Physical Communication最新文献_第10页

CFO-based physical-layer authentication for integrated sensing and communication under dynamic time resources 动态时间资源下基于cfo的集成传感与通信物理层认证

IF 2.2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2026-02-01 Epub Date: 2025-12-31 DOI: 10.1016/j.phycom.2025.102977

Tuanwei Tian , Ke Shao , Jing Yang , Jinlong Zhang , Hao Deng

This study proposes an integrated sensing and communication (ISAC) framework that incorporates physical-layer authentication through a time-slotted protocol with delayed authentication. To begin with, we propose a time-slotted framework in which the carrier frequency offset (CFO) features obtained during the previous authentication phase are used as a reference for current real-time transmitter verification. This structure effectively decouples the sensing and security operations while ensuring both high-precision target localization and robust authentication. Then, we establish a unified analytical model that quantitatively connects the Cramér-Rao Bound (CRB) for time-of-arrival and direction-of-arrival estimation with CFO-based authentication performance, explicitly characterizing the trade-off between sensing accuracy and authentication security under joint time-power resource constraints. Finally, extensive simulations validate the analytical framework and demonstrate significant performance improvements over conventional schemes in terms of authentication probability, detection accuracy, and localization error across various signal-to-noise ratios and mobility conditions. The robustness of the proposed scheme under practical hardware imperfections is also verified.

本研究提出了一种集成传感和通信（ISAC）框架，该框架通过具有延迟身份验证的时隙协议合并了物理层身份验证。首先，我们提出了一个时隙框架，其中在前一个认证阶段获得的载波频率偏移（CFO）特征被用作当前实时发射机验证的参考。该结构有效地解耦了传感和安全操作，同时保证了高精度的目标定位和鲁棒性认证。然后，我们建立了一个统一的分析模型，定量地将到达时间和到达方向估计的cram r- rao边界（CRB）与基于cfo的认证性能联系起来，明确表征了在联合时间-功率资源约束下感知精度和认证安全性之间的权衡。最后，广泛的仿真验证了分析框架，并证明了在各种信噪比和移动条件下，在认证概率、检测精度和定位误差方面比传统方案有显著的性能改进。验证了该方案在实际硬件缺陷下的鲁棒性。

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引用次数: 0

Efficient signal detection in downlink NOMA systems using LSTM-projected layer deep neural networks 基于lstm投影层深度神经网络的下行NOMA系统有效信号检测

IF 2.2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2026-02-01 Epub Date: 2025-12-07 DOI: 10.1016/j.phycom.2025.102950

Sebin J. Olickal, Renu Jose

Non-orthogonal multiple access (NOMA) systems help to increase the spectral efficiency of wireless communication and thus support many users simultaneously, making them suitable for next-generation wireless networks. However, signal detection in NOMA systems remains a significant challenge due to inherent interference between users. This paper introduces a novel deep learning (DL) based signal detection method which uses a long-short-term memory projected layer (LSTM-PL), a deep neural network (DNN) model to address this challenge effectively. The proposed approach uses the sequence learning capabilities of LSTMs to capture temporal dependencies in received signals, enabling more accurate detection of superimposed user signals. By incorporating a Projected Layer (PL), the complexity of the detection process is substantially reduced, making it suitable for real-time applications. Extensive simulations demonstrate that the proposed LSTM-PL-based detector achieves a better symbol error rate (SER) compared to traditional signal detection techniques and other DNNs. The method is compared with conventional approaches such as least squares (LS) successive interference cancellation (SIC) and SIC minimum mean square error (MMSE), as well as deep learning methods such as LSTM and gated recurrent unit (GRU). The simulations were conducted using different pilot lengths: 64, 16, 8, and 4. The SER shows that the proposed method leaves behind both conventional and other DNN techniques, offering a robust and efficient solution for signal detection in NOMA systems.

