arXiv - CS - Networking and Internet Architecture最新文献_第9页

User-centric Service Provision for Edge-assisted Mobile AR: A Digital Twin-based Approach 为边缘辅助移动 AR 提供以用户为中心的服务：基于数字双胞胎的方法

arXiv - CS - Networking and Internet Architecture Pub Date : 2024-08-31 DOI: arxiv-2409.00324

Conghao Zhou, Jie Gao, Yixiang Liu, Shisheng Hu, Nan Cheng, Xuemin Shen

{"title":"User-centric Service Provision for Edge-assisted Mobile AR: A Digital Twin-based Approach","authors":"Conghao Zhou, Jie Gao, Yixiang Liu, Shisheng Hu, Nan Cheng, Xuemin Shen","doi":"arxiv-2409.00324","DOIUrl":"https://doi.org/arxiv-2409.00324","url":null,"abstract":"Future 6G networks are envisioned to support mobile augmented reality (MAR)\u0000applications and provide customized immersive experiences for users via\u0000advanced service provision. In this paper, we investigate user-centric service\u0000provision for edge-assisted MAR to support the timely camera frame uploading of\u0000an MAR device by optimizing the spectrum resource reservation. To address the\u0000challenge of non-stationary data traffic due to uncertain user movement and the\u0000complex camera frame uploading mechanism, we develop a digital twin (DT)-based\u0000data-driven approach to user-centric service provision. Specifically, we first\u0000establish a hierarchical data model with well-defined data attributes to\u0000characterize the impact of the camera frame uploading mechanism on the\u0000user-specific data traffic. We then design an easy-to-use algorithm to adapt\u0000the data attributes used in traffic modeling to the non-stationary data\u0000traffic. We also derive a closed-form service provision solution tailored to\u0000data-driven traffic modeling with the consideration of potential modeling\u0000inaccuracies. Trace-driven simulation results demonstrate that our DT-based\u0000approach for user-centric service provision outperforms conventional approaches\u0000in terms of adaptivity and robustness.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183937","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 Functional Split in Non-terrestrial Open Radio Access Networks 非地面开放式无线接入网络中的节能功能分区

arXiv - CS - Networking and Internet Architecture Pub Date : 2024-08-31 DOI: arxiv-2409.00466

S. M. Mahdi Shahabi, Xiaonan Deng, Ahmad Qidan, Taisir Elgorashi, Jaafar Elmirghani

引用次数: 0

Time varying channel estimation for RIS assisted network with outdated CSI: Looking beyond coherence time 具有过时 CSI 的 RIS 辅助网络的时变信道估计：超越相干时间的视角

arXiv - CS - Networking and Internet Architecture Pub Date : 2024-08-30 DOI: arxiv-2408.17128

Souvik Deb, Sasthi C. Ghosh

{"title":"Time varying channel estimation for RIS assisted network with outdated CSI: Looking beyond coherence time","authors":"Souvik Deb, Sasthi C. Ghosh","doi":"arxiv-2408.17128","DOIUrl":"https://doi.org/arxiv-2408.17128","url":null,"abstract":"The channel estimation (CE) overhead for unstructured multipath-rich channels\u0000increases linearly with the number of reflective elements of reconfigurable\u0000intelligent surface (RIS). This results in a significant portion of the channel\u0000coherence time being spent on CE, reducing data communication time.\u0000Furthermore, due to the mobility of the user equipment (UE) and the time\u0000consumed during CE, the estimated channel state information (CSI) may become\u0000outdated during actual data communication. In recent studies, the timing for CE\u0000has been primarily determined based on the coherence time interval, which is\u0000dependent on the velocity of the UE. However, the effect of the current channel\u0000condition and pathloss of the UEs can also be utilized to control the duration\u0000between successive CE to reduce the overhead while still maintaining the\u0000quality of service. Furthermore, for muti-user systems, the appropriate\u0000coherence time intervals of different users may be different depending on their\u0000velocities. Therefore CE carried out ignoring the difference in coherence time\u0000of different UEs may result in the estimated CSI being detrimentally outdated\u0000for some users. In contrast, others may not have sufficient time for data\u0000communication. To this end, based on the throughput analysis on outdated CSI,\u0000an algorithm has been designed to dynamically predict the next time instant for\u0000CE after the current CSI acquisition. In the first step, optimal RIS phase\u0000shifts to maximise channel gain is computed. Based on this and the amount of\u0000degradation of SINR due to outdated CSI, transmit powers are allocated for the\u0000UEs and finally the next time instant for CE is predicted such that the\u0000aggregated throughput is maximized. Simulation results confirm that our\u0000proposed algorithm outperforms the coherence time-based strategies.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183942","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

