IEEE Open Journal of the Communications Society最新文献_第8页

A Worst-Case Latency and Age Analysis of Coded Distributed Computing With Unreliable Workers and Periodic Tasks 具有不可靠工作者和周期性任务的编码分布式计算的最坏情况延迟和时长分析

IF 6.3

IEEE Open Journal of the Communications Society Pub Date : 2024-09-11 DOI: 10.1109/OJCOMS.2024.3458802

Federico Chiariotti;Beatriz Soret;Petar Popovski

{"title":"A Worst-Case Latency and Age Analysis of Coded Distributed Computing With Unreliable Workers and Periodic Tasks","authors":"Federico Chiariotti;Beatriz Soret;Petar Popovski","doi":"10.1109/OJCOMS.2024.3458802","DOIUrl":"10.1109/OJCOMS.2024.3458802","url":null,"abstract":"Over the past decade, the deep learning revolution has led to ever-increasing demands for computing power and working memory to support larger and larger neural networks. As this coincided with the end of Moore’s law, distributed solutions have emerged as a natural answer: in particular, the novel Coded Distributed Computing (CDC) paradigm exploits results from coding theory to divide large tasks into redundant sets of smaller subtasks to be processed across multiple workers, making the computation more robust to stragglers and malicious worker nodes. Optimizing the use of these distributed computing resources is critical, as excessive redundancy might impact on performance and energy consumption. This work considers a CDC system receiving periodic tasks, deriving the full distribution of the latency, reliability, and Peak Age of Information (PAoI) under worker diversity and random failures. The CDC system is modeled as a fork-join \u0000<inline-formula> <tex-math>$D/M/(K, N)/L$ </tex-math></inline-formula>\u0000 queue, where only K of the coded N subtasks are necessary to solve the overall task, and workers can hold up to L subtasks in their queues. Our results are useful for resource optimization, showing the relationship between system load, redundancy, and latency, as well as the trade-off between latency, reliability, and age performance.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"5874-5889"},"PeriodicalIF":6.3,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10677483","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209502","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

Sparse Bayesian Learning Using Complex t-Prior for Beam-Domain Massive MIMO Channel Estimation 使用复杂 t 先验的稀疏贝叶斯学习用于波束域大规模多输入多输出信道估计

IF 6.3

IEEE Open Journal of the Communications Society Pub Date : 2024-09-10 DOI: 10.1109/OJCOMS.2024.3457507

Kengo Furuta;Takumi Takahashi;Hideki Ochiai

{"title":"Sparse Bayesian Learning Using Complex t-Prior for Beam-Domain Massive MIMO Channel Estimation","authors":"Kengo Furuta;Takumi Takahashi;Hideki Ochiai","doi":"10.1109/OJCOMS.2024.3457507","DOIUrl":"10.1109/OJCOMS.2024.3457507","url":null,"abstract":"This paper proposes a novel beam-domain channel estimation (CE) algorithm via sparse Bayesian learning (SBL) using complex t-prior for massive multi-user multiple-input multiple-output (MIMO) systems. Due to the sidelobe leakage and insufficient observation resolution resulting from physical constraints, the equivalent channel after digital beamforming at the receiver has a structure with many small but non-zero elements, which cannot be modeled strictly as a sparse signal. To fully capture this pseudo-sparse structure characterized by the signal strength variations among elements, we design a novel SBL algorithm that incorporates a complex t-distribution using a hierarchical Bayesian model. By utilizing a high degree of adaptability of this heavy-tailed prior, it is possible to efficiently learn the signal strength, accounting for elements with non-zero but small values, which is verified by the regularization analysis based on an equivalent optimization problem. The efficacy of the proposed CE algorithm is confirmed by numerical simulations, which show that the proposed method not only significantly outperforms the state-of-the-art (SotA) sparse signal recovery (SSR)-based algorithms but also achieves the performance of a genie-aided scheme over a wide signal-to-noise ratio (SNR) range in both sub-6 GHz and millimeter-wave (mmWave) wireless communication scenarios.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"5905-5920"},"PeriodicalIF":6.3,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10673899","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209503","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

Privacy-Preserving Hierarchical Reinforcement Learning Framework for Task Offloading in Low-Altitude Vehicular Fog Computing 低空车载雾计算任务卸载的隐私保护分层强化学习框架

IF 7.9

IEEE Open Journal of the Communications Society Pub Date : 2024-09-10 DOI: 10.1109/ojcoms.2024.3457023

