Physical Communication最新文献_第3页

Effective degrees of freedom maximization for XL-RIS-assisted near-field communication via hybrid learning-driven optimization 通过混合学习驱动优化实现xml - ris辅助近场通信的有效自由度最大化

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

Physical Communication

Pub Date : 2026-03-01 Epub Date: 2026-01-20 DOI: 10.1016/j.phycom.2026.103012

Yaru Li , Wengen Gao , Yunfei Li , Wenlong Jiang

Extremely Large Reconfigurable Intelligent Surfaces (XL-RIS) have emerged as a transformative technology for controlling electromagnetic propagation in near-field wireless communication. However, optimizing their performance is challenging due to the complex spatial coupling and polarization effects in this regime-physical phenomena that are not fully captured by conventional models and result in intractable high-dimensional optimization problems. This paper proposes a hybrid learning-driven framework for maximizing the Effective Degrees of Freedom (EDoF) of XL-RIS-assisted systems. The proposed framework is grounded in an electromagnetically rigorous dyadic Green’s function-based channel model that accurately captures these critical near-field environment. To tackle the high-dimensional optimization problem efficiently, we introduce a novel method that combines a Multi-Layer Perceptron (MLP) as a fast performance surrogate with a Genetic Algorithm (GA) for global search. Comprehensive simulations demonstrate that the proposed framework achieves superior performance in achievable EDoF and channel capacity compared to existing benchmarks, effectively reveals the saturation behavior of spatial degrees of freedom and highlights the substantial gains enabled by polarization diversity. The results indicate that the integration of precise physical modeling with learning-based optimization offers an efficient and scalable approach for enhancing the performance of near-field XL-RIS.

极大可重构智能表面（XL-RIS）作为一种变革性的技术出现在近场无线通信中，用于控制电磁传播。然而，优化它们的性能是具有挑战性的，因为在这种情况下，复杂的空间耦合和极化效应——传统模型不能完全捕捉到的物理现象，并导致难以处理的高维优化问题。本文提出了一个混合学习驱动框架，用于最大化xml - ris辅助系统的有效自由度（EDoF）。所提出的框架是基于电磁严格的并矢格林函数的信道模型，该模型准确地捕获了这些关键的近场环境。为了有效地解决高维优化问题，我们引入了一种新的方法，将多层感知器（MLP）作为快速性能代理与遗传算法（GA）相结合进行全局搜索。综合仿真表明，与现有基准相比，该框架在可实现的EDoF和信道容量方面具有优越的性能，有效地揭示了空间自由度的饱和行为，并突出了极化分集所带来的实质性收益。结果表明，将精确物理建模与基于学习的优化相结合，为提高近场xml - ris的性能提供了一种高效、可扩展的方法。

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引用次数: 0

LSTM-based deep reinforcement learning for ISI mitigation in maritime LoRaWAN 基于lstm的海上LoRaWAN ISI缓解深度强化学习

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

Physical Communication

Pub Date : 2026-03-01 Epub Date: 2026-02-28 DOI: 10.1016/j.phycom.2026.103063

Martine Lyimo, Bonny Mgawe, Judith Leo, Mussa Dida, Kisangiri Michael

For reliable, long-range, low-power Maritime Internet of Things (MIoT) communication (e.g., vessel tracking, ocean monitoring, and offshore automation), LoRaWAN offers attractive coverage and energy efficiency. However, sea-surface reflections, wave motion, and platform mobility create time-varying multipath with large delay spread, which induces inter-symbol interference (ISI) and degrades packet delivery ratio (PDR) and energy performance. This paper proposes a Long Short-Term Memory (LSTM)-assisted Deep Reinforcement Learning (DRL) framework LSTM–DDPG Adaptive Modulation and Coding (LD-AMC) that proactively mitigates ISI by predicting short-term channel evolution and adapting the LoRaWAN physical-layer parameters. An LSTM predictor learns temporal correlations in observed link metrics (RSSI, SNR, PER, and RMS delay spread) and provides one-step-ahead forecasts, which are appended to the agent state. A Deep Deterministic Policy Gradient (DDPG) controller then selects the spreading factor (SF), coding rate (CR), bandwidth (BW), and transmit power (P_t) within LoRaWAN constraints to maximize a reward that favors reliable delivery and throughput while penalizing energy cost and ISI severity. MATLAB/Simulink simulations under coastal and offshore two-ray maritime channels show that LD-AMC reduces ISI-induced symbol errors by up to 58%, improving PDR by up to 47% and reducing energy per delivered packet by up to 32% compared with standard and enhanced ADR baselines.

