Physical Communication最新文献_第9页

Hybrid AI-driven optimization for real-time 6G ad hoc communications using fluid antenna systems 使用流体天线系统的实时6G自组织通信的混合ai驱动优化

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.2025.102948

El Miloud Ar-Reyouchi , Ayoub Hadj-Sadek , Kamal Ghoumid , Sami Hage-Ali , Omar Elmazria

Future sixth-generation (6G) Ad Hoc networks must sustain ultra-reliable, low-latency, and energy-efficient connectivity in highly dynamic wireless environments, where interference management, real-time antenna reconfiguration, and computational constraints remain major challenges. Fluid Antenna Systems (FAS) provide additional spatial degrees of freedom through position- and shape-reconfigurable radiating elements, but existing optimization schemes for FAS and next-generation reconfigurable antennas either treat beamforming, phase control, and antenna positioning separately or rely on high-complexity Artificial Intelligence (AI) models that are difficult to deploy under slot-level latency and power budgets. This paper aims to design a unified, low-complexity framework for real-time control of FAS in 6G ad hoc networks. We propose AI-HFASO, a hybrid AI framework in which a Multi-Task Coordination Controller (MTCC) jointly optimizes beamforming, interference mitigation, and antenna positioning by integrating deep learning (DL) for fast beamforming initialization, reinforcement learning (RL) for adaptive element positioning, and a delay-aware genetic algorithm (GA) for phase refinement under latency constraints. The main novelty lies in the joint multi-objective optimization of spectral efficiency, interference suppression, and energy efficiency at the slot level, while reducing computational complexity through lightweight AI modules and a hybrid AI-traditional optimization loop. Simulation results under realistic multi-user, multi-cell 6G scenarios show that AI-HFASO achieves up to 31 % interference reduction, 21 % throughput improvement, and 18 % spectral-efficiency gain, while lowering computational overhead by about 30 % compared to state-of-the-art MIMO, RIS, and AI-based baselines, demonstrating its potential as a scalable and latency-aware solution for FAS-enabled 6G ad hoc networks.

未来的第六代（6G）自组织网络必须在高度动态的无线环境中保持超可靠、低延迟和高能效的连接，在这些环境中，干扰管理、实时天线重构和计算限制仍然是主要挑战。流体天线系统（FAS）通过位置和形状可重构的辐射元件提供了额外的空间自由度，但现有的FAS和下一代可重构天线优化方案要么单独处理波束形成、相位控制和天线定位，要么依赖于高复杂性的人工智能（AI）模型，这些模型很难在槽级延迟和功耗预算下部署。本文旨在为6G自组织网络中FAS的实时控制设计一个统一的、低复杂度的框架。我们提出了AI- hfaso，这是一种混合AI框架，其中多任务协调控制器（MTCC）通过集成用于快速波束形成初始化的深度学习（DL）、用于自适应元件定位的强化学习（RL）和用于延迟约束下相位优化的延迟感知遗传算法（GA），共同优化波束形成、干扰缓解和天线定位。其主要新颖之处在于频谱效率、干扰抑制和槽级能量效率的联合多目标优化，同时通过轻量级AI模块和AI-传统混合优化回路降低计算复杂度。在真实的多用户、多小区6G场景下的仿真结果表明，与最先进的MIMO、RIS和基于ai的基线相比，AI-HFASO实现了高达31%的干扰减少、21%的吞吐量提高和18%的频谱效率增益，同时降低了约30%的计算开销，证明了其作为支持fas的6G自组织网络的可扩展和延迟感知解决方案的潜力。

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引用次数: 0

Downlink optimization for direct-to-satellite IoT with LEO satellites and LoRaWAN 低轨道卫星和LoRaWAN直接到卫星物联网的下行链路优化

