Physical Communication最新文献_第6页

Shortwave signal modulation recognition method using adaptive time-Frequency threshold denoising and feature fusion 短波信号调制识别方法采用自适应时频阈值去噪和特征融合

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

Physical Communication

Pub Date : 2026-03-01 Epub Date: 2026-01-06 DOI: 10.1016/j.phycom.2026.102994

Chen Shen , Tingting Lyu , Yu Li , Tianqi Lin , Yulong Liu

Automatic modulation classification (AMC) techniques are crucial for cognitive radio and communication systems. However, in low signal-to-noise ratio (SNR) conditions, transient shortwave signals are highly vulnerable to noise interference. This vulnerability leads to a reduction in identification accuracy. Medium time scale shortwave signals offer more stable characteristics. However, these signals are influenced by the time-varying SNR. This effect causes the energy density distribution to become discrete, thereby leading to lower recognition accuracy. To address this issue, this paper proposes a new architecture combining the adaptive time-frequency threshold denoising (ATFTD) algorithm and dual-modal feature fusion to enhance the modulation recognition accuracy of medium time scale shortwave signals. First, the signals are transformed into two types of time-frequency images (TFIs) using smoothed pseudo Wigner-Ville distribution (SPWVD) and Born-Jordan distribution (BJD). Subsequently, the ATFTD algorithm denoises these two TFIs. Next, the denoised TFIs are input into deep networks for feature extraction, and Jensen-Shannon divergence (JSD) is employed for fusion. Meanwhile, the time-domain statistical features of the signals are extracted and concatenated with the fused TFI features. Finally, the concatenated features are fed into a fully connected network for classification. Experimental results demonstrate that the proposed solution achieves over 90% recognition accuracy across six deep learning networks (AlexNet, ResNet18, VGGNet16, DenseNet121, ResNet50, and ResNet152), with the best performance observed in the ResNet152 network, ultimately reaching an average recognition accuracy of 99.625%.

自动调制分类（AMC）技术是认知无线电通信系统的关键技术。然而，在低信噪比条件下，瞬态短波信号极易受到噪声干扰。这个漏洞会降低识别的准确性。中时间尺度短波信号具有更稳定的特性。然而，这些信号受到时变信噪比的影响。这种影响导致能量密度分布变得离散，从而导致识别精度降低。针对这一问题，本文提出了一种将自适应时频阈值去噪（ATFTD）算法与双峰特征融合相结合的新架构，以提高中时间尺度短波信号的调制识别精度。首先，利用平滑伪Wigner-Ville分布（SPWVD）和Born-Jordan分布（BJD）将信号变换成两种时频图像（tfi）。随后，ATFTD算法对这两个tfi进行去噪。然后，将去噪后的tfi输入深度网络进行特征提取，并利用Jensen-Shannon散度（JSD）进行融合。同时，提取信号的时域统计特征，并与融合后的TFI特征进行拼接。最后，将连接的特征输入到一个全连接的网络中进行分类。实验结果表明，该方案在六个深度学习网络（AlexNet、ResNet18、VGGNet16、DenseNet121、ResNet50和ResNet152）上的识别准确率超过90%，其中ResNet152网络的识别准确率最高，达到99.625%的平均识别准确率。

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引用次数: 0

A RIS-Programmable hybrid uplink for grant-Free NOMA networks with distributed signal aggregation 基于分布式信号聚合的无授权NOMA网络的ris -可编程混合上行链路

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

Physical Communication

Pub Date : 2026-03-01 Epub Date: 2026-02-14 DOI: 10.1016/j.phycom.2026.103047

Emmanuel Atebawone , Kwame Opuni-Boachie Obour Agyekum , James Dzisi Gadze , Kwasi Adu-Boahen Opare , Owusu Agyeman Antwi , Robert Akromond

