Computer Communications最新文献_第10页

Joint design of resource allocation and QoS enhancement via serial optimization in UAV-NOMA communications 基于串行优化的无人机- noma通信资源分配与QoS增强联合设计

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-12-30 DOI: 10.1016/j.comcom.2025.108409

Zhongyu Wang , Yanan Lian , Jie Zeng , Zheng Chang , Tiejun Lv

We investigate the challenges of user pairing, power allocation, and bandwidth allocation problems in unmanned aerial vehicle (UAV) systems that employ nonorthogonal multiple access (NOMA) for communication with multiple ground users. The primary objective is to maximize the system’s achievable transmission rate while ensuring the users’ quality of service (QoS) requirements under a constrained total power budget. Considering the nonconvexity of the original problem and the interdependencies among multiple optimization variables, the problem is decomposed into three subproblems to optimize power and bandwidth allocation. To increase resource utilization and address user pairing challenges, a serial-optimized communication scheme is proposed, which leverages an optimized block coordinate descent (OP-BCD) method to sequentially solve the subproblems. Specifically, the power allocation strategy is optimized using an optimized deep Q-network (DQN) combined with a gradient ascent approach, whereas the intergroup bandwidth is optimized via a sequential least squares programming (SLSQP). Simulation results demonstrate that the proposed group matching method significantly enhances resource utilization and fairness compared to other user pairing strategies. Moreover, the proposed scheme effectively increases the system transmission rate and resource efficiency.

本文研究了采用非正交多址（NOMA）与多个地面用户通信的无人机系统中的用户配对、功率分配和带宽分配问题。主要目标是在有限的总功率预算下，在保证用户服务质量（QoS）需求的同时，最大限度地提高系统可实现的传输速率。考虑到原问题的非凸性和多个优化变量之间的相互依赖性，将问题分解为三个子问题，对功率和带宽分配进行优化。为了提高资源利用率和解决用户配对难题，提出了一种串行优化通信方案，该方案利用优化块坐标下降（OP-BCD）方法对子问题进行顺序求解。具体而言，采用优化的深度q网络（DQN）和梯度上升方法对功率分配策略进行优化，而通过顺序最小二乘规划（SLSQP）对群间带宽进行优化。仿真结果表明，与其他用户配对策略相比，所提出的分组匹配方法显著提高了资源利用率和公平性。此外，该方案有效地提高了系统传输速率和资源效率。

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引用次数: 0

Making cellular networks crisis-proof: Towards island-ready, resilient-by-design 6G communication networks 使蜂窝网络防危机：迈向孤岛就绪、设计弹性的6G通信网络

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-12-30 DOI: 10.1016/j.comcom.2025.108407

Leon Janzen, Matthias Hollick

5G and 5G-Advanced cellular networks are vulnerable to local outages resulting from disasters or targeted attacks. This fragility stems from the reliance on the central core network involved for most 5G connectivity use cases. Crisis-struck areas isolated from the cellular core network form islands, where crisis response is hindered by the unavailability of recovery-relevant services, such as emergency calls, cell broadcasts, messengers, and news apps. Our concept of island-ready, resilient-by-design 6G communication networks envisions local cellular connectivity allowing users to connect to local application servers, which is currently impossible. In our conceptualization, we follow an all-society approach, as realizing island connectivity requires the cooperation of multiple actors, including users, operators, developers, providers, and authorities. We evaluate the island readiness of 5G and 5G-Advanced systems and outline the open challenges stakeholders must address for full island readiness, such as decentralizing the 6G core network and designing local-first application architectures.