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

Physical Communication

Pub Date : 2026-02-01 Epub Date: 2025-12-21 DOI: 10.1016/j.phycom.2025.102971

Hassan Haghighi , Maamar El Amine Hamri , Davood Asadi , Daniel Delahaye

Direct-to-satellite communication systems for the Internet of Things, particularly those based on low-Earth-orbit satellite constellations, are emerging as a transformative solution to achieve global connectivity. However, ensuring efficient and reliable downlink communication from satellites to ground-based IoT devices remains a significant challenge due to intermittent satellite visibility, short contact durations, limited bandwidth, device energy constraints, and high network density. Unlike prior studies that primarily focus on uplink optimization, this work proposes a downlink-aware optimization framework that integrates satellite dynamics, LoRaWAN MAC constraints, and energy-aware scheduling. The framework accounts for physical-layer limitations, satellite visibility modeling, time-slot feasibility, and realistic system parameters consistent with LEO satellite operations. Simulations demonstrate that the proposed downlink-aware optimization framework improves the packet delivery ratio from 0.41 (achieved under random scheduling) to 0.96, while reducing the average energy consumption per successful transmission by approximately 55 %. These results highlight the efficiency of the proposed NSGA-II-based scheduling approach and provide an initial pattern that points toward potential scalability, compared to conventional non-optimized methods, demonstrating its promise for next-generation satellite-enabled IoT networks.

用于物联网的直接与卫星通信系统，特别是基于低地球轨道卫星星座的通信系统，正在成为实现全球连接的变革性解决方案。然而，由于间歇性卫星可见性、短接触持续时间、有限带宽、设备能量限制和高网络密度，确保从卫星到地面物联网设备的高效可靠下行通信仍然是一个重大挑战。与先前主要关注上行链路优化的研究不同，这项工作提出了一个下行链路感知优化框架，该框架集成了卫星动力学、LoRaWAN MAC约束和能量感知调度。该框架考虑了物理层限制、卫星可视性建模、时隙可行性以及与低轨道卫星运行相一致的现实系统参数。仿真结果表明，所提出的下行链路感知优化框架将分组传输率从随机调度下的0.41提高到0.96，同时将每次成功传输的平均能耗降低了约55%。这些结果突出了所提出的基于nsga - ii调度方法的效率，并提供了一个与传统非优化方法相比具有潜在可扩展性的初始模式，展示了其在下一代卫星支持物联网网络中的前景。

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引用次数: 0

Optimizing UAV trajectories for quality of service enhancement in 5G urban deployments 优化无人机轨迹，提高5G城市部署的服务质量

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

Physical Communication

Pub Date : 2026-02-01 Epub Date: 2025-12-05 DOI: 10.1016/j.phycom.2025.102951

Joaquin Salazar, Karel Toledo

The rapid proliferation of bandwidth-hungry applications and the limitations of fixed terrestrial infrastructure in urban environments have driven interest in deploying unmanned aerial vehicles (UAVs) as adaptive aerial base station (BS) within fifth generation (5G) wireless networks. In this work, we develop a three-dimensional (3D) simulation framework that incorporates 3GPP-standardized clustered delay line (CDL) channel models, probabilistic user mobility, 5G-compliant protocol stacks, and both single-input single-output (SISO) and multiple-input multiple-output (MIMO) transmission schemes in an urban macro cell (UMa) setting. We formulate a trajectory planning optimization problem that balances average signal-to-noise ratio (SNR) and its variability to derive flight paths under geographic constraints. Through extensive simulations, we evaluate multiple UAV mobility strategies including centroid following, maximum SNR expected value, minimum SNR standard deviation, weighted SNR trade-off, and fixed hovering, using throughput and block error rate (BLER) as the primary quality of service (QoS) performance indicators. Results show that the weighted SNR strategy achieves consistently high throughput with reduced variability, while its MIMO configuration yields an average throughput gain of 193% compared to SISO, highlighting the combined benefits of trajectory optimization and spatial diversity. These findings validate the proposed optimization criterion and underscore the substantial benefits of aerial mobility and spatial diversity in next-generation wireless networks.

