Conghao Zhou, Jie Gao, Yixiang Liu, Shisheng Hu, Nan Cheng, Xuemin Shen
Future 6G networks are envisioned to support mobile augmented reality (MAR) applications and provide customized immersive experiences for users via advanced service provision. In this paper, we investigate user-centric service provision for edge-assisted MAR to support the timely camera frame uploading of an MAR device by optimizing the spectrum resource reservation. To address the challenge of non-stationary data traffic due to uncertain user movement and the complex camera frame uploading mechanism, we develop a digital twin (DT)-based data-driven approach to user-centric service provision. Specifically, we first establish a hierarchical data model with well-defined data attributes to characterize the impact of the camera frame uploading mechanism on the user-specific data traffic. We then design an easy-to-use algorithm to adapt the data attributes used in traffic modeling to the non-stationary data traffic. We also derive a closed-form service provision solution tailored to data-driven traffic modeling with the consideration of potential modeling inaccuracies. Trace-driven simulation results demonstrate that our DT-based approach for user-centric service provision outperforms conventional approaches in terms of adaptivity and robustness.
{"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":"23 1","pages":""},"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}
S. M. Mahdi Shahabi, Xiaonan Deng, Ahmad Qidan, Taisir Elgorashi, Jaafar Elmirghani
This paper investigates the integration of Open Radio Access Network (O-RAN) within non-terrestrial networks (NTN), and optimizing the dynamic functional split between Centralized Units (CU) and Distributed Units (DU) for enhanced energy efficiency in the network. We introduce a novel framework utilizing a Deep Q-Network (DQN)-based reinforcement learning approach to dynamically find the optimal RAN functional split option and the best NTN-based RAN network out of the available NTN-platforms according to real-time conditions, traffic demands, and limited energy resources in NTN platforms. This approach supports capability of adapting to various NTN-based RANs across different platforms such as LEO satellites and high-altitude platform stations (HAPS), enabling adaptive network reconfiguration to ensure optimal service quality and energy utilization. Simulation results validate the effectiveness of our method, offering significant improvements in energy efficiency and sustainability under diverse NTN scenarios.
本文研究了在非地面网络（NTN）中集成开放无线接入网（O-RAN），并优化集中式单元（CU）和分布式单元（DU）之间的动态功能划分，以提高网络能效。我们引入了一个新颖的框架，利用基于深度 Q 网络（DQN）的强化学习方法，根据实时条件、流量需求和 NTN 平台有限的能源资源，从可用的 NTN 平台中动态找到最佳的 RAN 功能划分选项和基于 NTN 的最佳 RAN 网络。这种方法支持在低地轨道卫星和高空平台站（HAPS）等不同平台上适应各种基于NTN的RAN，实现自适应网络重新配置，以确保最佳服务质量和能源利用。仿真结果验证了我们的方法的有效性，在各种 NTN 场景下显著提高了能效和可持续性。
{"title":"Energy-efficient Functional Split in Non-terrestrial Open Radio Access Networks","authors":"S. M. Mahdi Shahabi, Xiaonan Deng, Ahmad Qidan, Taisir Elgorashi, Jaafar Elmirghani","doi":"arxiv-2409.00466","DOIUrl":"https://doi.org/arxiv-2409.00466","url":null,"abstract":"This paper investigates the integration of Open Radio Access Network (O-RAN)\u0000within non-terrestrial networks (NTN), and optimizing the dynamic functional\u0000split between Centralized Units (CU) and Distributed Units (DU) for enhanced\u0000energy efficiency in the network. We introduce a novel framework utilizing a\u0000Deep Q-Network (DQN)-based reinforcement learning approach to dynamically find\u0000the optimal RAN functional split option and the best NTN-based RAN network out\u0000of the available NTN-platforms according to real-time conditions, traffic\u0000demands, and limited energy resources in NTN platforms. This approach supports\u0000capability of adapting to various NTN-based RANs across different platforms\u0000such as LEO satellites and high-altitude platform stations (HAPS), enabling\u0000adaptive network reconfiguration to ensure optimal service quality and energy\u0000utilization. Simulation results validate the effectiveness of our method,\u0000offering significant improvements in energy efficiency and sustainability under\u0000diverse NTN scenarios.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183949","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}
