Journal of Communications and Information Networks最新文献

An Efficient Signal Detection Technique for Uplink Massive MIMO-OFDM System over Frequency Selective Channel

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

Frame Generation Algorithm for Differentiated Services in Tactical Communication Networks

Journal of Communications and Information Networks Pub Date : 2025-03-01 DOI: 10.23919/JCIN.2025.10964098

Huaifeng Shi;Chengsheng Pan;Lishang Qin;Yingzhi Wang;Huangjie Lu

{"title":"Frame Generation Algorithm for Differentiated Services in Tactical Communication Networks","authors":"Huaifeng Shi;Chengsheng Pan;Lishang Qin;Yingzhi Wang;Huangjie Lu","doi":"10.23919/JCIN.2025.10964098","DOIUrl":"https://doi.org/10.23919/JCIN.2025.10964098","url":null,"abstract":"In response to the complex and multidimensional nature of converged traffic on heterogeneous links in tactical communication networks, which leads to the difficulty in ensuring the quality of service (QoS) requirements for critical services, a frame generation algorithm for differentiated services (DS-FG) is proposed. DS-FG deploys an adaptive frame generation algorithm based on deep reinforcement learning (DRL-FG) for time-sensitive service, while deploying a high efficient frame generation (HEFG) algorithm for non-time-sensitive service. DRL-FG constructs a reward function by combining the queue status information of time-sensitive service and utilizes deep deterministic policy gradients (DDPG) to train a decision model for adaptive frame generation (AFG) algorithm thresholds. Furthermore, Gaussian noise sampling and prioritized experience replay strategies are employed to enhance model training efficiency and performance, achieving optimal matching between time-sensitive service QoS requirements and frame generation thresholds. Experimental results demonstrate that DS-FG outperforms traditional algorithms, achieving up to 13% improvement in throughput and over 19.7% reduction in average queueing delay for time-sensitive service.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"10 1","pages":"15-25"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821644","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

Feature Correlation Based RF Fingerprinting Identification for Walkie-Talkies Push-to-Talk Communications

Journal of Communications and Information Networks Pub Date : 2025-03-01 DOI: 10.23919/JCIN.2025.10964097

Renhe Fan;Longcheng Li;Pengfei Sun;Yang Yu;Gang Wu

引用次数: 0

Microservice Migration in Hybrid Satellite-Terrestrial Networks for Autonomous Vehicles

Journal of Communications and Information Networks Pub Date : 2025-03-01 DOI: 10.23919/JCIN.2025.10964099

Xin Long;Yuning Jiang;Zixin Wang;Xin Liu;Yuanming Shi;Yong Zhou

{"title":"Microservice Migration in Hybrid Satellite-Terrestrial Networks for Autonomous Vehicles","authors":"Xin Long;Yuning Jiang;Zixin Wang;Xin Liu;Yuanming Shi;Yong Zhou","doi":"10.23919/JCIN.2025.10964099","DOIUrl":"https://doi.org/10.23919/JCIN.2025.10964099","url":null,"abstract":"Autonomous vehicles (AVs) have the potential to enhance road safety, reduce fuel consumption, and alleviate traffic congestion. However, the computational demands of processing large volumes of sensory data for tasks like motion planning and trajectory forecasting impose critical challenges on limited onboard resources of AVs. Mobile edge computing (MEC) offers a solution by offloading these tasks to edge servers located in proximity of the vehicles. When AVs traverse remote areas lacking terrestrial infrastructures, low Earth orbit (LEO) satellites can fill this gap by providing edge computing services. In this paper, we propose a microservice-based framework for MEC-enabled hybrid satellite-terrestrial networks to support AVs. By decomposing monolithic applications into microservices deployed in containers, we enable scalable and flexible computing services. We address the challenges of microservice migration due to the mobility of AVs and LEO satellites by formulating a long-term optimization problem aimed at minimizing task and migration delays. An online Lyapunov-based algorithm is developed to solve this problem, reducing the decision space by scheduling periodic migrations and decomposing it into mixed-integer linear programming. Numerical results demonstrate that our proposed algorithm can achieve a nearly optimal results while maintaining a low execution time.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"10 1","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821646","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

Relaying in Cislunar Telemetry and Telecommand Communication: An Information Value Loss Guided Scheduling Mechanism for Multiple Time-Delay Control Systems 星际遥测和遥控通信中的中继：多时延控制系统的信息值损失引导调度机制

Journal of Communications and Information Networks Pub Date : 2025-03-01 DOI: 10.23919/JCIN.2025.10964104

Zhouyong Hu;Afang Yuan;Qinyu Zhang;Zhihua Yang

引用次数: 0

A Hierarchical Networking and Privacy-Preserving Federated Learning Framework for 5G Networks