5G和5G- advanced蜂窝网络很容易受到灾难或有针对性攻击造成的局部中断的影响。这种脆弱性源于对大多数5G连接用例所涉及的中央核心网络的依赖。与蜂窝核心网络隔离的受危机影响地区形成孤岛，由于无法获得与恢复相关的服务，如紧急呼叫、蜂窝广播、信使和新闻应用程序，危机应对受到阻碍。我们的概念是孤岛就绪，设计灵活的6G通信网络，设想本地蜂窝连接允许用户连接到本地应用服务器，这是目前不可能的。在我们的概念中，我们遵循全社会的方法，因为实现岛屿连接需要多个参与者的合作，包括用户、运营商、开发商、提供商和当局。我们评估了5G和5G- advanced系统的孤岛准备情况，并概述了利益相关者必须解决的开放挑战，以实现完全的孤岛准备，例如分散6G核心网络和设计本地优先的应用架构。

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引用次数: 0

MetaHeart: Metasurface enabled biometrics camouflage MetaHeart: metassurface启用生物识别伪装

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-12-27 DOI: 10.1016/j.comcom.2025.108405

Dora Zivanovic , Jy-Chin Liao , Zhambyl Shaikhanov , Hou-Tong Chen , Chun-Chieh Chang , Sadhvikas Addamane , Daniel M. Mittleman , Edward W. Knightly

Privacy-invading biometrics monitoring is becoming a prominent security threat as modern sensing systems move to higher operating frequencies (mmWave, sub-THz), increasing sensing resolution and accuracy. As such, developing systems that can protect or obfuscate biometrics from adversarial intrusion becomes pivotal to preserving user privacy. In this work, we develop and implement MetaHeart, a real-time biometrics misinformation system based on reflective, programmable metasurfaces and dynamic phase-front manipulation of radar inferences. MetaHeart’s key goal is to prevent the leakage of a legitimate user’s heartbeat biometrics by spoofing fake heartbeat signals at a malicious, radar-equipped, heart rate sensing intruder. We experimentally demonstrate MetaHeart’s ability to fake Alice’s presence when she is not there and to fool Trudy’s inferences even when Alice is present, achieving an overall accuracy above 98%. Finally, we conduct a robustness analysis to determine MetaHeart’s required spatial placement within the intruder’s monitoring area that would allow for effective spoofing.

随着现代传感系统向更高的工作频率（毫米波、次太赫兹）移动，传感分辨率和精度不断提高，侵犯隐私的生物识别监测正成为一个突出的安全威胁。因此，开发能够保护或混淆生物识别技术免受敌对入侵的系统对于保护用户隐私至关重要。在这项工作中，我们开发并实现了MetaHeart，这是一个基于反射、可编程元表面和雷达推断的动态相位前操作的实时生物识别错误信息系统。MetaHeart的主要目标是通过欺骗伪造的心跳信号，防止合法用户的心跳生物识别信息泄露给恶意的、配备雷达的心率感应入侵者。我们通过实验证明，MetaHeart能够在爱丽丝不在场的时候假装她的存在，即使在爱丽丝在场的时候也能欺骗特鲁迪的推断，总体准确率超过98%。最后，我们进行了鲁棒性分析，以确定MetaHeart在入侵者监控区域内所需的空间位置，从而允许有效的欺骗。

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引用次数: 0

Making TCP IoT-friendly towards the 6G era 面向6G时代，使TCP物联网友好

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-12-27 DOI: 10.1016/j.comcom.2025.108406

Carles Gomez , Jon Crowcroft

Traditionally, Internet of Things (IoT) communication technologies have been designed to offer low bit rates (from ∼10² to ∼10⁶ bit/s). However, recent IoT-intended technologies like 5G Reduced Capability (RedCap) support significantly greater bit rates (up to ∼10⁸ bit/s), enabling emerging IoT use cases that demand greater capacity. Thus, the spectrum of IoT scenarios and corresponding requirements is expanding, a trend which is expected to continue with 6G networks. In this context, support, configuration and performance of a crucial upper-layer protocol like TCP become challenging. In this paper, based on our IETF standardization work, we describe how TCP can run suitably on a wide variety of IoT environments (from highly constrained scenarios to resource-rich ones). Furthermore, we present and study the novel TCP option called TCP Acknowledgment Rate Request (TARR), designed for further TCP adaptability, which is particularly useful for current and future IoT networks.