对于可靠、远程、低功耗的海上物联网（MIoT）通信（例如，船舶跟踪、海洋监测和海上自动化），LoRaWAN提供了有吸引力的覆盖范围和能源效率。然而，海面反射、波浪运动和平台移动性会产生具有大延迟扩展的时变多径，从而诱发符号间干扰（ISI），降低分组传输比（PDR）和能量性能。本文提出了一个长短期记忆（LSTM）辅助深度强化学习（DRL）框架LSTM - ddpg自适应调制和编码（LD-AMC），该框架通过预测短期信道演变和适应LoRaWAN物理层参数来主动缓解ISI。LSTM预测器学习观察到的链路度量（RSSI、SNR、PER和RMS延迟扩展）中的时间相关性，并提供一步前的预测，这些预测被附加到代理状态。然后，深度确定性策略梯度（DDPG）控制器在LoRaWAN约束下选择扩展因子（SF）、编码速率（CR）、带宽（BW）和传输功率（Pt），以最大化有利于可靠传输和吞吐量的奖励，同时惩罚能源成本和ISI严重程度。MATLAB/Simulink在沿海和海上双射线海事通道下的模拟表明，与标准和增强的ADR基线相比，LD-AMC将isi引起的符号误差降低了58%，将PDR提高了47%，将每个交付包的能量降低了32%。

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引用次数: 0

The detection method for UE-Codebook Mismatch in grant-free SCMA IoT systems 无授权SCMA物联网系统中ue -码本不匹配检测方法

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

Physical Communication

Pub Date : 2026-03-01 Epub Date: 2026-01-28 DOI: 10.1016/j.phycom.2026.103026

Hua He, Yanxia Liang, Qiong Zhao

A detection algorithm for sparse code multiple access receivers in granted-free Internet of Things is proposed to address the detection problem under the condition of unknown UE-codebook matching at the base station. This problem entails a three-dimensional matching process involving UEs, codebooks, and codewords. The proposed algorithm treats each frequency resource block as an independent decoding layer and performs layer-wise decoding. To ensure both low computational complexity and high accuracy, the algorithm incorporates dynamic UE classification and a dual-threshold strategy. Simulation results demonstrate that, with the threshold being properly set, the computational complexity of the first decoding layer is reduced by up to 98.26%, and that of the second decoding layer by up to 96.8%. In addition, the computational complexity decreases progressively across decoding layers, achieving continuous layer-wise complexity reduction without requiring decoding on all available frequency resource blocks.

提出了一种免费物联网中稀疏码多址接收机检测算法，解决了基站ue码本匹配未知情况下的检测问题。这个问题需要涉及ue、码本和码字的三维匹配过程。该算法将每个频率资源块视为一个独立的解码层，进行逐层解码。为了保证较低的计算复杂度和较高的准确率，该算法结合了动态UE分类和双阈值策略。仿真结果表明，在适当设置阈值的情况下，第一解码层的计算复杂度可降低98.26%，第二解码层的计算复杂度可降低96.8%。此外，计算复杂度在解码层之间逐渐降低，在不需要对所有可用的频率资源块进行解码的情况下实现逐层复杂度的连续降低。

引用次数: 0

Investigation of MIMO channel model for ultra supersonic unmanned aerial vehicles 超声速无人机MIMO信道模型研究

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

Physical Communication

Pub Date : 2026-03-01 Epub Date: 2026-01-16 DOI: 10.1016/j.phycom.2026.103005

Lei Xu , Jie Zhou , Rui Liu , Manyan Zhao , Gen-Fu Shao

In this paper, a three-dimensional multi-input multi-output (MIMO) random channel model based on unmanned aerial vehicle (UAV) is proposed to address the communication challenges between supersonic UAVs and ground stations. During the flight of supersonic UAVs, a unique plasma sheath phenomenon is observed. The plasma sheath, located beneath the UAV, initially affects the signal emitted by the UAVs transmitter before it reaches the scatterers or ground receiver. Consequently, we develop a model that incorporates the signal passing through the plasma sheath and couples it with the air-to-ground channel model. The envelope and phase correlation coefficients of the channel, as well as the spatial-temporal frequency correlation function, are derived and described in detail. The statistical characteristics of the channel are examined by varying the incidence angles of the plasma layer and the UAV flight altitude. The results demonstrate that the plasma sheath significantly reduces the channel correlation coefficient within a short time frame, while a higher channel correlation coefficient is observed when the signal vertically impacts the plasma layer.