Grant-free uplink transmission enables scalable connectivity in dense wireless networks but poses fundamental challenges for reliable signal aggregation under interference- and collision-dominated access. Over-the-air analog aggregation offers low-latency superposition-based processing but is highly vulnerable to collision-induced distortion, whereas digital decoding improves reliability at the expense of reduced parallelism and increased overhead. Reconfigurable intelligent surfaces (RISs) provide a programmable propagation mechanism that can actively reshape uplink signal superposition, rather than merely enhancing individual channel gains. This paper proposes a RIS-programmable hybrid uplink framework for grant-free non-orthogonal multiple access (GF-NOMA) systems, in which the wireless environment is dynamically configured to enable either coherent analog aggregation or successive interference cancellation (SIC)-based decoding depending on instantaneous access conditions. A lightweight adaptive control policy jointly selects the uplink aggregation mode and RIS phase configuration on a per-round basis, allowing explicit regulation of aggregation distortion and access reliability without scheduling or retransmission control. Analytical results characterize the bias-variance behavior of the aggregated gradients under hybrid operation, revealing how RIS-induced coherence and partitioning govern aggregation accuracy. Simulation studies using distributed learning workloads demonstrate significant improvements in convergence efficiency and robustness under dense access, heterogeneous data distributions, and interference-dominated regimes compared with fixed-mode RIS-assisted and grant-free baselines. These results confirm the effectiveness of RIS-enabled adaptive uplink control for reliable signal aggregation in large-scale grant-free wireless networks.

无授权上行链路传输能够在密集无线网络中实现可扩展连接，但在干扰和冲突为主的接入下，对可靠的信号聚合提出了根本性挑战。空中模拟聚合提供了低延迟的基于叠加的处理，但极易受到碰撞引起的失真的影响，而数字解码以降低并行性和增加开销为代价提高了可靠性。可重构智能表面（RISs）提供了一种可编程的传播机制，可以主动重塑上行信号叠加，而不仅仅是提高单个信道增益。本文提出了一种用于无授权非正交多址（GF-NOMA）系统的ris -可编程混合上行链路框架，其中无线环境被动态配置为根据瞬时接入条件实现相干模拟聚合或基于连续干扰消除（SIC）的解码。轻量级的自适应控制策略可以按轮联合选择上行链路聚合方式和RIS相位配置，不需要调度和重传控制，可以对汇聚失真和接入可靠性进行显式调节。分析结果描述了混合操作下聚合梯度的偏方差行为，揭示了ris诱导的相干性和分区如何影响聚合精度。使用分布式学习工作负载的仿真研究表明，与固定模式ris辅助和无授权基线相比，在密集访问、异构数据分布和干扰主导的制度下，收敛效率和鲁棒性有了显著提高。这些结果证实了RIS-enabled自适应上行链路控制在大规模无授权无线网络中可靠信号聚合的有效性。

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引用次数: 0

Relay-driven magnetic induction communication with enhanced coverage-Data rate trade-offs for transboundary UUV control and information exchange 中继驱动磁感应通信与增强覆盖-跨界UUV控制和信息交换的数据速率权衡

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

Physical Communication

Pub Date : 2026-03-01 Epub Date: 2026-01-10 DOI: 10.1016/j.phycom.2026.102996

Ke Yang, Jiarui Yang, Mingyu Gao, Peng Lin, Xin Zhang

Magnetic induction (MI) communication across the air-sea boundary (transboundary MI) offers unique advantages for seamless information exchange between aerial and underwater platforms. However, its performance, quantified by the product of coverage range and data rate, is fundamentally constrained by the rapid attenuation of MI signals with distance and frequency. This paper presents a novel relay transmission framework to address such limitations by enabling distributed superposition of magnetic induction fields. The proposed method extends MI propagation from the near-field to medium/far-field regimes, thereby mitigating signal attenuation while enhancing transmission robustness. A comprehensive propagation model and channel characterization are developed, along with closed-form expressions for channel capacity. Through systematic simulations guided by underwater coverage threshold lines, the communication range, achievable bandwidth, and Coverage×Data-Rate performance limits are rigorously evaluated under diverse relay configurations. Numerical results demonstrate that optimized relay strategies not only enable transboundary MI signals to penetrate expected underwater depths but also elevate data rates by up to 10-fold compared to conventional non-relay systems. This breakthrough significantly extends the theoretical and practical performance boundaries of transboundary MI communication, establishing relay-aided architectures as a transformative paradigm for next-generation cross-domain UUV networks.