Journal of Communications and Information Networks Pub Date : 2025-03-01 DOI: 10.23919/JCIN.2025.10964101

Chen Guo;Fang Cui;Chao Xu;Mohan Su;Zhihao Wang;Hongjia Li

{"title":"A Hierarchical Networking and Privacy-Preserving Federated Learning Framework for 5G Networks","authors":"Chen Guo;Fang Cui;Chao Xu;Mohan Su;Zhihao Wang;Hongjia Li","doi":"10.23919/JCIN.2025.10964101","DOIUrl":"https://doi.org/10.23919/JCIN.2025.10964101","url":null,"abstract":"Artificial intelligence (AI) has been widely envisioned as a key enabler for 5G and beyond networks. To integrate AI into mobile networks, the third generation partnership (3GPP) introduces the network data analytics function (NWDAF) starting from Release 15 to support “in-network” learning and inference, and further supports federated learning (FL) from Release 16 to protect data privacy. However, practical deployment of federated learning in 5G networks still faces challenges of high communication overhead and potential risks of model and data leakage. Motivated by these challenges, we propose a hierarchical networking and privacy-preserving federated learning (HiNP-FL) framework for 5G networks. Specifically, in the HiNP-FL framework, 1) we propose the hierarchical NWDAF based FL mechanism to reduce FL communication overhead in 5G networks; 2) based on multi-party polynomial evaluation (OMPE), we design a FL model and data privacy protection mechanism for the hierarchical FL mechanism; 3) we validate the privacy protection capability of the HiNP-FL framework through privacy analysis, and testify its effectiveness in terms of model accuracy and communication efficiency through extensive experiments.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"10 1","pages":"26-36"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821645","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

Resource Allocation and Performance Analysis in D2D Underlay Cellular Networks with Poisson Cluster Process

Journal of Communications and Information Networks Pub Date : 2025-03-01 DOI: 10.23919/JCIN.2025.10964096

Shuai Chen;Kunqi Guo

引用次数: 0

Coverage-Optimal UAV Deployment and Energy-Efficient Sensor Power Allocation for UAV-Enabled Data Collection 无人机数据采集的覆盖范围优化无人机部署和高能效传感器功率分配

Journal of Communications and Information Networks Pub Date : 2025-03-01 DOI: 10.23919/JCIN.2025.10964100

Dong Wang;Runqin Rong;Guizhi Li

引用次数: 0

Quick and Good: A DRL Based Communication-Caching-Energy Joint Optimization Scheme for Prolonging the Lifetime of UAV Assisted IoE 快速与良好：基于DRL的延长无人机辅助物联网寿命的通信-缓存-能量联合优化方案

Journal of Communications and Information Networks Pub Date : 2024-12-01 DOI: 10.23919/JCIN.2024.10820159

Chun Zhu;Guilong Zhu;Jie Yang;Miao Liu;Zheng Shi

{"title":"Quick and Good: A DRL Based Communication-Caching-Energy Joint Optimization Scheme for Prolonging the Lifetime of UAV Assisted IoE","authors":"Chun Zhu;Guilong Zhu;Jie Yang;Miao Liu;Zheng Shi","doi":"10.23919/JCIN.2024.10820159","DOIUrl":"https://doi.org/10.23919/JCIN.2024.10820159","url":null,"abstract":"The rapid increase in the number of Internet of things (IoT) devices has led to significant access pressure, making network energy consumption and communication load key challenges. Edge caching, cooperative communication, and energy management technologies have proven to be effective in alleviating these issues. This paper investigates a unmanned aerial vehicle (UAV)-assisted Internet of everything (IoE) architecture that integrates caching, communication, and energy management. A collaborative communication-caching-energy optimization scheme is proposed, which involves the joint operation of the UAV and base station (BS) to pre-cache content required by ground users, thus minimizing system energy consumption. We model the joint optimization of content caching, communication, and energy consumption as a Markov decision process (MDP), transforming it into a long-term optimization problem solvable by deep reinforcement learning. Based on the simple deep Q-network (DQN), we design a dynamic content placement strategy that jointly optimizes communication, caching, and energy consumption. Simulation results demonstrate that the proposed method, compared to branch and bound (B&B), particle swarm optimization (PSO), genetic algorithm (GA), and random algorithms, not only approaches the optimal solution most closely, effectively reducing system energy consumption, but also exhibits the lowest time complexity.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"9 4","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912490","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

Detection and Recovery of RFID Harmonic Signal for Wi-Fi Interference Wi-Fi干扰下RFID谐波信号的检测与恢复

Journal of Communications and Information Networks Pub Date : 2024-12-01 DOI: 10.23919/JCIN.2024.10820165

Chao Gong;Ping Li;Panlong Yang;Zhao Lv

引用次数: 0