Journal of Communications and Information Networks最新文献

FANETs in Low-Altitude Space: A Q-Learning Enabled Routing Algorithm with Visual Information 低空空间的飞碟：一种具有视觉信息的q -学习路由算法

Journal of Communications and Information Networks Pub Date : 2025-06-01 DOI: 10.23919/JCIN.2025.11083698

Haoran Shen;Jingzheng Chong;Zhihua Yang

引用次数: 0

Reactive Jamming Adaptation Technique Based on Tandem Spreading Multiple Access 基于串列扩频多址的无功干扰自适应技术

Journal of Communications and Information Networks Pub Date : 2025-06-01 DOI: 10.23919/JCIN.2025.11083700

Hangyu Zheng;Guoyu Ma;Yiyan Ma;Yuhang Gao;Xue Bai;Bo Ai

引用次数: 0

Localization of RF Emitters using Convolutional Neural Networks under Sparse Prior 稀疏先验下卷积神经网络射频发射器定位

Journal of Communications and Information Networks Pub Date : 2025-06-01 DOI: 10.23919/JCIN.2025.11083703

Wei Guo;Huan Wang;Yanqing Yang;Rong Yuan;Yudong Fang;Wenchi Cheng

引用次数: 0

An Efficient Channel Feedback and Transmission Scheme for Large-Scale MIMO Ad Hoc Networks 大规模MIMO自组织网络中一种有效的信道反馈与传输方案

Journal of Communications and Information Networks Pub Date : 2025-06-01 DOI: 10.23919/JCIN.2025.11083696

Jiaxing Wang;Xin Xie;Rui Han;Guowei Shi;Lin Bai

引用次数: 0

Toward Intelligent Space-Air-Ground Integrated Network: Architecture, Challenges, and Emerging Directions 迈向智能天空地一体化网络：体系结构、挑战和新兴方向

Journal of Communications and Information Networks Pub Date : 2025-06-01 DOI: 10.23919/JCIN.2025.11083695

Lina Wang;Mingrui Fan;Ning Yang;Xu Ma;Yan Liang;Haijun Zhang

{"title":"Toward Intelligent Space-Air-Ground Integrated Network: Architecture, Challenges, and Emerging Directions","authors":"Lina Wang;Mingrui Fan;Ning Yang;Xu Ma;Yan Liang;Haijun Zhang","doi":"10.23919/JCIN.2025.11083695","DOIUrl":"https://doi.org/10.23919/JCIN.2025.11083695","url":null,"abstract":"As a foundational architecture for next generation communication systems, the space-air-ground integrated network (SAGIN) is driving the evolution toward global, resilient, and task-aware connectivity. However, SAGIN is characterized by highly dynamic topologies, heterogeneous nodes, and strong task-driven demands, which pose unprecedented challenges to the real-time performance of scheduling strategies. Artificial intelligence (AI) technologies, particularly reinforcement learning, deep neural networks, and large-scale models, provide promising solutions to these structural bottlenecks. This paper introduces a system-layer-oriented AI capability alignment framework to map model architectures to communication demands, and analyzes key deployment challenges including edge inference, policy consistency, and cross-domain knowledge transfer. We also present a comprehensive review of AI-driven applications in SAGIN, with a focus on critical tasks such as link and routing selection, resource scheduling, traffic offloading, unmanned aerial vehicles (UAV) deployment optimization, semantic communication, and large model integration. Based on this review, the paper outlines future research trends and identifies core technical bottlenecks. The goal is to provide methodological guidance and a development roadmap for building a new generation of intelligent, scalable, and cross-domain adaptive SAGIN architectures.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"10 2","pages":"87-102"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11083695","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646558","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

Adaptive Power Control for Dense RFID Networks 密集RFID网络的自适应功率控制

Journal of Communications and Information Networks Pub Date : 2025-06-01 DOI: 10.23919/JCIN.2025.11083699

Bernard Amoah;Xiangyu Wang;Jian Zhang;Shiwen Mao;Senthilkumar C. G. Periaswamy;Justin Patton

{"title":"Adaptive Power Control for Dense RFID Networks","authors":"Bernard Amoah;Xiangyu Wang;Jian Zhang;Shiwen Mao;Senthilkumar C. G. Periaswamy;Justin Patton","doi":"10.23919/JCIN.2025.11083699","DOIUrl":"https://doi.org/10.23919/JCIN.2025.11083699","url":null,"abstract":"Adaptive power control is a critical challenge in dense radio frequency identification (RFID) environments, where uncontrolled power levels can lead to excessive interference, energy inefficiency, and reduced system performance. This paper presents a robust and scalable adaptive power control framework that dynamically adjusts transmit power levels to optimize energy efficiency, minimize interference, and enhance system throughput. The proposed framework leverages an optimization-driven approach based on real-time environmental feedback, ensuring compliance with regulatory constraints while maintaining optimal performance. A multi-objective optimization strategy is employed to balance several key metrics, including throughput, energy consumption, and fairness, with a Pareto front analysis demonstrating superior trade-offs compared to fixed power strategies. The effectiveness of the proposed approach is validated through extensive simulations and real-world experiments using universal software radio peripheral (USRP) devices in dense RFID deployments. The results show that our framework achieves a 34% reduction in cumulative interference, a 15% improvement in energy efficiency, and a 20% increase in throughput compared to baseline fixed power methods. Furthermore, it converges rapidly, even in dynamic and high-density networks. These improvements make it highly scalable and adaptable to varying reader densities, ensuring reliable performance in large-scale RFID applications such as supply chain management and industrial automation.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"10 2","pages":"103-122"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646557","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