带宽饥渴型应用的快速扩散以及城市环境中固定地面基础设施的局限性，促使人们对在第五代（5G）无线网络中部署无人机（uav）作为自适应空中基站（BS）产生了兴趣。在这项工作中，我们开发了一个三维（3D）仿真框架，该框架结合了3gpp标准化的集群延迟线（CDL）信道模型、概率用户移动性、5g兼容协议栈，以及城市宏小区（UMa）设置中的单输入单输出（SISO）和多输入多输出（MIMO）传输方案。我们提出了一个平衡平均信噪比（SNR）及其变异性的轨迹规划优化问题，以获得地理约束下的飞行路径。通过广泛的仿真，我们评估了多种无人机移动策略，包括质心跟随、最大信噪比期望值、最小信噪比标准差、加权信噪比权衡和固定悬停，并将吞吐量和块错误率（BLER）作为主要服务质量（QoS）性能指标。结果表明，加权信噪比策略在降低可变性的情况下实现了持续的高吞吐量，而其MIMO配置与SISO相比，平均吞吐量增益为193%，突出了轨迹优化和空间多样性的综合效益。这些发现验证了所提出的优化标准，并强调了下一代无线网络的空中移动性和空间多样性的实质性好处。

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引用次数: 0

Channel estimation in RIS-NOMA enabled 5G/B5G systems using meta-learned spatio-temporal graph neural networks 基于元学习时空图神经网络的基于RIS-NOMA的5G/B5G系统信道估计

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

Physical Communication

Pub Date : 2026-02-01 Epub Date: 2026-01-01 DOI: 10.1016/j.phycom.2025.102984

Aryan Mittal , Sandeep Kumar Singh , Amit Agarwal

Accurate and adaptive channel estimation is essential for realizing intelligent and energy-efficient reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) systems. However, conventional learning-based channel estimation (CE) models often struggle to generalize across varying SNR, frequency, and environmental dynamics. This paper proposes a novel First-Order Model-Agnostic Meta-Learning (FOMAML)-based Spatio-Temporal Graph Neural Network (STGNN) framework for robust and frequency-adaptive CE in RIS-assisted multi-user systems. The proposed model jointly captures spatial dependencies among RIS elements and temporal channel variations using edge-conditioned graph convolutions and gated recurrent units (GRUs), while meta-learning enables rapid adaptation to unseen conditions with minimal fine-tuning. A synthetic yet realistic dataset covering multiple carrier frequencies (3.5 GHz, 28 GHz, and 60 GHz) and SNR ranges (10-30 dB) is generated to evaluate performance. Simulation results demonstrate that the proposed FOMAML-STGNN achieves superior generalization, attaining up to 15% lower NRMSE and higher R² scores compared to conventional CNN-BiLSTM and full MAML-GNN baselines, while maintaining reduced computational complexity. Moreover, system-level evaluations confirm that RIS-NOMA consistently outperforms RIS-OMA in achievable sum rate, and controlled ablation studies validate the scalability and robustness of the proposed framework under varying SNRs, RIS sizes, and user densities. These results highlight the suitability of the proposed approach for real-time and scalable CE in beyond-5G (B5G) networks.

准确的自适应信道估计是实现可重构智能曲面（RIS）辅助非正交多址（NOMA）系统智能化、高能效的关键。然而，传统的基于学习的信道估计（CE）模型往往难以在不同的信噪比、频率和环境动态中进行泛化。本文提出了一种新的基于一阶模型不可知元学习（faml）的时空图神经网络（STGNN）框架，用于ris辅助多用户系统的鲁棒和频率自适应CE。所提出的模型使用边缘条件图卷积和门控循环单元（gru）共同捕获RIS元素之间的空间依赖关系和时间通道变化，而元学习能够以最小的微调快速适应未知条件。生成了一个合成但现实的数据集，涵盖多个载波频率（3.5 GHz， 28 GHz和60 GHz）和信噪比范围（10-30 dB），以评估性能。仿真结果表明，与传统的CNN-BiLSTM和完整的mail - gnn基线相比，所提出的FOMAML-STGNN实现了卓越的泛化，在保持较低的计算复杂度的同时，NRMSE降低了15%，R2得分更高。此外，系统级评估证实，RIS- noma在可实现的求和速率方面始终优于RIS- oma，控制消融研究验证了所提出框架在不同信噪比、RIS大小和用户密度下的可扩展性和鲁棒性。这些结果突出了所提出的方法在超5g （B5G）网络中实时和可扩展CE的适用性。

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引用次数: 0

Frequency-mixing RIS-induced channel decoupling and delay-Phase conversion for angular sensing 用于角传感的频率混合ris诱导通道解耦和延迟相位转换