针对超声速无人机与地面站之间的通信问题，提出了一种基于无人机的三维多输入多输出（MIMO）随机信道模型。在超声速无人机飞行过程中，观察到一种独特的等离子鞘层现象。等离子护套，位于无人机下方，在到达散射器或地面接收器之前，最初影响由无人机发射器发射的信号。因此，我们开发了一个模型，该模型包含了通过等离子体护套的信号，并将其与空对地通道模型耦合。推导并详细描述了信道的包络和相位相关系数以及时空频率相关函数。通过改变等离子体层入射角和无人机飞行高度，考察了通道的统计特性。结果表明，等离子体鞘层在短时间内显著降低了通道相关系数，而当信号垂直撞击等离子体层时，通道相关系数更高。

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引用次数: 0

A hybrid GAN and attention-based sparse autoencoder framework for robust end-to-end wireless communication 一种用于端到端无线通信的混合GAN和基于注意力的稀疏自编码器框架

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

Physical Communication

Pub Date : 2026-03-01 Epub Date: 2026-01-22 DOI: 10.1016/j.phycom.2026.103008

Safalata S. Sindal, Y.N. Trivedi

This paper presents a hybrid deep learning framework that integrates a Generative Adversarial Network (GAN) with an Attention-based Sparse Autoencoder (GAN-AAE) for end-to-end wireless communication over Rayleigh fading channels with imperfect channel state information at the receiver (CSIR). Traditional autoencoder models lack the ability to learn underlying signal distributions or correct distortions caused by fading and noise. The proposed GAN-AAE addresses these limitations by using a generator as a learnable channel surrogate to refine encoded signals and an attention mechanism to dynamically prioritize relevant features for improved decoding. The imperfection in the CSI is quantified by a correlation coefficient ρ, where 0 ≤ ρ ≤ 1. Perfect channel knowledge is denoted by ρ = 1, and decreasing values of ρ correspond to increasingly inaccurate CSIR. The model is jointly trained using adversarial and reconstruction losses to enhance its adaptability. Simulation results show that the GAN-AAE framework significantly outperforms conventional Maximum Likelihood Detection and baseline deep and convolutional neural network-based models in terms of bit error rate (BER). The model is evaluated over M-ary phase shift keying (M-PSK) and M-ary Quadrature Amplitude Modulation (M-QAM) with Rayleigh fading channel. At ρ = 0.9 and a signal-to-noise ratio (SNR) of 10 dB, the conventional baseline model achieves a BER of 0.072, whereas the proposed GAN-AAE achieves a lower BER of 0.02404 for Binary phase shift keying (BPSK). A complexity analysis indicates that although the GAN-AAE model introduces some additional computational overhead, the performance gains in reconstruction justify the trade-off. Overall, the GAN-AAE offers a resilient and adaptive solution for end-to-end communication under realistic wireless impairments.

本文提出了一种混合深度学习框架，该框架将生成对抗网络（GAN）与基于注意力的稀疏自编码器（GAN- aae）集成在瑞利衰落信道上，用于端到端无线通信，接收器（CSIR）的信道状态信息不完善。传统的自编码器模型缺乏学习潜在信号分布或纠正由衰落和噪声引起的失真的能力。提出的GAN-AAE通过使用生成器作为可学习的通道代理来改进编码信号，并使用注意机制来动态优先考虑相关特征以改进解码，从而解决了这些限制。CSI的不完全性通过相关系数ρ来量化，其中0 ≤ ρ ≤ 1。完美通道知识用ρ = 1表示，ρ值越小，CSIR越不准确。利用对抗损失和重建损失对模型进行联合训练，增强模型的适应性。仿真结果表明，GAN-AAE框架在误码率（BER）方面明显优于传统的最大似然检测和基于基线深度和卷积神经网络的模型。对该模型进行了基于瑞利衰落信道的M-ary相移键控（M-PSK）和M-ary正交调幅（M-QAM）的评估。在ρ = 0.9，信噪比（SNR）为10 dB时，传统基线模型的误码率为0.072，而GAN-AAE的二进制相移键控（BPSK）误码率较低，为0.02404。复杂性分析表明，尽管GAN-AAE模型引入了一些额外的计算开销，但重构中的性能增益证明了这种权衡是合理的。总体而言，GAN-AAE为实际无线损伤下的端到端通信提供了弹性和自适应解决方案。