跨海气界磁感应通信为空中和水下平台之间的无缝信息交换提供了独特的优势。然而，它的性能，用覆盖范围和数据速率的乘积来量化，从根本上受到MI信号随距离和频率的快速衰减的限制。本文提出了一种新的继电器传输框架，通过实现磁感应场的分布叠加来解决这些限制。该方法将MI的传播范围从近场扩展到中/远场，从而在增强传输鲁棒性的同时减轻了信号衰减。建立了一个全面的传播模型和信道特性，以及信道容量的封闭形式表达式。通过水下覆盖阈值线引导下的系统仿真，严格评估了不同中继配置下的通信范围、可实现带宽和Coverage×Data-Rate性能限制。数值结果表明，优化后的中继策略不仅使跨界MI信号能够穿透预期的水下深度，而且与传统的非中继系统相比，数据速率提高了10倍。这一突破极大地扩展了跨界MI通信的理论和实践性能边界，建立了中继辅助架构，作为下一代跨域UUV网络的变革范例。

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引用次数: 0

GMSANet: A hybrid CNN-transformer network for CSI feedback GMSANet：一种用于CSI反馈的混合cnn -变压器网络

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

Physical Communication

Pub Date : 2026-03-01 Epub Date: 2026-01-17 DOI: 10.1016/j.phycom.2026.103010

Wenhong Pan, Junjie Wu, Jiangnan Yuan

In massive multiple-input multiple-output (MIMO) systems operating under the frequency division duplexing (FDD) mode, user equipment (UE) is required to feed back downlink channel state information (CSI) to the base station (BS). As the number of antennas increases, CSI feedback suffers from excessive bandwidth overhead. To reduce the feedback cost and facilitate practical deployment, both convolutional neural network (CNN)-based and Transformer-based methods have achieved remarkable success in CSI feedback. However, CNN-based approaches are inherently limited by convolution operations and struggle to effectively capture global contextual information. Meanwhile, Transformer-based approaches often face insufficient local feature modeling and high computational complexity caused by the self-attention mechanism. To address these limitations, this paper proposes a novel CNN-Transformer hybrid architecture Gated Multi-Scale Additive Attention Network, termed GMSANet. The proposed framework is built upon two key ideas. First, we introduce a new Multi-Scale Additive Attention (MSAA) mechanism that can effectively extract multi-scale features from the CSI matrix while modeling long-range correlations with reduced computational complexity. Second, by employing gated linear units (GLU) as channel mixers, the model captures frequency-domain correlations among adjacent subcarriers, thereby enhancing local modeling capability and improving robustness. Simulation results demonstrate that the proposed network achieves superior CSI reconstruction performance while reducing computational complexity.

在工作在频分双工（FDD）模式下的大规模多输入多输出（MIMO）系统中，用户设备（UE）需要向基站（BS）反馈下行信道状态信息（CSI）。随着天线数量的增加，CSI反馈受到带宽开销过大的困扰。为了降低反馈成本和便于实际部署，基于卷积神经网络（CNN）和基于transformer的方法在CSI反馈中都取得了显著的成功。然而，基于cnn的方法固有地受到卷积操作的限制，并且难以有效地捕获全局上下文信息。同时，基于transformer的方法往往面临局部特征建模不足和自关注机制导致的计算复杂度高的问题。为了解决这些限制，本文提出了一种新颖的CNN-Transformer混合架构门控多尺度加性注意力网络，称为GMSANet。拟议的框架建立在两个关键思想之上。首先，我们引入了一种新的多尺度加性注意（MSAA）机制，该机制可以有效地从CSI矩阵中提取多尺度特征，同时降低了计算复杂度。其次，通过采用门控线性单元（GLU）作为信道混频器，该模型捕获了相邻子载波之间的频域相关性，从而增强了局部建模能力并提高了鲁棒性。仿真结果表明，该网络在降低计算复杂度的同时取得了较好的CSI重构性能。