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

Physical Communication

Pub Date : 2026-02-01 Epub Date: 2025-12-30 DOI: 10.1016/j.phycom.2025.102988

Xianglin Shi, Jiangtian Gu, Fengkai Chen, Jide Yuan

Reconfigurable intelligent surfaces (RIS) have emerged as a promising technology for 6G wireless systems. This paper investigates a space-time-coding metasurface (STCM)-based frequency-mixing RIS (SFMx-RIS) architecture for high-precision angular sensing. We first establish the fundamental operating principles of SFMx-RIS, theoretically deriving the amplitude-frequency and phase-frequency responses, and further revealing its capability for precise delay-phase conversion in the reflective pattern. Building on this foundation, we construct a frequency-decoupled communication system enabled by SFMx-RIS and examine the feasibility of such architecture for angular sensing applications. Leveraging the effective delay-phase conversion capability of SFMx-RIS, we propose a weighted iterative algorithm that utilizes spatial information from the propagation channel to achieve accurate angular sensing. Numerical results reveal that SFMx-RIS can achieve extremely high-precision angular estimation accuracy in line-of-sight environments, highlighting the strong potential of SFMx-RIS for angular sensing applications.

可重构智能表面（RIS）已成为6G无线系统的一项有前途的技术。研究了一种基于空时编码元表面（STCM）的高精度角度传感混频RIS （SFMx-RIS）结构。我们首先建立了SFMx-RIS的基本工作原理，从理论上推导了其幅频和相频响应，并进一步揭示了其在反射方向图中精确延迟相位转换的能力。在此基础上，我们构建了一个由SFMx-RIS支持的频率解耦通信系统，并研究了这种架构在角度传感应用中的可行性。利用SFMx-RIS有效的延迟相位转换能力，我们提出了一种加权迭代算法，利用传播信道的空间信息来实现精确的角度感知。数值结果表明，SFMx-RIS在视距环境下可以实现极高精度的角度估计精度，突出了SFMx-RIS在角度传感应用中的强大潜力。

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引用次数: 0

Performance analysis of IRS-UAV-assisted active jamming receiver against active eavesdropper 红外-无人机辅助有源干扰接收机抗有源窃听器性能分析

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

Physical Communication

Pub Date : 2026-02-01 Epub Date: 2025-12-29 DOI: 10.1016/j.phycom.2025.102978

Yi Shen , Yuxin Xin , Ping Tan

We propose a secure transmission system in the presence of an illegal user on the ground. Due to the shielding of obstacles, the ground source is unable to directly transmit confidential information to the ground destination. Therefore, the ground source relays the confidential information to the ground destination through a jointly deployed relay system comprising intelligent reflecting surface and unmanned aerial vehicle (IRS-UAV). The illegal user either monitors the legitimate communication link or actively eavesdrops on legitimate information. Consequently, we consider that the destination employs an active jamming strategy to disrupt the eavesdropper’s reception, thereby enhancing the security and covert performance of the legitimate communication. Based on this scenario, we derive approximate expressions for the detection error probability, covert rate, transmission outage probability, and secrecy outage probability, and analyze the corresponding secure and covert performance. Simulations verify the correctness of the derived formulas and determine the optimal parameters for the destination and illegal user. Analysis demonstrates that introducing active interference by the legitimate destination can prevent the warden from detecting the covert communication with absolute certainty. Furthermore, the deployment of an active IRS is shown to significantly enhance the overall security performance of the system. Conversely, active interference by the eavesdropper increases the secrecy outage probability, thereby degrading secure transmission performance.