Prioritized Information Bottleneck Theoretic Framework with Distributed Online Learning for Edge Video Analytics 针对边缘视频分析的分布式在线学习优先信息瓶颈理论框架

arXiv - CS - Networking and Internet Architecture Pub Date : 2024-08-30 DOI: arxiv-2409.00146

Zhengru Fang, Senkang Hu, Jingjing Wang, Yiqin Deng, Xianhao Chen, Yuguang Fang

{"title":"Prioritized Information Bottleneck Theoretic Framework with Distributed Online Learning for Edge Video Analytics","authors":"Zhengru Fang, Senkang Hu, Jingjing Wang, Yiqin Deng, Xianhao Chen, Yuguang Fang","doi":"arxiv-2409.00146","DOIUrl":"https://doi.org/arxiv-2409.00146","url":null,"abstract":"Collaborative perception systems leverage multiple edge devices, such\u0000surveillance cameras or autonomous cars, to enhance sensing quality and\u0000eliminate blind spots. Despite their advantages, challenges such as limited\u0000channel capacity and data redundancy impede their effectiveness. To address\u0000these issues, we introduce the Prioritized Information Bottleneck (PIB)\u0000framework for edge video analytics. This framework prioritizes the shared data\u0000based on the signal-to-noise ratio (SNR) and camera coverage of the region of\u0000interest (RoI), reducing spatial-temporal data redundancy to transmit only\u0000essential information. This strategy avoids the need for video reconstruction\u0000at edge servers and maintains low latency. It leverages a deterministic\u0000information bottleneck method to extract compact, relevant features, balancing\u0000informativeness and communication costs. For high-dimensional data, we apply\u0000variational approximations for practical optimization. To reduce communication\u0000costs in fluctuating connections, we propose a gate mechanism based on\u0000distributed online learning (DOL) to filter out less informative messages and\u0000efficiently select edge servers. Moreover, we establish the asymptotic\u0000optimality of DOL by proving the sublinearity of their regrets. Compared to\u0000five coding methods for image and video compression, PIB improves mean object\u0000detection accuracy (MODA) while reducing 17.8% and reduces communication costs\u0000by 82.80% under poor channel conditions.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183961","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

Deadline and Priority Constrained Immersive Video Streaming Transmission Scheduling 有期限和优先级限制的沉浸式视频流传输调度

arXiv - CS - Networking and Internet Architecture Pub Date : 2024-08-30 DOI: arxiv-2408.17028

Tongtong Feng, Qi Qi, Bo He, Jingyu Wang

{"title":"Deadline and Priority Constrained Immersive Video Streaming Transmission Scheduling","authors":"Tongtong Feng, Qi Qi, Bo He, Jingyu Wang","doi":"arxiv-2408.17028","DOIUrl":"https://doi.org/arxiv-2408.17028","url":null,"abstract":"Deadline-aware transmission scheduling in immersive video streaming is\u0000crucial. The objective is to guarantee that at least a certain block in\u0000multi-links is fully delivered within their deadlines, which is referred to as\u0000delivery ratio. Compared with existing models that focus on maximizing\u0000throughput and ultra-low latency, which makes bandwidth resource allocation and\u0000user satisfaction locally optimized, immersive video streaming needs to\u0000guarantee more high-priority block delivery within personalized deadlines. In\u0000this paper, we propose a deadline and priority-constrained immersive video\u0000streaming transmission scheduling scheme. It builds an accurate bandwidth\u0000prediction model that can sensitively assist scheduling decisions. It divides\u0000video streaming into various media elements and performs scheduling based on\u0000the user's personalized latency sensitivity thresholds and the media element's\u0000priority. We evaluate our scheme via trace-driven simulations. Compared with\u0000existing models, the results further demonstrate the superiority of our scheme\u0000with 12{%}-31{%} gains in quality of experience (QoE).","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183944","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

PIB: Prioritized Information Bottleneck Framework for Collaborative Edge Video Analytics PIB：用于协作式边缘视频分析的优先信息瓶颈框架

arXiv - CS - Networking and Internet Architecture Pub Date : 2024-08-30 DOI: arxiv-2408.17047