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引用次数: 0

Seasonally adaptive power control for NOMA-mMIMO mmWave systems under foliage-induced attenuation 树叶诱导衰减下NOMA-mMIMO毫米波系统的季节性自适应功率控制

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

Physical Communication

Pub Date : 2026-03-01 Epub Date: 2026-02-21 DOI: 10.1016/j.phycom.2026.103054

Mourtada Oubassghir, Mohamed Boulouird

The presence of vegetation between base stations and users in outdoor non-orthogonal multiple access (NOMA), massive multiple-input multiple-output (mMIMO), and millimeter-wave (mmWave) networks introduces seasonal signal attenuation, as foliage density and structure vary throughout the year. These fluctuations cause substantial performance variations, challenging reliable and energy-efficient operation.

This paper proposes a seasonally adaptive power control framework to compensate for foliage-induced attenuation in downlink NOMA-mMIMO mmWave systems. The core idea is to select a reference month and adjust the base station transmit power, raising or lowering it as needed, to maintain consistent throughput across the year relative to the chosen month. The framework employs a bisection-based optimization algorithm to determine the minimum transmit power required each month to maintain the reference spectral efficiency (SE).

Simulation results show that the approach stabilizes SE year-round and reveals a trade-off between SE and energy efficiency (EE): a high-SE reference stabilizes throughput but lowers EE in dense-foliage months, whereas a low-SE reference improves EE by reducing power but limits throughput. The framework is tunable for stability or efficiency, offering practical guidance for sustainable fifth- and sixth-generation (5G/6G) deployments in foliage-affected outdoor environments.

在室外非正交多址（NOMA）、大规模多输入多输出（mMIMO）和毫米波（mmWave）网络中，基站和用户之间存在植被，会导致季节性信号衰减，因为树叶密度和结构全年都在变化。这些波动造成了巨大的性能变化，挑战了可靠和节能的操作。本文提出了一种季节性自适应功率控制框架，以补偿下行NOMA-mMIMO毫米波系统中树叶引起的衰减。其核心思想是选择一个参考月份并调整基站发射功率，根据需要提高或降低它，以保持全年相对于所选月份的一致吞吐量。该框架采用基于分割的优化算法来确定维持参考频谱效率（SE）每月所需的最小发射功率。仿真结果表明，该方法全年稳定SE，并揭示了SE和能效（EE）之间的权衡：高SE参考稳定吞吐量，但在茂密的季节降低EE，而低SE参考通过降低功率提高EE，但限制吞吐量。该框架可根据稳定性或效率进行调整，为在受植被影响的室外环境中可持续的第五代和第六代（5G/6G）部署提供实用指导。

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引用次数: 0

High-accuracy 3D indoor positioning via OFDM-based VLC using PDoA 利用PDoA的基于ofdm的VLC进行高精度室内三维定位

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

Physical Communication

Pub Date : 2026-03-01 Epub Date: 2026-02-09 DOI: 10.1016/j.phycom.2026.103039

Saeed Amini, S. Alireza Nezamalhosseini, Mahdi Nassiri

This paper presents a novel visible light communication (VLC)-based indoor positioning system that integrates orthogonal frequency-division multiplexing (OFDM) with frequency-dependent subcarrier phase estimation to achieve high-precision distance measurement. The proposed approach exploits the phase difference of arrival technique, where the phase slope across OFDM subcarriers is extracted and the inter-LED phase difference is used for three-dimensional (3D) localization. Unlike conventional schemes relying on received signal strength, time of arrival, or angle of arrival, the proposed system derives spatial information directly from the inherent phase continuity of OFDM subcarriers, thus eliminating calibration and synchronization dependencies. The OFDM framing and subcarrier structure are optimized to improve both spectral efficiency and bit-error-rate performance, ensuring robust phase recovery under realistic VLC conditions. Simulation results in a standard indoor environment demonstrate an average 3D localization error of approximately 1.41 cm, which significantly outperforms existing VLC-based positioning schemes. Owing to its scalability, practicality, and cost efficiency, the proposed method offers strong potential for high-precision indoor localization in smart buildings, industrial automation, and healthcare applications.