非正交多址（NOMA）系统有助于提高无线通信的频谱效率，从而同时支持多个用户，使其适用于下一代无线网络。然而，由于用户之间的固有干扰，在NOMA系统中信号检测仍然是一个重大挑战。本文介绍了一种新的基于深度学习（DL）的信号检测方法，该方法使用长短期记忆投影层（LSTM-PL），一种深度神经网络（DNN）模型来有效地解决这一挑战。该方法利用lstm的序列学习能力来捕获接收信号中的时间依赖性，从而更准确地检测叠加的用户信号。通过结合投影层（PL），大大降低了检测过程的复杂性，使其适合实时应用。大量的仿真表明，与传统的信号检测技术和其他深度神经网络相比，所提出的基于lstm - pl的检测器具有更好的符号错误率（SER）。将该方法与传统的最小二乘（LS）逐次干扰消除（SIC）和SIC最小均方误差（MMSE）方法以及LSTM和门控循环单元（GRU）等深度学习方法进行了比较。模拟使用不同的飞行员长度：64、16、8和4。SER表明，该方法优于传统的深度神经网络技术和其他深度神经网络技术，为NOMA系统中的信号检测提供了鲁棒和高效的解决方案。

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引用次数: 0

Roadside beyond diagonal reconfigurable intelligent surfaces scattering matrix design for vehicular application 路边超对角线可重构智能表面散射矩阵设计在车辆中的应用

IF 2.2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2026-02-01 Epub Date: 2025-12-13 DOI: 10.1016/j.phycom.2025.102963

Fatemeh Mozhdehjou , Mahmoud Atashbar , Hamed Alizadeh Ghazijahani

In high-mobility communication contexts, the rapidly fluctuating channel has a considerable effect on both the throughput and reliability of communications. Beyond diagonal reconfigurable intelligent surfaces (BD-RIS) alleviates the limitations of conventional reconfigurable intelligent surfaces (RIS) by enabling the flexible arrangement of elements, resulting in an enhancement of the maximum achievable rate. In this paper, a method is presented to design the scattering matrix for roadside BD-RIS with a flexible grouping strategy by maximizing the reflected channel gain. In the proposed method, to avoid the high computational burden, maximizing the upper bound of the channel gain is used for the design of the permutation function. Also, the scattering matrix of the classical grouping is designed by solving the optimization problem in a reference time block and is subsequently updated in non-reference blocks. Simulation results indicate that the presented approach outperforms the reference methods regarding channel gain.

在高移动通信环境中，快速波动的信道对通信的吞吐量和可靠性都有相当大的影响。超对角线可重构智能表面（BD-RIS）通过实现元件的灵活排列，减轻了传统可重构智能表面（RIS）的局限性，从而提高了最大可实现速率。本文提出了一种基于灵活分组策略的路边BD-RIS散射矩阵设计方法，以最大化反射信道增益为目标。在该方法中，为了避免较高的计算量，采用最大化信道增益上界的方法来设计排列函数。同时，通过在参考时间块中求解优化问题来设计经典分组的散射矩阵，然后在非参考时间块中更新。仿真结果表明，该方法在信道增益方面优于参考方法。

引用次数: 0

Spectral efficient resource allocation in IRS aided cognitive radio networks with impact of IRS elements association 考虑IRS元素关联影响的IRS辅助认知无线电网络频谱有效资源分配

IF 2.2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2026-02-01 Epub Date: 2025-12-16 DOI: 10.1016/j.phycom.2025.102964

Mohammad Hadi Hajheidari Varnosfaderani , Foroogh S. Tabataba , Mohammad Javad Omidi

Cognitive radio (CR) systems offer an efficient solution to improve spectral and energy efficiency, addressing the growing demand for wireless communication. Intelligent reflecting surfaces (IRS) enhance network performance by dynamically configuring the propagation environment using passive elements. This paper explores IRS integration into CR networks to optimize resource allocation and maximize spectral efficiency for secondary users (SUs) in a downlink scenario from a secondary base station (BS) while ensuring interference to the primary network remains below acceptable limits. This work aims to enhance the sum-rate of SUs by addressing a joint optimization problem that encompasses the allocation of IRS elements to SUs, adjustment of IRS reflection parameters, and the design of beamforming vectors at the secondary BS, modeled as a non-convex mixed-integer program. The problem is decomposed using a strategy based on variable decoupling and constraint relaxation, resulting in two subproblems that are solved in an iterative manner: initially, the secondary BS beamforming vector is obtained via fractional programming (FP), followed by a simulated annealing-based optimization for configuring the phase-shifts and determining IRS-user associations. Simulation results show the proposed method outperforms random allocation and phase-shift scenarios. Performance analysis in practical settings reveals up to a 61 % enhancement in sum-rate in proportion to zero-forcing beamforming utilizing randomly assigned IRS phase-shifts and user association.