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引用次数: 0

Enhanced AI–simulink hybrid framework for low-latency interference mitigation and performance optimization in 5 G RF receivers 增强的AI-simulink混合框架，用于5g射频接收器的低延迟干扰缓解和性能优化

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

Physical Communication

Pub Date : 2026-03-01 Epub Date: 2026-01-13 DOI: 10.1016/j.phycom.2026.102995

Shatha Kareem Abbas, Osman Nuri Uçan, AlSeddiq Oday

'The rapid expansion of 5 G wireless communication systems has accelerated the need for intelligent receiver architectures capable of adaptively mitigating interference while preserving signal integrity. This study presents an enhanced hybrid AI–Simulink framework that integrates deep learning-based estimation with a parametric Simulink RF signal chain to detect, predict, and suppress jamming and noise-induced distortion in 5 G RF receivers. A processed dataset of approximately 250,000 labeled signal samples was generated from Simulink simulations and used to train and validate the proposed model. The framework demonstrates substantial improvements in signal-to-noise ratio (SNR) and root mean square error (RMSE) when compared with traditional filtering and baseline machine learning approaches. Experimental results show an SNR enhancement of over +14 dB and consistently low error metrics across multiple interference power levels and frequency configurations. The proposed architecture maintains a compact computational footprint (≈8.4 MB) and supports low-latency inference suitable for integration into hardware-accelerated or embedded execution environments. These outcomes confirm the potential of the proposed hybrid approach as a precise and efficient solution for AI-assisted interference mitigation in 5 G receivers, while also outlining future directions toward over-the-air validation and FPGA-based deployment.

“5g无线通信系统的快速扩展加速了对智能接收器架构的需求，该架构能够自适应地减轻干扰，同时保持信号完整性。本研究提出了一个增强型混合AI-Simulink框架，该框架将基于深度学习的估计与参数化Simulink射频信号链集成在一起，以检测、预测和抑制5g射频接收器中的干扰和噪声引起的失真。从Simulink模拟中生成了大约25万个标记信号样本的处理数据集，并用于训练和验证所提出的模型。与传统的滤波和基线机器学习方法相比，该框架在信噪比（SNR）和均方根误差（RMSE）方面有了实质性的改进。实验结果表明，在多种干扰功率水平和频率配置下，信噪比增强超过+14 dB，误差指标始终较低。所提出的体系结构保持紧凑的计算占用（≈8.4 MB），并支持适合集成到硬件加速或嵌入式执行环境中的低延迟推理。这些结果证实了所提出的混合方法作为5g接收器中人工智能辅助干扰缓解的精确有效解决方案的潜力，同时也概述了空中验证和基于fpga的部署的未来方向。

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引用次数: 0

Measurement-based analysis of RIS-assisted MIMO channel characteristics and capacity at 6 GHz in an outdoor scenario 基于测量的室外场景下6 GHz下ris辅助MIMO信道特性和容量分析

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

Physical Communication

Pub Date : 2026-03-01 Epub Date: 2026-01-15 DOI: 10.1016/j.phycom.2026.103007

Shiyu Zhang , Yuxiang Zhang , Jianhua Zhang , Huiwen Gong , Yutong Sun , Jialin Wang , Tao Jiang , Zheng Hu

Reconfigurable intelligent surfaces (RIS) are a promising technology for future wireless networks. However, the channel characteristics of RIS-assisted multiple-input multiple-output (MIMO) systems remain insufficiently studied, especially in the 6 GHz band. To address this, an outdoor street-corner measurement campaign was conducted using a 32  ×  56 MIMO array and an RIS operating at 6 GHz with 200 MHz bandwidth. The measured power-angle-delay profiles (PADPs) show that RIS reinforcement strengthens reflected paths and increases received power, while reduced root mean square (RMS) delay and angular spreads indicate a mitigation of temporal and angular dispersion. Channel capacity and eigenvalue analyses further reveal that although RIS improves capacity, it slightly limits spatial multiplexing. These findings suggest that the primary advantage of RIS-assisted MIMO lies in enhancing power and link robustness under non-line-of-sight (NLoS) conditions, rather than universally improving multiplexing capability. This emphasizes the need for environment-aware RIS deployment strategies that balance power gain and spatial richness in future wireless systems.