The channel estimation (CE) overhead for unstructured multipath-rich channels increases linearly with the number of reflective elements of reconfigurable intelligent surface (RIS). This results in a significant portion of the channel coherence time being spent on CE, reducing data communication time. Furthermore, due to the mobility of the user equipment (UE) and the time consumed during CE, the estimated channel state information (CSI) may become outdated during actual data communication. In recent studies, the timing for CE has been primarily determined based on the coherence time interval, which is dependent on the velocity of the UE. However, the effect of the current channel condition and pathloss of the UEs can also be utilized to control the duration between successive CE to reduce the overhead while still maintaining the quality of service. Furthermore, for muti-user systems, the appropriate coherence time intervals of different users may be different depending on their velocities. Therefore CE carried out ignoring the difference in coherence time of different UEs may result in the estimated CSI being detrimentally outdated for some users. In contrast, others may not have sufficient time for data communication. To this end, based on the throughput analysis on outdated CSI, an algorithm has been designed to dynamically predict the next time instant for CE after the current CSI acquisition. In the first step, optimal RIS phase shifts to maximise channel gain is computed. Based on this and the amount of degradation of SINR due to outdated CSI, transmit powers are allocated for the UEs and finally the next time instant for CE is predicted such that the aggregated throughput is maximized. Simulation results confirm that our proposed algorithm outperforms the coherence time-based strategies.
非结构化多径信道的信道估计（CE）开销与可重构智能表面（RIS）的反射元素数量呈线性增长。此外，由于用户设备（UE）的移动性和 CE 期间消耗的时间，估计的信道状态信息（CSI）在实际数据通信期间可能会过时。在最近的研究中，CE 的定时主要是根据相干时间间隔确定的，而相干时间间隔取决于 UE 的速度。不过，也可以利用 UE 当前信道条件和路径损耗的影响来控制连续 CE 之间的持续时间，从而在保持服务质量的同时减少开销。此外，对于多用户系统，不同用户的适当相干时间间隔可能因其位置而异。因此，忽略不同 UE 相干时间差异的 CE 可能会导致估计的 CSI 对某些用户不利地过时。相反，其他用户可能没有足够的时间进行数据通信。为此，基于对过时 CSI 的吞吐量分析，我们设计了一种算法来动态预测当前 CSI 获取后的下一个CE 时间瞬时。第一步，计算最佳 RIS 相移，以实现信道增益最大化。在此基础上，再根据过时 CSI 导致的 SINR 下降量，为 UE 分配发射功率，最后预测 CE 的下一个时间点，从而最大限度地提高综合吞吐量。仿真结果证实，我们提出的算法优于基于相干时间的策略。
{"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":"25 1","pages":""},"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}
Collaborative perception systems leverage multiple edge devices, such surveillance cameras or autonomous cars, to enhance sensing quality and eliminate blind spots. Despite their advantages, challenges such as limited channel capacity and data redundancy impede their effectiveness. To address these issues, we introduce the Prioritized Information Bottleneck (PIB) framework for edge video analytics. This framework prioritizes the shared data based on the signal-to-noise ratio (SNR) and camera coverage of the region of interest (RoI), reducing spatial-temporal data redundancy to transmit only essential information. This strategy avoids the need for video reconstruction at edge servers and maintains low latency. It leverages a deterministic information bottleneck method to extract compact, relevant features, balancing informativeness and communication costs. For high-dimensional data, we apply variational approximations for practical optimization. To reduce communication costs in fluctuating connections, we propose a gate mechanism based on distributed online learning (DOL) to filter out less informative messages and efficiently select edge servers. Moreover, we establish the asymptotic optimality of DOL by proving the sublinearity of their regrets. Compared to five coding methods for image and video compression, PIB improves mean object detection accuracy (MODA) while reducing 17.8% and reduces communication costs by 82.80% under poor channel conditions.