传统上，物联网（IoT）通信技术被设计为提供低比特率（从~ 102到~ 106比特/秒）。然而，最近的物联网技术，如5G低容量（RedCap），支持更高的比特率（高达~ 108比特/秒），使新兴的物联网用例需要更大的容量。因此，物联网场景的范围和相应的需求正在扩大，预计6G网络将继续这一趋势。在这种情况下，像TCP这样重要的上层协议的支持、配置和性能变得具有挑战性。在本文中，基于我们的IETF标准化工作，我们描述了TCP如何在各种物联网环境（从高度受限的场景到资源丰富的场景）上适当地运行。此外，我们提出并研究了一种新的TCP选项，称为TCP确认率请求（TARR），旨在进一步提高TCP的适应性，这对当前和未来的物联网网络特别有用。

引用次数: 0

Joint optimization of UAV trajectory, RIS selection, and offloading strategy for RIS-assisted UAV-MEC systems based on DRL 基于DRL的RIS辅助UAV- mec系统航迹、RIS选择及卸载策略联合优化

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-12-24 DOI: 10.1016/j.comcom.2025.108402

Jianhua Liu , Bo Tang , Jiajia Liu , Xia Lei , Xiaoguang Tu , Xiaofan Wang

The Mobile Edge Computing (MEC) system equipped with servers on Unmanned Aerial Vehicle (UAV) is an effective solution for computing offloading in the absence of communication resource infrastructure. Introducing Reconfigurable Intelligent Surfaces (RIS) into the UAV-MEC system can enhance air-to-ground communication quality and optimize Non-Line-of-Sight (N-LoS) links. However, how to dynamically select service users for the RIS based on environmental changes, so as to reduce signal interference and improve resource utilization, remains a key challenge to be addressed. To tackle this issue, we aim to jointly optimize the three-dimensional (3D) trajectory of UAV, RIS selection decisions, and task offloading strategy to minimize the overall system delay. Due to the non-convexity of the joint problem, it is difficult to solve it efficiently using traditional optimization methods. Therefore, this paper proposes a Hybrid Exploration Deep Deterministic Policy Gradient (HEDDPG) algorithm based on Deep Reinforcement Learning (DRL). By integrating random exploration into the original DDPG framework, the proposed algorithm enhances global search capability in complex environments. The experimental results show that with the addition of RIS assistance, the system delay is reduced by 18.8%. Compared to other benchmark algorithms, HEDDPG performs better, achieving efficient strategy optimization for the system and improving the performance of communication links and resource utilization efficiency.

在无人机（UAV）上配备服务器的移动边缘计算（MEC）系统是在没有通信资源基础设施的情况下进行计算卸载的有效解决方案。将可重构智能表面（RIS）引入无人机- mec系统可以提高空对地通信质量并优化非视距（N-LoS）链路。然而，如何根据环境变化动态选择RIS的业务用户，以减少信号干扰，提高资源利用率，仍然是一个需要解决的关键挑战。为了解决这一问题，我们的目标是共同优化无人机的三维（3D）轨迹，RIS选择决策和任务卸载策略，以最小化整个系统的延迟。由于关节问题的非凸性，传统的优化方法难以有效求解。为此，本文提出了一种基于深度强化学习（DRL）的混合探索深度确定性策略梯度（HEDDPG）算法。该算法将随机搜索集成到原始DDPG框架中，增强了复杂环境下的全局搜索能力。实验结果表明，加入RIS辅助后，系统延迟降低了18.8%。与其他基准算法相比，HEDDPG性能更好，实现了系统高效的策略优化，提高了通信链路性能和资源利用效率。

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引用次数: 0

A cooperative distributed model to evaluate and optimize task offloading in Mobile Edge Computing 移动边缘计算中任务卸载评估与优化的协作分布式模型

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-12-20 DOI: 10.1016/j.comcom.2025.108403

Fabrizio Messina , Domenico Rosaci

This paper proposes a cooperative and distributed framework to evaluate and optimize task offloading in Mobile Edge Computing (MEC). Each agent, representing either a user device or an edge domain, autonomously interacts with others through trust-driven recommendations and cluster formation. The proposed algorithm exploits this information to iteratively increase – and asymptotically converge over time to – the configuration that maximizes the collective utility of edge servers and user devices, i.e., the Average Performance (