Zhengru Fang, Senkang Hu, Liyan Yang, Yiqin Deng, Xianhao Chen, Yuguang Fang

{"title":"PIB: Prioritized Information Bottleneck Framework for Collaborative Edge Video Analytics","authors":"Zhengru Fang, Senkang Hu, Liyan Yang, Yiqin Deng, Xianhao Chen, Yuguang Fang","doi":"arxiv-2408.17047","DOIUrl":"https://doi.org/arxiv-2408.17047","url":null,"abstract":"Collaborative edge sensing systems, particularly in collaborative perception\u0000systems in autonomous driving, can significantly enhance tracking accuracy and\u0000reduce blind spots with multi-view sensing capabilities. However, their limited\u0000channel capacity and the redundancy in sensory data pose significant\u0000challenges, affecting the performance of collaborative inference tasks. To\u0000tackle these issues, we introduce a Prioritized Information Bottleneck (PIB)\u0000framework for collaborative edge video analytics. We first propose a\u0000priority-based inference mechanism that jointly considers the signal-to-noise\u0000ratio (SNR) and the camera's coverage area of the region of interest (RoI). To\u0000enable efficient inference, PIB reduces video redundancy in both spatial and\u0000temporal domains and transmits only the essential information for the\u0000downstream inference tasks. This eliminates the need to reconstruct videos on\u0000the edge server while maintaining low latency. Specifically, it derives\u0000compact, task-relevant features by employing the deterministic information\u0000bottleneck (IB) method, which strikes a balance between feature informativeness\u0000and communication costs. Given the computational challenges caused by IB-based\u0000objectives with high-dimensional data, we resort to variational approximations\u0000for feasible optimization. Compared to TOCOM-TEM, JPEG, and HEVC, PIB achieves\u0000an improvement of up to 15.1% in mean object detection accuracy (MODA) and\u0000reduces communication costs by 66.7% when edge cameras experience poor channel\u0000conditions.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183943","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

Reasoning AI Performance Degradation in 6G Networks with Large Language Models 利用大型语言模型推理 6G 网络中的人工智能性能下降问题

arXiv - CS - Networking and Internet Architecture Pub Date : 2024-08-30 DOI: arxiv-2408.17097

Liming Huang, Yulei Wu, Dimitra Simeonidou

{"title":"Reasoning AI Performance Degradation in 6G Networks with Large Language Models","authors":"Liming Huang, Yulei Wu, Dimitra Simeonidou","doi":"arxiv-2408.17097","DOIUrl":"https://doi.org/arxiv-2408.17097","url":null,"abstract":"The integration of Artificial Intelligence (AI) within 6G networks is poised\u0000to revolutionize connectivity, reliability, and intelligent decision-making.\u0000However, the performance of AI models in these networks is crucial, as any\u0000decline can significantly impact network efficiency and the services it\u0000supports. Understanding the root causes of performance degradation is essential\u0000for maintaining optimal network functionality. In this paper, we propose a\u0000novel approach to reason about AI model performance degradation in 6G networks\u0000using the Large Language Models (LLMs) empowered Chain-of-Thought (CoT) method.\u0000Our approach employs an LLM as a ''teacher'' model through zero-shot prompting\u0000to generate teaching CoT rationales, followed by a CoT ''student'' model that\u0000is fine-tuned by the generated teaching data for learning to reason about\u0000performance declines. The efficacy of this model is evaluated in a real-world\u0000scenario involving a real-time 3D rendering task with multi-Access Technologies\u0000(mATs) including WiFi, 5G, and LiFi for data transmission. Experimental results\u0000show that our approach achieves over 97% reasoning accuracy on the built test\u0000questions, confirming the validity of our collected dataset and the\u0000effectiveness of the LLM-CoT method. Our findings highlight the potential of\u0000LLMs in enhancing the reliability and efficiency of 6G networks, representing a\u0000significant advancement in the evolution of AI-native network infrastructures.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183974","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

Next-Generation Wi-Fi Networks with Generative AI: Design and Insights 采用生成式人工智能的下一代 Wi-Fi 网络：设计与见解

arXiv - CS - Networking and Internet Architecture Pub Date : 2024-08-09 DOI: arxiv-2408.04835