{"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":"17 1","pages":""},"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}
Deadline-aware transmission scheduling in immersive video streaming is crucial. The objective is to guarantee that at least a certain block in multi-links is fully delivered within their deadlines, which is referred to as delivery ratio. Compared with existing models that focus on maximizing throughput and ultra-low latency, which makes bandwidth resource allocation and user satisfaction locally optimized, immersive video streaming needs to guarantee more high-priority block delivery within personalized deadlines. In this paper, we propose a deadline and priority-constrained immersive video streaming transmission scheduling scheme. It builds an accurate bandwidth prediction model that can sensitively assist scheduling decisions. It divides video streaming into various media elements and performs scheduling based on the user's personalized latency sensitivity thresholds and the media element's priority. We evaluate our scheme via trace-driven simulations. Compared with existing models, the results further demonstrate the superiority of our scheme with 12{%}-31{%} gains in quality of experience (QoE).
{"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":"26 1","pages":""},"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}
Collaborative edge sensing systems, particularly in collaborative perception systems in autonomous driving, can significantly enhance tracking accuracy and reduce blind spots with multi-view sensing capabilities. However, their limited channel capacity and the redundancy in sensory data pose significant challenges, affecting the performance of collaborative inference tasks. To tackle these issues, we introduce a Prioritized Information Bottleneck (PIB) framework for collaborative edge video analytics. We first propose a priority-based inference mechanism that jointly considers the signal-to-noise ratio (SNR) and the camera's coverage area of the region of interest (RoI). To enable efficient inference, PIB reduces video redundancy in both spatial and temporal domains and transmits only the essential information for the downstream inference tasks. This eliminates the need to reconstruct videos on the edge server while maintaining low latency. Specifically, it derives compact, task-relevant features by employing the deterministic information bottleneck (IB) method, which strikes a balance between feature informativeness and communication costs. Given the computational challenges caused by IB-based objectives with high-dimensional data, we resort to variational approximations for feasible optimization. Compared to TOCOM-TEM, JPEG, and HEVC, PIB achieves an improvement of up to 15.1% in mean object detection accuracy (MODA) and reduces communication costs by 66.7% when edge cameras experience poor channel conditions.
{"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":"1 1","pages":""},"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}
The integration of Artificial Intelligence (AI) within 6G networks is poised to revolutionize connectivity, reliability, and intelligent decision-making. However, the performance of AI models in these networks is crucial, as any decline can significantly impact network efficiency and the services it supports. Understanding the root causes of performance degradation is essential for maintaining optimal network functionality. In this paper, we propose a novel approach to reason about AI model performance degradation in 6G networks using the Large Language Models (LLMs) empowered Chain-of-Thought (CoT) method. Our approach employs an LLM as a ''teacher'' model through zero-shot prompting to generate teaching CoT rationales, followed by a CoT ''student'' model that is fine-tuned by the generated teaching data for learning to reason about performance declines. The efficacy of this model is evaluated in a real-world scenario involving a real-time 3D rendering task with multi-Access Technologies (mATs) including WiFi, 5G, and LiFi for data transmission. Experimental results show that our approach achieves over 97% reasoning accuracy on the built test questions, confirming the validity of our collected dataset and the effectiveness of the LLM-CoT method. Our findings highlight the potential of LLMs in enhancing the reliability and efficiency of 6G networks, representing a significant advancement in the evolution of AI-native network infrastructures.