提出了一种基于可见光通信（VLC）的室内定位系统，该系统将正交频分复用（OFDM）与频率相关的子载波相位估计相结合，实现了高精度的距离测量。该方法利用到达相位差技术，提取OFDM子载波间的相位斜率，并利用led间的相位差进行三维定位。与传统的依赖于接收信号强度、到达时间或到达角度的方案不同，该系统直接从OFDM子载波固有的相位连续性中获取空间信息，从而消除了校准和同步依赖。优化了OFDM分帧和子载波结构，提高了频谱效率和误码率性能，确保了在实际VLC条件下的鲁棒相位恢复。在标准室内环境下的仿真结果表明，平均三维定位误差约为1.41 cm，明显优于现有的基于vlc的定位方案。由于其可扩展性、实用性和成本效益，该方法在智能建筑、工业自动化和医疗保健应用中具有很强的高精度室内定位潜力。

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引用次数: 0

A hybrid CNN-transformer based automatic modulation recognition system for underwater acoustic communication: Method and implementation 基于混合cnn -变压器的水声通信自动调制识别系统：方法与实现

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

Physical Communication

Pub Date : 2026-03-01 Epub Date: 2026-01-24 DOI: 10.1016/j.phycom.2026.103018

Wenjie Huang, Sidan Yang, Xinyue Hou, Rong Fan, Yishan Su

Underwater acoustic (UWA) communication plays an essential role in marine exploration and oceanic resource utilization. Automatic modulation recognition (AMR) is a key technique for identifying the modulation of received signals without relying on prior transmitter information, and it is crucial for ensuring reliable and adaptive UWA communication. However, the AMR of UWA signals remains a challenging task due to severe multipath propagation, Doppler effects, and dynamic channel characteristics. To address these issues, this paper proposes a hybrid CNN-Transformer architecture, for efficient and accurate AMR. The CNN module with Multi-Scale Complex-Valued convolutions extracts robust local features under multipath and Doppler distortions, while the Transformer with a window self-attention mechanism captures long-range dependencies with reduced computational complexity. This design effectively balances recognition accuracy and computational efficiency, enabling deployment on edge devices. Experimental results across three field-collected datasets covering lake, open-sea and gulf environments demonstrate that the proposed model consistently delivers superior AMR performance, achieving accuracies of 98.83%, 95.75%, and 92.29% under increasingly challenging channel conditions. Additionally, a complete UAC-AMR system is designed and implemented in this study, where the proposed model achieves an NPU inference latency of 0.96 ms on the edge platform, fully meeting the real-time requirements of UWA communication.

水声通信在海洋勘探和海洋资源利用中发挥着重要作用。调制自动识别（AMR）是在不依赖于发送方先验信息的情况下识别接收信号调制的关键技术，是保证UWA通信可靠、自适应的关键。然而，由于严重的多径传播、多普勒效应和动态信道特性，UWA信号的AMR仍然是一项具有挑战性的任务。为了解决这些问题，本文提出了一种混合CNN-Transformer架构，以实现高效和准确的AMR。具有多尺度复值卷积的CNN模块在多径和多普勒失真下提取鲁棒的局部特征，而具有窗口自关注机制的Transformer捕获远程依赖关系，降低了计算复杂度。该设计有效地平衡了识别精度和计算效率，从而实现了在边缘设备上的部署。在湖泊、公海和海湾环境的三个现场采集数据集上的实验结果表明，该模型始终具有卓越的AMR性能，在日益严峻的航道条件下，准确率分别达到98.83%、95.75%和92.29%。此外，本研究还设计并实现了一个完整的UAC-AMR系统，提出的模型在边缘平台上实现了0.96 ms的NPU推理延迟，完全满足UWA通信的实时性要求。