Jingyu Wang, Xuming Fang, Dusit Niyato, Tie Liu

{"title":"Next-Generation Wi-Fi Networks with Generative AI: Design and Insights","authors":"Jingyu Wang, Xuming Fang, Dusit Niyato, Tie Liu","doi":"arxiv-2408.04835","DOIUrl":"https://doi.org/arxiv-2408.04835","url":null,"abstract":"Generative artificial intelligence (GAI), known for its powerful capabilities\u0000in image and text processing, also holds significant promise for the design and\u0000performance enhancement of future wireless networks. In this article, we\u0000explore the transformative potential of GAI in next-generation Wi-Fi networks,\u0000exploiting its advanced capabilities to address key challenges and improve\u0000overall network performance. We begin by reviewing the development of major\u0000Wi-Fi generations and illustrating the challenges that future Wi-Fi networks\u0000may encounter. We then introduce typical GAI models and detail their potential\u0000capabilities in Wi-Fi network optimization, performance enhancement, and other\u0000applications. Furthermore, we present a case study wherein we propose a\u0000retrieval-augmented LLM (RA-LLM)-enabled Wi-Fi design framework that aids in\u0000problem formulation, which is subsequently solved using a generative diffusion\u0000model (GDM)-based deep reinforcement learning (DRL) framework to optimize\u0000various network parameters. Numerical results demonstrate the effectiveness of\u0000our proposed algorithm in high-density deployment scenarios. Finally, we\u0000provide some potential future research directions for GAI-assisted Wi-Fi\u0000networks.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944404","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 performance of LR-FHSS: analysis and evaluation LR-FHSS 的能源性能：分析与评估

arXiv - CS - Networking and Internet Architecture Pub Date : 2024-08-09 DOI: arxiv-2408.04908

Roger Sanchez-Vital, Lluís Casals, Bartomeu Heer-Salva, Rafael Vidal, Carles Gomez, Eduard Garcia-Villegas

{"title":"Energy performance of LR-FHSS: analysis and evaluation","authors":"Roger Sanchez-Vital, Lluís Casals, Bartomeu Heer-Salva, Rafael Vidal, Carles Gomez, Eduard Garcia-Villegas","doi":"arxiv-2408.04908","DOIUrl":"https://doi.org/arxiv-2408.04908","url":null,"abstract":"Long Range-Frequency Hopping Spread Spectrum (LR-FHSS) is a pivotal\u0000advancement in the LoRaWAN protocol, designed to enhance the network's capacity\u0000and robustness, particularly in densely populated environments. Although energy\u0000consumption is paramount in LoRaWAN-based end-devices, there are currently no\u0000studies in the literature, to our knowledge, that model the impact of this\u0000novel mechanism on energy consumption. In this article, we provide a\u0000comprehensive energy consumption analytical model of LR-FHSS, focusing on three\u0000critical metrics: average current consumption, battery lifetime, and energy\u0000efficiency of data transmission. The model is based on measurements performed\u0000on real hardware in a fully operational LR-FHSS network. While in our\u0000evaluation, LR-FHSS can show worse consumption figures than LoRa, we found that\u0000with optimal configuration, the battery lifetime of LR-FHSS end-devices can\u0000reach 2.5 years for a 50-minute notification period. For the most\u0000energy-efficient payload size, this lifespan can be extended to a theoretical\u0000maximum of up to 16 years with a one-day notification interval using a\u0000cell-coin battery.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944401","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

Overlay-based Decentralized Federated Learning in Bandwidth-limited Networks 带宽受限网络中基于重叠的分散式联合学习

arXiv - CS - Networking and Internet Architecture Pub Date : 2024-08-08 DOI: arxiv-2408.04705

Yudi Huang, Tingyang Sun, Ting He

{"title":"Overlay-based Decentralized Federated Learning in Bandwidth-limited Networks","authors":"Yudi Huang, Tingyang Sun, Ting He","doi":"arxiv-2408.04705","DOIUrl":"https://doi.org/arxiv-2408.04705","url":null,"abstract":"The emerging machine learning paradigm of decentralized federated learning\u0000(DFL) has the promise of greatly boosting the deployment of artificial\u0000intelligence (AI) by directly learning across distributed agents without\u0000centralized coordination. Despite significant efforts on improving the\u0000communication efficiency of DFL, most existing solutions were based on the\u0000simplistic assumption that neighboring agents are physically adjacent in the\u0000underlying communication network, which fails to correctly capture the\u0000communication cost when learning over a general bandwidth-limited network, as\u0000encountered in many edge networks. In this work, we address this gap by\u0000leveraging recent advances in network tomography to jointly design the\u0000communication demands and the communication schedule for overlay-based DFL in\u0000bandwidth-limited networks without requiring explicit cooperation from the\u0000underlying network. By carefully analyzing the structure of our problem, we\u0000decompose it into a series of optimization problems that can each be solved\u0000efficiently, to collectively minimize the total training time. Extensive\u0000data-driven simulations show that our solution can significantly accelerate DFL\u0000in comparison with state-of-the-art designs.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944403","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