认知无线电（CR）系统提供了一种有效的解决方案，以提高频谱和能源效率，满足日益增长的无线通信需求。智能反射面（IRS）通过动态配置无源元素的传播环境来提高网络性能。本文探讨了将IRS集成到CR网络中，以优化资源分配并最大限度地提高从二级基站（BS）下行场景中的二级用户（su）的频谱效率，同时确保对主网络的干扰低于可接受的限制。本工作旨在通过解决一个联合优化问题，包括IRS元素分配到su， IRS反射参数的调整以及二级BS波束形成矢量的设计，以非凸混合整数程序建模，从而提高su的和速率。采用基于变量解耦和约束松弛的策略对问题进行分解，得到两个迭代求解的子问题：首先，通过分数规划（FP）获得次级BS波束形成向量，然后进行基于模拟退火的优化，以配置相移和确定irs用户关联。仿真结果表明，该方法优于随机分配和相移方案。实际环境中的性能分析表明，利用随机分配的IRS相移和用户关联，与零强迫波束形成成比例，和速率可提高61%。

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引用次数: 0

Energy-efficient beamforming and phase shift prediction using window-based recursive transformer in RIS-assisted mmWave networks 基于窗口的递推变压器在ris辅助毫米波网络中的高效波束形成和相移预测

IF 2.2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2026-02-01 Epub Date: 2025-12-20 DOI: 10.1016/j.phycom.2025.102957

Yuh-Shyan Chen , Jung-Chen Lee , Chih-Shun Hsu , Yu-Syuan Lyu

Millimeter-wave (mmWave) technology is essential for 5G and future wireless systems, offering high data capacity but suffering from severe path loss and obstruction issues. This paper introduces a window-based recursive transformer method, enhanced by reconfigurable intelligent surfaces (RIS), to optimize beamforming and phase shift matrices in dynamic user scenarios. The method uses a sliding window to capture temporal dependencies and a recursive memory mechanism to prioritize recent data, ensuring precise channel state estimation. It first predicts the phase shift matrix, then determines the beamforming matrix. This approach enhances communication performance while reducing energy consumption, leading to a more sustainable wireless system. Experimental results show that the proposed BPOR scheme achieves about 25 % energy savings compared to existing methods.

毫米波（mmWave）技术对于5G和未来的无线系统至关重要，它提供高数据容量，但存在严重的路径损耗和阻塞问题。本文介绍了一种基于窗口的递归变压器方法，通过可重构智能曲面（RIS）来优化动态用户场景下的波束形成和相移矩阵。该方法使用滑动窗口捕获时间依赖性，并使用递归内存机制对近期数据进行优先级排序，确保精确的通道状态估计。首先预测相移矩阵，然后确定波束形成矩阵。这种方法提高了通信性能，同时降低了能耗，从而实现了更可持续的无线系统。实验结果表明，与现有方法相比，所提出的BPOR方案节能约25%。

引用次数: 0

Doppler-aware dynamic resource allocation for V2X communications using Double Deep Q-Network 基于双深q网络的V2X通信多普勒感知动态资源分配

IF 2.2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2026-02-01 Epub Date: 2025-11-26 DOI: 10.1016/j.phycom.2025.102923