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引用次数: 0

A two-stage acoustic source localization algorithm incorporating frequency-dependent atmospheric absorption 结合频率相关大气吸收的两级声源定位算法

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

Physical Communication

Pub Date : 2026-03-01 Epub Date: 2026-01-06 DOI: 10.1016/j.phycom.2026.102997

Tran Trong Tai, Truong Van Tuan

This paper presents a novel two-stage acoustic source localization algorithm that adapts to frequency-dependent atmospheric absorption in outdoor environments. The first stage leverages multiband energy distribution characteristics to efficiently obtain a coarse estimate of the source position. The second stage refines this estimate by optimizing a frequency-weighted steered response power (SRP) function. The absorption model is constructed based on the ANSI S1.26–2014 standard, incorporating realistic environmental parameters such as humidity, temperature, and pressure. Simulation results demonstrate that the proposed method achieves high localization accuracy approaching the Cramér–Rao lower bound (CRLB), while maintaining computational costs comparable to conventional SRP algorithms. The method is especially effective under long-range propagation (beyond 5 km) and strong absorption conditions, which are typical in applications such as security monitoring, UAV tracking, and gunshot detection. The main contribution lies in the proposed “absorption-aware” localization mechanism, which significantly enhances performance in real-world settings where spectral distortion due to atmospheric effects is substantial and often overlooked by existing methods.

本文提出了一种新的两级声源定位算法，该算法适应室外环境中频率相关的大气吸收。第一级利用多波段能量分布特性有效地获得源位置的粗略估计。第二阶段通过优化频率加权转向响应功率（SRP）函数来改进该估计。吸收模型是基于ANSI S1.26-2014标准构建的，纳入了现实的环境参数，如湿度、温度和压力。仿真结果表明，该方法在保持与传统SRP算法相当的计算成本的同时，获得了接近cram r - rao下界的较高定位精度。该方法在远程传播（超过5公里）和强吸收条件下特别有效，这在安全监控、无人机跟踪和枪击探测等应用中是典型的。主要贡献在于提出的“吸收感知”定位机制，该机制显著提高了实际环境中的性能，在实际环境中，由于大气效应造成的光谱失真很大，并且通常被现有方法忽略。

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引用次数: 0

Joint doppler-delay quasi-stationarity region analysis for high-speed railway communication channels 高速铁路通信信道联合多普勒-延迟拟平稳区分析

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

Physical Communication

Pub Date : 2026-03-01 Epub Date: 2026-01-08 DOI: 10.1016/j.phycom.2025.102981

Jiachi Zhang , Rongchen Sun , Dongmei Liu , Baoyue Meng , Liu Liu

This paper proposes a novel method for estimating channel quasi-stationary regions (QSRs) using joint Doppler-delay power profiles (DDPPs), with a focus on high-speed railway (HSR) channels. Conventional non-stationarity assessment methods, which rely primarily on power delay profiles (PDPs), may yield inaccurate QSR estimates, especially near track-side transceiver stations due to symmetric propagation conditions. By incorporating Doppler-delay information, the proposed DDPP-based approach significantly improves QSR identification accuracy. The method is validated using real channel measurements at 2.35 GHz from the Zhengzhou-Xi’an HSR line, covering both viaduct and cutting scenarios. Results indicate that the DDPP-based definition not only avoids false QSR estimations but also produces generally smaller QSR values than the PDP-based method for a given threshold. Moreover, the viaduct scenario exhibits larger QSRs than the cutting scenario. For instance, at a threshold of 0.7, the QSR values near the trackside receiver in the cutting scenario are 0.98 m (DDPP-based) versus 9.34 m (PDP-based), while in the viaduct scenario, the values are 2.03 m (DDPP-based) and 31.61 m (PDP-based), highlighting the method’s ability to capture environment-dependent stationarity characteristics.