我们提出了一种安全的传输系统，以应对地面上的非法用户。由于障碍物的屏蔽，地源无法直接将机密信息传输到地面目的地。因此，地源通过智能反射面和无人机（IRS-UAV）联合部署的中继系统将机密信息传递到地面目的地。非法用户要么监视合法的通信链路，要么主动窃听合法的信息。因此，我们认为目的地采用有源干扰策略来干扰窃听者的接收，从而提高合法通信的安全性和隐蔽性。在此基础上，导出了检测错误概率、隐蔽率、传输中断概率和保密中断概率的近似表达式，并分析了相应的安全性能和隐蔽性能。仿真验证了推导公式的正确性，并确定了目的地和非法用户的最优参数。分析表明，引入合法目的地的主动干扰可以防止监狱长绝对确定地检测到隐蔽通信。此外，主动IRS的部署可以显著提高系统的整体安全性能。相反，窃听者的主动干扰增加了保密中断的概率，从而降低了安全传输的性能。

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引用次数: 0

Joint beamforming and phase optimization for UAV-RIS systems by gradient-based adaptive meta-learning 基于梯度自适应元学习的无人机- ris系统联合波束形成与相位优化

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

Physical Communication

Pub Date : 2026-02-01 Epub Date: 2025-11-28 DOI: 10.1016/j.phycom.2025.102942

Shibao Li , Qishuai Guan , Yunwu Zhang , Xuerong Cui , Huajun Song , Zhaozhi Gu , Lianghai Li

With the rapid advancement of wireless communication technologies, achieving high reliability, low latency, and energy efficiency remains a critical challenge. Unmanned Aerial Vehicle-mounted Reconfigurable Intelligent Surfaces (UAV-RIS) leverage both the reconfigurability of RIS and the mobility of UAVs to enhance air-to-ground link quality. This paper investigates the joint optimization of UAV trajectory, base station (BS) beamforming, and RIS phase control in UAV-RIS-assisted systems. A density-aware positioning algorithm is first introduced to optimize the UAV trajectory and improve link robustness. Building upon this, a gradient-based adaptive meta-learning (GAML) framework is developed to jointly optimize BS beamforming and RIS phase shifts without extensive pre-training, thereby enhancing adaptability in time-varying channels. The proposed GAML scheme avoids explicit non-convex optimization and significantly reduces computational complexity. Furthermore, the integration of adaptive network structures enhances gradient propagation and mitigates vanishing gradients. Simulation results show that GAML outperforms existing learning-based and random phase methods, achieving up to 14.25% improvement in weighted sum rate (WSR), demonstrating its superior adaptability and efficiency.

随着无线通信技术的快速发展，实现高可靠性、低延迟和能源效率仍然是一个关键的挑战。无人机可重构智能表面（UAV-RIS）利用RIS的可重构性和无人机的机动性来提高空对地链路质量。研究了无人机-RIS辅助系统中无人机弹道、基站波束形成和RIS相位控制的联合优化问题。首先提出了一种密度感知定位算法，用于优化无人机轨迹，提高链路鲁棒性。在此基础上，开发了基于梯度的自适应元学习（GAML）框架，无需大量预训练即可联合优化BS波束形成和RIS相移，从而增强了时变信道的适应性。该方案避免了显式非凸优化，显著降低了计算复杂度。此外，自适应网络结构的集成增强了梯度传播，减轻了梯度消失。仿真结果表明，GAML算法优于现有的基于学习和随机相位的方法，加权和率（WSR）提高了14.25%，显示了其优越的适应性和效率。

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引用次数: 0

Hybrid LSTM-actor-critic framework for temporal-spatial wideband beam tracking in 6G THz massive MIMO 6G THz大规模MIMO中时空宽带波束跟踪的混合LSTM-actor-critic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: 2025-11-30 DOI: 10.1016/j.phycom.2025.102933

Siva Kumar T, Jeyakumar P

Efficient beam tracking is essential for stable high-throughput communication in ultra-wideband terahertz (THz) massive MIMO systems. However, existing reinforcement learning (RL)-based methods often overlook temporal channel variations and beam squint effects inherent to wideband THz links. To address this, we propose a Hybrid Long Short-Term Memory–Actor-Critic (LSTM–A2C) framework that integrates temporal prediction and spatial decision-making for adaptive beam tracking in 6G THz systems. The LSTM captures beam dynamics over time, while the Actor–Critic learner adjusts beamforming vectors in real time under mobility and hardware impairments. Simulations under realistic THz conditions (with phase noise, ADC quantization, and beam squint) show that LSTM–A2C achieves 15–25 % higher tracking accuracy and 30–40 % lower error compared to DQN, PPO, and EKF methods. It also delivers 3.5 bps/Hz spectral efficiency at 10 dB SNR, 2.1 bits/Joule energy efficiency, and a fairness index of 0.99 in multi-user settings. The reward curve shows faster convergence ( ≈  200 epochs), validating the proposed framework’s efficiency.