Xi Huang , Yingying Yu , Longfei Huang , Wenxun Chen

To address the real-time and robustness challenges of resource allocation under rapidly time-varying channels in high-mobility scenarios, this paper proposes an MHA-augmented scheme within a centralized-training–decentralized-execution architecture for mixed V2I/V2V services. First, we adopt a bounded V2V normalized completion rate as a physically meaningful reliability metric. Second, we embed a Multi-Head Attention layer at the front end of the DDQN Q-network: local observations from each link are linearly projected into Query, Key and Value vectors, and multiple attention heads compute in parallel to produce a set of weighted contextual features. This enables agents to dynamically focus on the interference links most severely affected by Doppler shifts. In addition, we incorporate Prioritized Experience Replay together with importance-sampling correction and priority clipping to control sampling bias and to accelerate learning from critical, Doppler- or blockage-induced extreme samples. Simulation results indicate that the proposed hybrid MHA-enhanced scheme increases average V2I throughput by about 9.7%, while still retaining an approximately 5% advantage in high-density, high-speed scenarios. Concurrently, end-to-end V2V transmission success probability improves by roughly 7.8% on average and attains an average gain of about 9.2% at the peak speed of 30 m/s. These experiments validate the complementary effects of MHA and PER under strong Doppler perturbations and demonstrate the effectiveness of the proposed method in multicriteria trade-offs and engineering-level deployment. https://github.com/HX-hx206/V2XX.git.

为了解决高移动性场景下快速时变信道下资源分配的实时性和鲁棒性挑战，本文提出了一种用于混合V2I/V2V服务的集中-训练-分散-执行架构中的mha增强方案。首先，我们采用有界V2V归一化完成率作为物理上有意义的可靠性度量。其次，我们在DDQN Q-network的前端嵌入了一个Multi-Head Attention layer：来自每个链接的局部观测值被线性投影到Query、Key和Value向量中，多个注意头并行计算以产生一组加权上下文特征。这使代理能够动态地关注受多普勒频移影响最严重的干扰链路。此外，我们将优先体验回放与重要采样校正和优先级剪辑结合在一起，以控制采样偏差，并加速从临界、多普勒或阻塞诱导的极端样本中学习。仿真结果表明，提出的混合mha增强方案将平均V2I吞吐量提高了约9.7%，同时在高密度、高速场景下仍保持约5%的优势。同时，端到端V2V传输成功率平均提高约7.8%，在峰值速度为30 m/s时平均增益约为9.2%。这些实验验证了MHA和PER在强多普勒扰动下的互补效应，并证明了该方法在多准则权衡和工程级部署中的有效性。https://github.com/HX-hx206/V2XX.git。

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引用次数: 0

Performance analysis of covert communication in the presence of intelligent eavesdroppers 智能窃听存在下的隐蔽通信性能分析

IF 2.2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2026-02-01 Epub Date: 2025-11-24 DOI: 10.1016/j.phycom.2025.102927

Yinuo Zhao , Enyu Li , Xiaofei Zhai , Weidong Yao , Tonghao Wang

The objective of information confrontation is to detect, interfere with and disrupt satellite communications while protecting one’s own communication. In view of the above background, a covert communication system model is proposed in the information confrontation environment. The exact closed-form result of the detection error probability (DEP) of the eavesdropping user is analyzed, and the optimal value of DEP and the optimal decision threshold are also given. Considering the behavior that intelligent eavesdroppers automatically select attacks and eavesdropping, combined with non-orthogonal multiple access (NOMA) technology and the imperfect successive interference cancellation (SIC) technology, the exact closed-form results of the outage probability of legitimate users and the intercept probability of eavesdroppers are derived, and the approximate results are also given in high signal-to-noise ratio (SNR). Finally, the accuracy of the theoretical derivation is verified by Monte Carlo simulation, and the influence of relevant system parameters is discussed concretely in the simulation. The numerical simulation results show that a reasonable selection of system parameters can improve the covert performance and anti-interference ability of the system, thereby achieving more reliable covert communication.

信息对抗的目的是在保护自身通信的同时，探测、干扰和破坏卫星通信。在此背景下，提出了一种信息对抗环境下的隐蔽通信系统模型。分析了窃听用户检测错误概率（DEP）的精确封闭形式，给出了DEP的最优值和最优决策阈值。考虑智能窃听者自动选择攻击和窃听的行为，结合非正交多址（NOMA）技术和不完全连续干扰消除（SIC）技术，推导了合法用户中断概率和窃听者拦截概率的精确封闭形式结果，并给出了在高信噪比（SNR）条件下的近似结果。最后，通过蒙特卡罗仿真验证了理论推导的准确性，并在仿真中具体讨论了相关系统参数的影响。数值仿真结果表明，合理选择系统参数可以提高系统的隐蔽性能和抗干扰能力，从而实现更可靠的隐蔽通信。