DF-RL: A Dynamic Fuzzy-Neuro Reinforcement Learning Framework for Cloud Resource Management DF-RL：云资源管理的动态模糊神经强化学习框架

Journal of Communications and Information Networks Pub Date : 2025-06-01 DOI: 10.23919/JCIN.2025.11083697

Chunmao Jiang;Xinyu Lin

引用次数: 0

An OAI Based Cybertwin Networks Platform 基于OAI的网络孪生平台

Journal of Communications and Information Networks Pub Date : 2025-06-01 DOI: 10.23919/JCIN.2025.11083704

Enjin Zhou;Penghui Shen;Hui Liang;Zhihui Wu

{"title":"An OAI Based Cybertwin Networks Platform","authors":"Enjin Zhou;Penghui Shen;Hui Liang;Zhihui Wu","doi":"10.23919/JCIN.2025.11083704","DOIUrl":"https://doi.org/10.23919/JCIN.2025.11083704","url":null,"abstract":"Modern wireless networks must support numerous connected devices with diverse quality of service (QoS) and security requirements, posing challenges for traditional architectures. Cybertwin technology offers a novel approach by leveraging digital proxies to explore the potential of bridging the physical and virtual worlds. This paper introduces the programmable and flexible platform for cybertwin networks (PFPCTN), implemented using the open-source OpenAirInterface (OAI) framework. The platform integrates multiple universal software radio peripheral (USRP) devices and a containerized core network to create a software-defined, multi-vendor experimental environment, providing essential infrastructure for exploring cybertwin functionalities. Laboratory experiments demonstrate stable operation under moderate traffic, supporting up to eight cybertwin-assisted user entities (CTs), achieving over 100 Mbit/s downlink throughput on a 40 MHz bandwidth, and enabling smooth 1080P video playback at extended distances. To the best of our knowledge, this is the first fully programmable, open-source 5G testbed built on software-defined radio, integrating preliminary cybertwin interfaces and providing a foundation for future in-depth studies. Future plans include outdoor deployments, higher frequency bands, and AI-driven experimentation.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"10 2","pages":"152-162"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646566","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

A Deep Reinforcement Learning Based Dynamic Resource Allocation Approach in Satellite Systems 基于深度强化学习的卫星系统动态资源分配方法

Journal of Communications and Information Networks Pub Date : 2025-06-01 DOI: 10.23919/JCIN.2025.11083701

Junyang Zhou;Yunxiao Wan;Yurui Li;Jian Wang

{"title":"A Deep Reinforcement Learning Based Dynamic Resource Allocation Approach in Satellite Systems","authors":"Junyang Zhou;Yunxiao Wan;Yurui Li;Jian Wang","doi":"10.23919/JCIN.2025.11083701","DOIUrl":"https://doi.org/10.23919/JCIN.2025.11083701","url":null,"abstract":"Efficient resource allocation in space information networks (SINs) is crucial for providing global connectivity but is challenged by constrained satellite resources and dynamic user demand. While dynamic channel allocation techniques exist, they often fail to handle complex, multi-faceted resource constraints in practical scenarios. To address this issue, this paper introduces a deep reinforcement learning based dynamic resource allocation (DDRA) algorithm. The DDRA formulates the allocation problem as a Markov decision process and employs deep Q-network (DQN) to learn an optimal policy for assigning channel, power, and traffic resources. We developed a simulation environment in ns-3 to evaluate the DDRA algorithm against traditional fixed and greedy random allocation methods. The results demonstrate that the DDRA algorithm significantly outperforms these baselines, achieving substantially lower service blocking rates and higher traffic satisfaction rates across various user demand scenarios. This work validates the potential of DRL to create intelligent, adaptive resource management systems for next-generation satellite networks.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"10 2","pages":"183-190"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646637","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

An Efficient Signal Detection Technique for Uplink Massive MIMO-OFDM System over Frequency Selective Channel 选频信道上行海量MIMO-OFDM系统的有效信号检测技术

Journal of Communications and Information Networks Pub Date : 2025-03-01 DOI: 10.23919/JCIN.2025.10964102

Jyoti P. Patra;Bibhuti Bhusan Pradhan;Ranjan Kumar Mahapatra;Sankata Bhanjan Prusty

{"title":"An Efficient Signal Detection Technique for Uplink Massive MIMO-OFDM System over Frequency Selective Channel","authors":"Jyoti P. Patra;Bibhuti Bhusan Pradhan;Ranjan Kumar Mahapatra;Sankata Bhanjan Prusty","doi":"10.23919/JCIN.2025.10964102","DOIUrl":"https://doi.org/10.23919/JCIN.2025.10964102","url":null,"abstract":"Signal detection in massive multiple-input multiple-output (m-MIMO) is a challenging task due to high computational complexity. Although, the minimum mean square error (MMSE) method is a popular signal detection, however it involves matrix inversion with complexity of cubic order. Therefore, several linear signal detection methods were developed such as Gauss-Seidel, successive over relaxation, Jacobi method, and Richardson methods to provide a trade-off between performance and complexity. These methods are developed for flat fading scenario, however in practice, the channel is frequency selective rather flat fading. In this paper, we have proposed an efficient signal detection technique based on iterative parallel multistage detection with decision statistics combiner (IPMD-DSC) for uplink m-MIMO-orthogonal frequency division multiplexing (m-MIMO-OFDM) system over frequency selective channel. Finally, the proposed method is compared with several convention methods with respect to bit error rate (BER) and complexity. Simulation results demonstrate that the proposed method outperforms the MMSE method with lower complexity.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"10 1","pages":"81-86"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821864","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