireless Networks","authors":"Wei Jiang;Daquan Feng;Liping Qian;Yao Sun","doi":"10.23919/JCIN.2024.10582894","DOIUrl":"https://doi.org/10.23919/JCIN.2024.10582894","url":null,"abstract":"It is widely recognized that the future wireless networks are able to efficiently slice heterogeneous resources to provide customized services for various use cases. However, it is challenging to meet the diverse requirements of ever-growing applications, especially the stringent requirements of numerous delay-sensitive and/or computation-intensive applications. To tackle this challenge, we should not only consider user admission control to cope with resource limitations, but also make resource management more intelligent and flexible to meet diverse service needs. Taking advantages of mobile edge computing (MEC) and network slicing, in this paper, we propose deep edge slicing (DES), to jointly optimize user admission control and resource scheduling with the aim of minimizing the system cost while guaranteeing multitudinous quality-of-service (QoS) requirements. Specifically, we first apply a deep reinforcement learning approach to select the optimal set of access users with different service requests for maximizing resource utilization. Then a deep learning algorithm is employed to predict traffic data for allocating the communication and computing resources to different slices in advance. Finally, we realize the dynamic scheduling of heterogeneous resources by solving the optimization problem of minimizing the system cost. Simulation results demonstrate that DES can greatly reduce the system cost compared to other benchmarks.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"9 2","pages":"162-175"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10582894","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141495123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0