A P

), which corresponds to a Nash equilibrium where only reliable agents are rewarded. Two synthetic indicators are introduced to model the main aspects of MEC collaboration: the Quality of Experience (

Q o E

), representing the perceived user-side performance, and the Convenience (

C

), expressing the server-side efficiency and resource cost. Experimental validation, performed over a simulated MEC environment with up to 1000 agents, shows a rapid convergence (within 20 iterations), a stable equilibrium with

A P \approx 0.92

, and robustness to variations in the simulated values of agents’ reliability. The results demonstrate that the proposed distributed algorithm achieves efficient, self-organized coordination among heterogeneous edge entities while maintaining scalability and fairness.

提出了一种协作式分布式框架来评估和优化移动边缘计算（MEC）中的任务卸载。每个代理代表一个用户设备或一个边缘域，通过信任驱动的建议和集群形成自主地与其他代理交互。所提出的算法利用这些信息迭代地增加-并随着时间的推移渐近收敛-最大化边缘服务器和用户设备的集体效用的配置，即平均性能（AP），这对应于纳什均衡，其中只有可靠的代理得到奖励。引入了两个综合指标来对MEC协作的主要方面进行建模：体验质量（QoE），代表感知到的用户端性能，以及便利性(C)，表示服务器端效率和资源成本。在多达1000个代理的模拟MEC环境中进行的实验验证表明，该方法具有快速收敛（在20次迭代内）、稳定的平衡（AP≈0.92）以及对代理可靠性模拟值变化的鲁棒性。结果表明，该算法在保持可扩展性和公平性的同时，实现了异构边缘实体之间高效、自组织的协调。

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引用次数: 0

Power prediction and energy aware placement of containers over virtual machines 在虚拟机上对容器进行功率预测和能源感知

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-12-17 DOI: 10.1016/j.comcom.2025.108340

Rafael Albuquerque, Brigitte Jaumard

The rapid expansion of 5G and the upcoming arrival of 6G have significantly increased the demand for cloud computing resources, especially in edge cloud servers, to meet stringent connectivity and latency requirements. This surge has raised serious energy concerns as data centers now account for about 1–1.5% of global energy consumption and contribute about 1% of global CO

_{2}

emissions. In response to these facts, this study proposes a novel energy-aware machine learning model, using power sensor data from physical machines (PMs) in data centers, to optimize energy consumption while managing container placement as a use case.

We conducted experiments in a testbed using realistic 5G traffic scenarios, deliberately avoiding artificial stressors such as stress-ng, which create synthetic loads that do not accurately reflect real-world resource utilization. Our machine learning model, particularly the XGBoost implementation, proved to be highly effective, achieving an

R^{2}

score of 91.2%. The model demonstrated the ability to reduce energy consumption by 3% and improve task completion times, all without the need for explicit consolidation strategies or cluster reconfiguration.

This approach highlights the power of machine learning in optimizing energy efficiency in dynamic and resource-intensive environments such as edge cloud servers, providing a scalable solution for data centers facing increasing energy demands.

5G的快速扩张和即将到来的6G大幅增加了对云计算资源的需求，特别是在边缘云服务器方面，以满足严格的连接和延迟要求。这种激增引起了严重的能源问题，因为数据中心目前约占全球能源消耗的1-1.5%，并贡献了约1%的全球二氧化碳排放量。针对这些事实，本研究提出了一种新的能源感知机器学习模型，该模型使用数据中心物理机器（pm）的功率传感器数据，以优化能源消耗，同时管理容器放置作为用例。我们在一个使用真实5G流量场景的测试平台上进行了实验，刻意避免了压力-ng等人为压力源，这些压力源会产生无法准确反映真实资源利用率的合成负载。我们的机器学习模型，特别是XGBoost实现，被证明是非常有效的，达到了91.2%的R2分数。该模型表明，在不需要明确的整合策略或集群重新配置的情况下，该模型能够将能耗降低3%，并提高任务完成时间。这种方法突出了机器学习在动态和资源密集型环境（如边缘云服务器）中优化能源效率的强大功能，为面临日益增长的能源需求的数据中心提供了可扩展的解决方案。