Zhiwei Wei, Jingxin Mao, Bing Li, Rongqing Zhang

引用次数: 0

LLM-Based Edge Intelligence: A Comprehensive Survey on Architectures, Applications, Security and Trustworthiness 基于 LLM 的边缘智能：关于架构、应用、安全性和可信性的全面调查

IF 6.3

IEEE Open Journal of the Communications Society Pub Date : 2024-09-09 DOI: 10.1109/OJCOMS.2024.3456549

Othmane Friha;Mohamed Amine Ferrag;Burak Kantarci;Burak Cakmak;Arda Ozgun;Nassira Ghoualmi-Zine

{"title":"LLM-Based Edge Intelligence: A Comprehensive Survey on Architectures, Applications, Security and Trustworthiness","authors":"Othmane Friha;Mohamed Amine Ferrag;Burak Kantarci;Burak Cakmak;Arda Ozgun;Nassira Ghoualmi-Zine","doi":"10.1109/OJCOMS.2024.3456549","DOIUrl":"10.1109/OJCOMS.2024.3456549","url":null,"abstract":"The integration of Large Language Models (LLMs) and Edge Intelligence (EI) introduces a groundbreaking paradigm for intelligent edge devices. With their capacity for human-like language processing and generation, LLMs empower edge computing with a powerful set of tools, paving the way for a new era of decentralized intelligence. Yet, a notable research gap exists in obtaining a thorough comprehension of LLM-based EI architectures, which should incorporate crucial elements such as security, optimization, and responsible development. This survey aims to bridge this gap by providing a comprehensive resource for both researchers and practitioners. We explore LLM-based EI architectures in-depth, carefully analyzing state-of-the-art paradigms and design decisions. To facilitate efficient and scalable edge deployments, we perform a comparative analysis of recent optimization and autonomy techniques specifically designed for resource-constrained edge environments. Additionally, we shed light on the extensive potential of LLM-based EI by demonstrating its varied practical applications across a wide range of domains. Acknowledging the utmost importance of security, our survey thoroughly investigates potential vulnerabilities inherent in LLM-based EI deployments. We explore corresponding defense mechanisms to protect the integrity and confidentiality of data processed at the edge. In conclusion, highlighting the essential aspect of trustworthiness, we outline best practices and guiding principles for the responsible development and deployment of these systems. By conducting a comprehensive review of these key components, our survey aims to support the ethical development and strategic implementation of LLM-driven EI, paving the way for its transformative impact on diverse applications.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"5799-5856"},"PeriodicalIF":6.3,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10669603","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209508","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

Harnessing Tullock Contests and Signaling Games: A Novel Weight Assignment Strategy for Ethereum 2.0 利用塔洛克竞赛和信号游戏：以太坊 2.0 的新型权重分配策略

IF 6.3

IEEE Open Journal of the Communications Society Pub Date : 2024-09-06 DOI: 10.1109/OJCOMS.2024.3455769

Daniel Mawunyo Doe;Jing Li;Dusit Niyato;Yuqing Hu;Jun Li;Zhen Gao;Xiao-Ping Zhang;Zhu Han

引用次数: 0

Real-Time Immersive Aerial Video Streaming: A Comprehensive Survey, Benchmarking, and Open Challenges 实时沉浸式空中视频流：全面调查、基准测试和公开挑战

IF 6.3

IEEE Open Journal of the Communications Society Pub Date : 2024-09-06 DOI: 10.1109/OJCOMS.2024.3455763

Mohit K. Sharma;Ibrahim Farhat;Chen-Feng Liu;Nassim Sehad;Wassim Hamidouche;Mérouane Debbah