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引用次数: 0

DSFLS-Net: The multi-band cooperative spectrum sensing based on DSFLS-Net DSFLS-Net：基于DSFLS-Net的多波段协同频谱感知

IF 2.2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2026-02-01 Epub Date: 2025-12-06 DOI: 10.1016/j.phycom.2025.102932

Guanghai Xu, Xinran Mao, Yonghua Wang

To enhance detection performance and cross-scenario generalization in dynamic complex environments, an intelligent spectrum sensing method based on dual-scale feature learning is proposed. To fully utilize inter-band correlation information in multi-band signals and adaptively capture underlying patterns in data, this approach combines the Fast Fourier Transform (FFT) and the Discrete Wavelet Transform (DWT) for feature extraction and learning from both the frequency and time-frequency domains. The method first employs an FFT module to extract global frequency-domain features from multi-band signals, obtaining overall energy distribution and spectral characteristics across sub-bands. Simultaneously, a DWT module enables multiresolution time-frequency analysis to mine local time-frequency details. To reduce complexity in broadband detection tasks, a multi-label classification framework is adopted, providing a scalable solution for multi-scenario applications. Furthermore, to address class imbalance in training samples, a focal loss (FL) function is introduced to dynamically adjust learning weights, thereby improving sensing performance in complex environments. Simulations demonstrate that the proposed method achieves excellent detection performance, strong generalization capability, and good robustness across varying SNR conditions and dynamic complex scenarios, offering new insights for multi-band intelligent spectrum sensing.

为了提高动态复杂环境下的检测性能和跨场景泛化能力，提出了一种基于双尺度特征学习的智能频谱感知方法。为了充分利用多波段信号中的带间相关信息并自适应捕获数据中的潜在模式，该方法结合了快速傅里叶变换（FFT）和离散小波变换（DWT），从频域和时频域进行特征提取和学习。该方法首先利用FFT模块从多波段信号中提取全局频域特征，得到各子波段的总体能量分布和频谱特征。同时，DWT模块支持多分辨率时频分析，以挖掘本地时频细节。为了降低宽带检测任务的复杂性，采用了多标签分类框架，为多场景应用提供了可扩展的解决方案。此外，为了解决训练样本中的类不平衡问题，引入焦点损失（focal loss， FL）函数来动态调整学习权值，从而提高复杂环境下的传感性能。仿真结果表明，该方法在不同信噪比条件和动态复杂场景下具有优异的检测性能、较强的泛化能力和较好的鲁棒性，为多频段智能频谱感知提供了新的思路。

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引用次数: 0

The AIoT ecosystem for next-generation satellite systems 下一代卫星系统的AIoT生态系统

IF 2.2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2026-02-01 Epub Date: 2025-12-22 DOI: 10.1016/j.phycom.2025.102967

Waqas Iqrar , Kiran Khurshid , Sagheer Khan , Nasir Saeed

The rapid expansion of satellite mega-constellations and demand for low-latency connectivity pose significant operational challenges that traditional, static approaches cannot address. This paper asserts that the Artificial Intelligence of Things (AIoT) paradigm is the essential, transformative framework for future space communications, fundamentally integrating intelligent processing across the entire physical space and ground segment infrastructure. Moving beyond standard synthesis, this work provides a leading-edge unified AIoT taxonomy for satellite systems derived from a systematic examination of 100 recent publications. This work offers critical insights into the practical implementation and synergistic effects of AIoT across key applications in modern satellite networks, including ground station scheduling, dynamic network optimization, predictive maintenance, and physical security. The insights derived from this work demonstrate how the convergence of distributed sensing, intelligent analytics, and autonomous actuation transforms operations across space, ground, and link segments, a perspective often fragmented in the existing literature. This work highlights the unique utility of AIoT in enabling real-time detection of orbital debris and system interruptions. Furthermore, this work provides critical research frontiers that must be prioritized, addressing the multi-level optimization problem for extreme conditions, the lack of representative training datasets, and the engineering of robust, scalable security protocols against an expanding attack surface. By consolidating these applications and focusing on actionable future development paths, this paper serves as an essential strategic reference for researchers and professionals developing autonomous, resilient, and highly efficient space infrastructure.