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引用次数: 0

Intelligent routing optimization with deep reinforcement learning and Betweenness Centrality Theory in software-defined networks 基于深度强化学习和中间性理论的软件定义网络智能路由优化

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-12-16 DOI: 10.1016/j.comcom.2025.108401

Zongming Wu , Qiang Tang , Jijun Cao , Sihao Wen , Bao Li

For highly dynamic and complex communication networks, existing DRL-based routing optimization solutions suffer from inefficient training, leading to degraded network performance. In this paper, we propose an Intelligent Routing Optimization method with Deep Reinforcement Learning and Betweenness Centrality Theory (IROD-BC). This SDN routing solution based on distributed proximal policy optimization can achieve fast convergence of training and improve the overall performance of the network. First, before training, we select a set of controlled nodes in the network based on the Betweenness Centrality Theory. Second, during training, we adjust the weights of the links in the weighted shortest path algorithm based on this set of controlled nodes to improve the convergence efficiency of distributed proximal policy optimization. The learning agent modifies the weights of the links in the controlled nodes links based on the network traffic state information of this set of controlled nodes to reduce the agent’s dependence on the network topology. We utilize SDN controller to collect network traffic state information including packet loss and latency. Ultimately, the IROD-BC proposed in this paper can learn to make better routing control decisions from its own experience by interacting with the network environment until the learning agent converges and obtains the optimal routing paths. We conducted extensive experiments on three real network topologies to evaluate the performance of IROD-BC. The experimental results show that IROD-BC outperforms existing DRL-based routing solutions and OSPF algorithm in terms of latency, link throughput, and packet loss.

对于高动态、复杂的通信网络，现有基于drl的路由优化方案存在训练效率低下的问题，导致网络性能下降。本文提出了一种基于深度强化学习和中间性理论（IROD-BC）的智能路由优化方法。这种基于分布式近端策略优化的SDN路由解决方案可以实现训练的快速收敛，提高网络的整体性能。首先，在训练前，我们根据中间性中心性理论在网络中选择一组受控节点。其次，在训练过程中，基于该控制节点集调整加权最短路径算法中链路的权值，提高分布式近端策略优化的收敛效率。学习智能体根据这组被控制节点的网络流量状态信息来修改被控制节点链路中链路的权重，以减少智能体对网络拓扑的依赖。我们利用SDN控制器来收集网络流量状态信息，包括丢包和延迟。最终，本文提出的IROD-BC可以通过与网络环境的交互，从自身的经验中学习做出更好的路由控制决策，直到学习代理收敛并获得最优路由路径。我们在三种真实网络拓扑上进行了大量的实验来评估IROD-BC的性能。实验结果表明，IROD-BC在时延、链路吞吐量和丢包方面都优于现有基于drl的路由解决方案和OSPF算法。

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引用次数: 0

Indoor positioning with Wi-Fi Location: A survey of IEEE 802.11mc/az/bk fine timing measurement research 基于Wi-Fi定位的室内定位：IEEE 802.11mc/az/bk精细定时测量研究综述

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-12-12 DOI: 10.1016/j.comcom.2025.108400

Katarzyna Kosek-Szott , Szymon Szott , Wojciech Ciezobka , Maksymilian Wojnar , Krzysztof Rusek , Jonathan Segev

Indoor positioning is an enabling technology for home, office, and industrial network users because it provides numerous information and communication technology (ICT) and Internet of things (IoT) functionalities such as indoor navigation, smart meter localization, asset tracking, support for emergency services, and detection of hazardous situations. The IEEE 802.11mc fine timing measurement (FTM) protocol (commercially known as Wi-Fi Location) has great potential to enable indoor positioning in future generation devices, primarily because of the high availability of Wi-Fi networks, FTM’s high accuracy and device support. Furthermore, new FTM enhancements are available in the released (802.11az) and recently completed (802.11bk) amendments. Despite the multitude of literature reviews on indoor positioning, a survey dedicated to FTM and its recent enhancements has so far been lacking. We fill this gap by classifying and reviewing over 180 research papers related to the practical accuracy achieved with FTM, methods for improving its accuracy (also with machine learning), combining FTM with other indoor positioning systems, FTM-based applications, and security issues. Based on the conducted survey, we summarize the most important research achievements and formulate open areas for further research.