{"title":"Real-Time Immersive Aerial Video Streaming: A Comprehensive Survey, Benchmarking, and Open Challenges","authors":"Mohit K. Sharma;Ibrahim Farhat;Chen-Feng Liu;Nassim Sehad;Wassim Hamidouche;Mérouane Debbah","doi":"10.1109/OJCOMS.2024.3455763","DOIUrl":"10.1109/OJCOMS.2024.3455763","url":null,"abstract":"Over the past decade, the use of Unmanned Aerial Vehicles (UAVs) has grown significantly due to their agility, maneuverability, and rapid deployability. An important application is the use of UAV-mounted 360-degree cameras for real-time streaming of Omnidirectional Videos (ODVs), enabling immersive experiences with up to six Degrees-of-freedom (6DoF) for applications like remote surveillance and gaming. However, streaming high-resolution ODVs with low latency (below 1 second) over an air-to-ground (A2G) wireless channel faces challenges due to its inherent non-stationarity, impacting the Quality-of-experience (QoE). Limited onboard energy availability and energy consumption variability based on flight parameters add to the complexity. This paper conducts a thorough survey of challenges and research efforts in UAV-based immersive video streaming. First, we outline the end-to-end 360-degree video transmission pipeline, covering coding, packaging, and streaming with a focus on standardization for device and service interoperability. Next, we review the research on optimizing video streaming over UAV-to-ground wireless channels, and present a real testbed demonstrating 360-degree video streaming from a UAV with remote control over a 5G network. To assess performance, a high-resolution 360-degree video dataset captured from UAVs under different conditions is introduced. Encoding schemes like AVC/H.264, HEVC/H.265, VVC/H.266, VP9, and AV1 are evaluated for encoding latency and QoE. Results show that HEVC‘s hardware implementation achieves a good QoE-latency trade-off, while AV1’s software implementation provides superior QoE. The paper concludes with discussions on open challenges and future directions for efficient and low-latency immersive video streaming via UAVs.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"5680-5705"},"PeriodicalIF":6.3,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10668820","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209506","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

Implementation Insights of Robust Dynamic Spectrum Sharing for Heterogeneous Services in Non-Standalone 5G 非孤岛 5G 中异构服务的鲁棒动态频谱共享的实施启示

IF 7.9

IEEE Open Journal of the Communications Society Pub Date : 2024-09-05 DOI: 10.1109/ojcoms.2024.3454700

Marziyeh Karkhaneh, Sajedeh Norouzi, Mohammad R. Abedi, Nader Mokari, Mohammad R. Javan, Hamid Saeedi, Eduard A. Jorswieck

引用次数: 0

Private Data Leakage in Federated Contrastive Learning Networks 联合对比学习网络中的隐私数据泄露

IF 7.9

IEEE Open Journal of the Communications Society Pub Date : 2024-09-04 DOI: 10.1109/ojcoms.2024.3454247

Kongyang Chen, Wenfeng Wang, Zixin Wang, Yao Huang, Yatie Xiao, Wangjun Zhang, Zhipeng Li, Zhefei Guo, Zhucheng Luo, Lin Yin, Haiyan Mai, Xiaoying Wang, Qintai Yang

引用次数: 0

Coherent Detection of MIMO LoRa With Increased Data Rate 提高数据速率的 MIMO LoRa 相干检测

IF 6.3

IEEE Open Journal of the Communications Society Pub Date : 2024-09-04 DOI: 10.1109/OJCOMS.2024.3454454

Luca Rugini;Keya Sardar;Giuseppe Baruffa

引用次数: 0

Spectrum Allocation for Multiuser Terahertz Communication Systems: A Machine Learning Approach 多用户太赫兹通信系统的频谱分配：机器学习方法

IF 6.3

IEEE Open Journal of the Communications Society Pub Date : 2024-09-04 DOI: 10.1109/OJCOMS.2024.3454479

Akram Shafie;Nan Yang;Chunhui Li;Xiangyun Zhou;Trung Q. Duong

{"title":"Spectrum Allocation for Multiuser Terahertz Communication Systems: A Machine Learning Approach","authors":"Akram Shafie;Nan Yang;Chunhui Li;Xiangyun Zhou;Trung Q. Duong","doi":"10.1109/OJCOMS.2024.3454479","DOIUrl":"10.1109/OJCOMS.2024.3454479","url":null,"abstract":"In this paper, we propose a novel spectrum allocation design, leveraging machine learning, for multiuser communication systems operating at the terahertz (THz) band. In this design, we propose to (i) change the bandwidth of sub-bands and (ii) underutilize edge spectra of transmission windows (TWs) where the molecular absorption (MA) coefficient is very high. Different from existing studies, our design is not limited to the scenario where the MA coefficient in the spectrum designated for allocation can be accurately modeled by simply using a piecewise exponential function. We establish a constrained optimization problem and introduce an unsupervised learning approach for its solution. Through offline training, we learn a deep neural network (DNN) using a loss function inspired by the Lagrangian of the established problem. The trained DNN is then employed to derive solutions when multiuser distance parameters are given. Based on numerical analysis, we show that when the MA coefficient in the spectrum designated for allocation exhibits highly non-linear variations, our proposed approach can achieve a higher data rate than that of existing approaches which only attain approximate solutions.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"5857-5873"},"PeriodicalIF":6.3,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10664588","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209510","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