{"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":"11 1","pages":""},"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}
Generative artificial intelligence (GAI), known for its powerful capabilities in image and text processing, also holds significant promise for the design and performance enhancement of future wireless networks. In this article, we explore the transformative potential of GAI in next-generation Wi-Fi networks, exploiting its advanced capabilities to address key challenges and improve overall network performance. We begin by reviewing the development of major Wi-Fi generations and illustrating the challenges that future Wi-Fi networks may encounter. We then introduce typical GAI models and detail their potential capabilities in Wi-Fi network optimization, performance enhancement, and other applications. Furthermore, we present a case study wherein we propose a retrieval-augmented LLM (RA-LLM)-enabled Wi-Fi design framework that aids in problem formulation, which is subsequently solved using a generative diffusion model (GDM)-based deep reinforcement learning (DRL) framework to optimize various network parameters. Numerical results demonstrate the effectiveness of our proposed algorithm in high-density deployment scenarios. Finally, we provide some potential future research directions for GAI-assisted Wi-Fi networks.
生成式人工智能（GAI）以其在图像和文本处理方面的强大功能而著称，在未来无线网络的设计和性能提升方面也大有可为。在本文中，我们将探讨 GAI 在下一代 Wi-Fi 网络中的变革潜力，利用其先进功能应对关键挑战并提高整体网络性能。我们首先回顾了主要几代 Wi-Fi 的发展历程，并说明了未来 Wi-Fi 网络可能遇到的挑战。然后，我们介绍了典型的 GAI 模型，并详细介绍了它们在 Wi-Fi 网络优化、性能提升和其他应用中的潜在能力。此外，我们还介绍了一个案例研究，在这个案例研究中，我们提出了一个支持检索增强 LLM（RA-LLM）的 Wi-Fi 设计框架，该框架有助于问题的提出，随后使用基于生成扩散模型（GDM）的深度强化学习（DRL）框架来优化各种网络参数。数值结果证明了我们提出的算法在高密度部署场景中的有效性。最后，我们为 GAI 辅助的 Wi-Finetworks 提供了一些潜在的未来研究方向。
{"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":"39 1","pages":""},"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}
Roger Sanchez-Vital, Lluís Casals, Bartomeu Heer-Salva, Rafael Vidal, Carles Gomez, Eduard Garcia-Villegas
Long Range-Frequency Hopping Spread Spectrum (LR-FHSS) is a pivotal advancement in the LoRaWAN protocol, designed to enhance the network's capacity and robustness, particularly in densely populated environments. Although energy consumption is paramount in LoRaWAN-based end-devices, there are currently no studies in the literature, to our knowledge, that model the impact of this novel mechanism on energy consumption. In this article, we provide a comprehensive energy consumption analytical model of LR-FHSS, focusing on three critical metrics: average current consumption, battery lifetime, and energy efficiency of data transmission. The model is based on measurements performed on real hardware in a fully operational LR-FHSS network. While in our evaluation, LR-FHSS can show worse consumption figures than LoRa, we found that with optimal configuration, the battery lifetime of LR-FHSS end-devices can reach 2.5 years for a 50-minute notification period. For the most energy-efficient payload size, this lifespan can be extended to a theoretical maximum of up to 16 years with a one-day notification interval using a cell-coin battery.
{"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":"26 1","pages":""},"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}
The emerging machine learning paradigm of decentralized federated learning (DFL) has the promise of greatly boosting the deployment of artificial intelligence (AI) by directly learning across distributed agents without centralized coordination. Despite significant efforts on improving the communication efficiency of DFL, most existing solutions were based on the simplistic assumption that neighboring agents are physically adjacent in the underlying communication network, which fails to correctly capture the communication cost when learning over a general bandwidth-limited network, as encountered in many edge networks. In this work, we address this gap by leveraging recent advances in network tomography to jointly design the communication demands and the communication schedule for overlay-based DFL in bandwidth-limited networks without requiring explicit cooperation from the underlying network. By carefully analyzing the structure of our problem, we decompose it into a series of optimization problems that can each be solved efficiently, to collectively minimize the total training time. Extensive data-driven simulations show that our solution can significantly accelerate DFL in comparison with state-of-the-art designs.
{"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":"77 1","pages":""},"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}
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