卫星巨型星座的快速扩张和对低延迟连接的需求构成了传统静态方法无法解决的重大运营挑战。本文断言，物联网人工智能（AIoT）范式是未来空间通信必不可少的变革性框架，从根本上集成了整个物理空间和地面部分基础设施的智能处理。超越标准综合，这项工作为卫星系统提供了一个领先的统一AIoT分类，该分类来源于对100个最近出版物的系统检查。这项工作为AIoT在现代卫星网络关键应用中的实际实施和协同效应提供了重要见解，包括地面站调度、动态网络优化、预测性维护和物理安全。从这项工作中得出的见解表明，分布式传感、智能分析和自主驱动的融合如何改变跨空间、地面和链路段的操作，这一观点在现有文献中往往是支离破碎的。这项工作突出了AIoT在实现轨道碎片和系统中断实时检测方面的独特效用。此外，这项工作提供了必须优先考虑的关键研究前沿，解决了极端条件下的多级优化问题，缺乏代表性的训练数据集，以及针对不断扩大的攻击面设计健壮的、可扩展的安全协议。通过整合这些应用并关注可操作的未来发展路径，本文为研究人员和专业人员开发自主、弹性和高效的空间基础设施提供了重要的战略参考。

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引用次数: 0

LMTSA-MEC: A light weight mobility aware task and service assignment framework in mobile edge computing LMTSA-MEC：移动边缘计算中轻量级移动感知任务和服务分配框架

IF 2.2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2026-02-01 Epub Date: 2025-12-10 DOI: 10.1016/j.phycom.2025.102954

N. Rana Singha, Nityananda Sarma, Nabajyoti Medhi, Dilip Kumar Saikia

In the realm of mobile edge computing (MEC), efficient compute-intensive task offloading in MEC from mobile devices to nearby edge servers has become a critical area of research. However, the challenges posed by user mobility in 5G urban networks demand innovative solutions that optimize task offloading decisions while considering real-time mobility factors. Existing studies in this domain predominantly address either the delivery of results from previously offloaded users to their mobile devices or the migration of user application services to an edge server close to the user’s current location. However, there are applications which require simultaneous addressing of both of these issues in a very urgent manner. To address it, this paper introduces a LMTSA framework that combines lightweight user mobility prediction and decision-making techniques to optimally assign previously offloaded tasks’ result and services on demand basis. By leveraging a shallow recurrent neural network-based model to anticipate where users will go next and integrating a JAYA-based TOPSIS technique, LMTSA predicts user trajectories and identifies the most suitable edge servers to assign previously offloaded tasks’ result and services along their anticipated paths. The simulation results highlight how LMTSA significantly minimizes average application latency by 16.17 %, not only reducing offloading energy consumption but also improving the task completion rate and resource utilization with reasonable service migration frequency relative to the second best benchmark approach.

在移动边缘计算（MEC）领域，将MEC中的计算密集型任务从移动设备高效地卸载到附近的边缘服务器已成为一个关键的研究领域。然而，5G城市网络中用户移动性带来的挑战需要创新的解决方案，在考虑实时移动性因素的同时优化任务卸载决策。该领域的现有研究主要涉及将先前卸载的用户的结果交付到其移动设备或将用户应用程序服务迁移到靠近用户当前位置的边缘服务器。然而，有些应用程序需要以非常紧急的方式同时解决这两个问题。为了解决这个问题，本文引入了一个LMTSA框架，该框架结合了轻量级用户移动性预测和决策技术，以根据需求优化分配先前卸载的任务结果和服务。通过利用基于浅层循环神经网络的模型来预测用户下一步将去哪里，并集成基于jaya的TOPSIS技术，LMTSA预测用户轨迹，并确定最合适的边缘服务器，以沿着预期路径分配先前卸载的任务结果和服务。仿真结果表明，相对于第二种最佳基准方法，LMTSA将平均应用程序延迟显著降低了16.17%，不仅降低了卸载能耗，而且通过合理的服务迁移频率提高了任务完成率和资源利用率。

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引用次数: 0