室内定位是一项适用于家庭、办公室和工业网络用户的使能技术，因为它提供了许多信息和通信技术（ICT）和物联网（IoT）功能，如室内导航、智能电表定位、资产跟踪、紧急服务支持和危险情况检测。IEEE 802.11mc精细定时测量（FTM）协议（商业上称为Wi-Fi定位）在下一代设备中具有实现室内定位的巨大潜力，主要是因为Wi-Fi网络的高可用性，FTM的高精度和设备支持。此外，在已发布的（802.11az）和最近完成的（802.11bk）修订中提供了新的FTM增强功能。尽管有大量关于室内定位的文献综述，但迄今为止还缺乏专门针对FTM及其近期增强功能的调查。我们通过分类和审查180多篇研究论文来填补这一空白，这些论文涉及FTM实现的实际精度、提高其精度的方法（也包括机器学习）、FTM与其他室内定位系统的结合、基于FTM的应用以及安全问题。在调查的基础上，我们总结了最重要的研究成果，并提出了进一步研究的开放领域。

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引用次数: 0

Energy saving in Fixed Wireless Access networks utilizing scheduled coordinated sleeping time 利用预定的协调睡眠时间节约固定无线接入网络的能源

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-12-09 DOI: 10.1016/j.comcom.2025.108399

Ozgur Ozkaya, Jetmir Haxhibeqiri, Ingrid Moerman, Jeroen Hoebeke

Fixed Wireless Access (FWA) networks are used to extend connectivity to areas with limited or no access, especially where the deployment of wired infrastructure is costly. In such networks, the infrastructure can take the form of a multi-hop mesh network consisting of Distribution Nodes (DNs) and Client Nodes (CNs). A CN is served by a DN and extends connectivity within homes by acting as an access point (AP). Due to fluctuations in traffic over time, network utilization also fluctuates. When the network is scarcely utilized, it leads to energy waste due to powering the network during these times. In this paper, we explore methods to reduce energy consumption in wireless mesh networks (WMNs) by implementing coordinated sleeping times for APs of the last hop and the DNs inside the FWA network. By dynamically scheduling sleep patterns in the last hop, the solution achieves network-wide energy savings without compromising the quality of service for traffic flows in terms of latency and reliability. Moreover, in this paper, we reduce the Orthogonal Frequency Division Multiple Access (OFDMA) overhead and utilize this to organize time-critical traffic in the last hop of FWA, benefiting from the coordinated sleeping time. Our results show that in medium-load scenarios, this approach can achieve up to 33% energy savings in FWA mesh networks combined with NextGen Wi-Fi while maintaining bounded latency for time-critical applications and serving non-time-critical traffic.

固定无线接入（FWA）网络用于将连接扩展到接入有限或没有接入的地区，特别是在有线基础设施部署成本高昂的地区。在这种网络中，基础设施可以采用由分布节点（DNs）和客户端节点（CNs）组成的多跳网状网络的形式。CN由DN提供服务，并通过充当接入点（AP）来扩展家庭内部的连通性。由于流量随时间的波动，网络利用率也会波动。当网络很少被利用时，由于在这些时间为网络供电，导致能源浪费。在本文中，我们探讨了通过在无线网状网络（WMNs）中实现最后一跳ap和FWA网络内DNs的协调睡眠时间来降低能量消耗的方法。通过在最后一跳中动态调度睡眠模式，该解决方案实现了全网范围的节能，而不会在延迟和可靠性方面影响流量流的服务质量。此外，本文还减少了正交频分多址（OFDMA）的开销，并利用它来组织FWA最后一跳的时间关键型业务，从而受益于协调的休眠时间。我们的研究结果表明，在中等负载情况下，这种方法可以在FWA网状网络与NextGen Wi-Fi相结合的情况下节省高达33%的能源，同时为时间关键型应用和服务非时间关键型流量保持有限的延迟。

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引用次数: 0