针对高速铁路信道，提出了一种利用联合多普勒-延迟功率谱（DDPPs）估计信道准平稳区（QSRs）的新方法。传统的非平稳性评估方法主要依赖于功率延迟分布（pdp），可能会产生不准确的QSR估计，特别是在轨道侧收发站附近，由于对称传播条件。通过引入多普勒延迟信息，该方法显著提高了QSR识别精度。该方法通过郑西高铁2.35 GHz的真实通道测量进行了验证，涵盖了高架桥和切割场景。结果表明，基于ddpp的定义不仅避免了错误的QSR估计，而且在给定阈值下产生的QSR值通常小于基于pdp的方法。此外，高架桥场景比路堑场景表现出更大的qsr。例如，在阈值为0.7时，在切割场景中，轨道旁接收器附近的QSR值为0.98 m（基于ddpp）和9.34 m（基于pdp），而在高架桥场景中，该值为2.03 m（基于ddpp）和31.61 m（基于pdp），突出了该方法捕获环境依赖性平稳特征的能力。

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引用次数: 0

FedDAK: Distribution-aware personalized federated learning with dynamic knowledge distillation FedDAK：基于动态知识蒸馏的分布感知个性化联邦学习

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

Physical Communication

Pub Date : 2026-03-01 Epub Date: 2026-01-24 DOI: 10.1016/j.phycom.2026.103015

Wenlong Lu , Ping Zhang , An Bao

With the rapid advancement of edge intelligence, conventional federated learning (FL) frameworks still struggle to achieve competitive performance under highly heterogeneous and class-imbalanced data distributions. To address these limitations, this paper presents FedDAK, a distribution-aware and adaptive knowledge distillation framework for personalized federated learning. FedDAK enhances both stability and personalization capability through three key designs: dynamic distillation weighting, adaptive rare-class enhancement, and distribution-aware global aggregation. Unlike existing distillation-based FL systems that rely on static or heuristic weighting, FedDAK introduces a KL-divergence–guided dynamic distillation coefficient, enabling each client to automatically regulate the strength of global knowledge constraints according to its divergence from the global data distribution. Furthermore, FedDAK integrates class-level scarcity modeling, assigning increased importance to underrepresented categories to alleviate bias under severe class imbalance. At the global level, FedDAK employs distribution-aware aggregation, reducing the negative influence of highly divergent clients and improving global stability and generalization. Extensive experiments on benchmark datasets demonstrate that FedDAK achieves significantly better personalized performance and global convergence than existing FL baselines under the standard federated learning setting, without requiring the sharing of raw data. The code is available at https://github.com/youmurong50-cmd/fedDAK.

随着边缘智能的快速发展，传统的联邦学习（FL）框架在高度异构和类不平衡的数据分布下仍然难以达到有竞争力的性能。为了解决这些限制，本文提出了FedDAK，一个用于个性化联邦学习的分布感知和自适应知识蒸馏框架。FedDAK通过三个关键设计增强了稳定性和个性化能力：动态蒸馏加权、自适应稀有类增强和分布感知全局聚合。与现有的依赖静态或启发式加权的基于蒸馏的FL系统不同，FedDAK引入了kl -发散引导的动态蒸馏系数，使每个客户端能够根据其与全局数据分布的偏离程度自动调节全局知识约束的强度。此外，FedDAK集成了类级稀缺性模型，增加了代表性不足的类别的重要性，以减轻严重的类不平衡下的偏见。在全球层面，FedDAK采用分布感知聚合，减少了高度分散客户的负面影响，提高了全球稳定性和泛化。在基准数据集上的大量实验表明，在标准联邦学习设置下，FedDAK在不需要共享原始数据的情况下，实现了比现有FL基线更好的个性化性能和全局收敛性。代码可在https://github.com/youmurong50-cmd/fedDAK上获得。

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引用次数: 0