在超宽带太赫兹（THz）大规模MIMO系统中，高效的波束跟踪是稳定高吞吐量通信的关键。然而，现有的基于强化学习（RL）的方法往往忽略了宽带太赫兹链路固有的时间信道变化和波束斜视效应。为了解决这个问题，我们提出了一个混合长短期记忆-行为者-批评（LSTM-A2C）框架，该框架集成了6G太赫兹系统中自适应波束跟踪的时间预测和空间决策。LSTM捕获随时间变化的波束动态，而Actor-Critic学习器在移动和硬件损坏情况下实时调整波束形成矢量。在现实太赫兹条件下（含相位噪声、ADC量化和波束斜视）的仿真表明，与DQN、PPO和EKF方法相比，LSTM-A2C的跟踪精度提高15 - 25%，误差降低30 - 40%。在10db信噪比下，它还提供3.5 bps/Hz的频谱效率，2.1比特/焦耳的能量效率，多用户设置下的公平性指数为0.99。奖励曲线显示出更快的收敛速度（ ≈ 200 epoch），验证了所提出框架的效率。

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引用次数: 0

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

Phase noise mitigation in higher order probabilistic constellation shaping for high-capacity optical communications 大容量光通信中高阶概率星座成形中的相位噪声抑制

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

Physical Communication

Pub Date : 2026-02-01 Epub Date: 2025-11-30 DOI: 10.1016/j.phycom.2025.102946

Sunil Narayan Thool, Devendra Chack

The advancement of Probabilistic Constellation Shaping (PCS) is pivotal for high-capacity optical communication systems and the forthcoming 6 G era. PCS aims to approach the Shannon limit, optimising signal efficiency and performance. However, phase noise from continuous-wave (CW) lasers significantly impacts system performance, especially in terms of bit error rate (BER). This paper introduces and evaluates phase noise estimation and correction using the Viterbi & Viterbi (V&V) algorithm at a 50 Gbaud symbol rate in dual-polarised high-capacity optical systems employing higher-order Quadrature Amplitude Modulation (QAM) schemes, such as 64-QAM and 256-QAM PCS. A comparative analysis with the Blind Phase Search (BPS) algorithm demonstrates the V&V algorithm’s ability to maintain BER of 10^–5 and 10^–4 for 64-QAM and 256-QAM PCS, respectively, under CW laser linewidth of 1 MHz. Extensive numerical results demonstrate that the V&V algorithm consistently meets the other analytical parameter requirements of the optical link under CW laser linewidths ranging from 10 kHz to 1 MHz. The implemented system design emphasises the V&V algorithm’s robustness in handling phase noise, highlighting its potential to enhance reliability and performance in high-capacity optical communication systems. The findings underscore the potential of our approach to meet the forthcoming requirements of optical links.

概率星座成形（PCS）技术的发展对于大容量光通信系统和即将到来的6g时代至关重要。PCS旨在接近香农极限，优化信号效率和性能。然而，连续波（CW）激光器的相位噪声会严重影响系统性能，尤其是误码率（BER）。本文介绍并评估了采用高阶正交调幅（QAM）方案（如64-QAM和256-QAM PCS）的双偏振大容量光学系统在50 Gbaud符号率下使用Viterbi &； Viterbi （V&；V）算法的相位噪声估计和校正。与盲相位搜索（Blind Phase Search， BPS）算法的对比分析表明，在连续波激光线宽为1 MHz的情况下，V&；V算法对64-QAM和256-QAM pc分别保持10-5和10-4的误码率。大量的数值结果表明，在10 kHz至1 MHz的连续波激光线宽范围内，V&；V算法始终满足光链路的其他分析参数要求。实现的系统设计强调了V&；V算法在处理相位噪声方面的鲁棒性，突出了其在高容量光通信系统中提高可靠性和性能的潜力。这些发现强调了我们的方法在满足即将到来的光链路需求方面的潜力。

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引用次数: 0