可重构智能表面（RIS）是未来无线网络的一项很有前途的技术。然而，ris辅助多输入多输出（MIMO）系统的信道特性研究仍然不够充分，特别是在6 GHz频段。为了解决这个问题，使用32 × 56 MIMO阵列和6 GHz、200 MHz带宽的RIS进行了室外街角测量活动。测量的功率-角-延迟曲线（PADPs）表明，RIS增强增强了反射路径并增加了接收功率，而减少的均方根（RMS）延迟和角扩散表明时间和角色散得到缓解。信道容量和特征值分析进一步表明，虽然RIS提高了容量，但它略微限制了空间复用。这些发现表明，ris辅助MIMO的主要优势在于增强非视距（NLoS）条件下的功率和链路鲁棒性，而不是普遍提高多路复用能力。这强调了在未来的无线系统中，需要有环境意识的RIS部署策略来平衡功率增益和空间丰富度。

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引用次数: 0

Beam search assisted DRX mechanism in 5G networks: A machine learning and Semi-Markov modeling integration 5G网络中波束搜索辅助DRX机制：机器学习和半马尔可夫建模集成

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

Physical Communication

Pub Date : 2026-03-01 Epub Date: 2026-02-10 DOI: 10.1016/j.phycom.2026.103043

Anupam Gautam, Mahima Kumar

The integration of 5G New Radio into unlicensed spectrum (NR-U) offers potential to expand capacity and enhance user experience. However, the shared environment is highly dynamic due to coexistence with technologies such as Wi-Fi, making conventional static Discontinuous Reception (DRX) mechanisms insufficient. These limitations lead to unnecessary wake-ups, increased delay, and reduced energy efficiency for User Equipment (UE). To address this challenge, this work proposes a beam search assisted adaptive DRX framework that enables reliable directional access under unlicensed operation. The framework integrates Machine Learning (ML) based channel status prediction to determine whether the channel is available or congested by analyzing Wi-Fi traffic patterns including Inter-Frame Space statistics and collision ratios. The DRX mechanism is further modeled using a Semi-Markov model to capture realistic state timing dynamics and non-exponential holding times. Multiple ML classifiers are evaluated on a dataset of 20,000 samples. Simulation results show that the LSTM model achieves 96.8% prediction accuracy and improves Power Saving Factor from 83.2% to 87.8%, throughput from 30.6 Mbps to 60.4 Mbps, and reduces delay from 125.3 ms to 50.9 ms compared with non-predictive DRX operation. These results demonstrate that prediction-driven DRX mechanism significantly improves energy efficiency and latency performance in NR-U deployments.

5G新无线电与未授权频谱（NR-U）的集成提供了扩展容量和增强用户体验的潜力。然而，由于与Wi-Fi等技术共存，共享环境是高度动态的，使得传统的静态不连续接收（DRX）机制不足。这些限制导致不必要的唤醒，增加延迟，并降低用户设备（UE）的能源效率。为了应对这一挑战，本研究提出了一种波束搜索辅助的自适应DRX框架，该框架可在未经许可的操作下实现可靠的定向访问。该框架集成了基于机器学习（ML）的信道状态预测，通过分析包括帧间空间统计和碰撞比率在内的Wi-Fi流量模式，确定信道是可用还是拥塞。DRX机制使用半马尔可夫模型进一步建模，以捕获现实状态定时动态和非指数保持时间。在20,000个样本的数据集上评估多个ML分类器。仿真结果表明，与非预测DRX操作相比，LSTM模型预测准确率达到96.8%，节能系数从83.2%提高到87.8%，吞吐量从30.6 Mbps提高到60.4 Mbps，延迟从125.3 ms降低到50.9 ms。这些结果表明，预测驱动的DRX机制显著提高了NR-U部署中的能源效率和延迟性能。

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引用次数: 0

Time-window-constrained computation offloading via cost-efficient multi-UAV scheduling in space-air-ground integrated networks 基于成本高效的空-空-地综合网络多无人机调度的时间窗约束计算卸载

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

Physical Communication

Pub Date : 2026-03-01 Epub Date: 2026-02-10 DOI: 10.1016/j.phycom.2026.103042

Zhe Han , Guoqiang Zheng , Chuanfeng Li , Baofeng Ji

In air-ground collaborative sensing scenarios such as periodic environmental monitoring, emergency rescue, and military reconnaissance in remote areas, computation tasks are often subject to strict time window constraints, urgently necessitating efficient computing mechanisms to guarantee their timeliness. We investigate the joint optimization problem of computation offloading and unmanned aerial vehicle (UAV) scheduling under time-window constraints within a collaborative computing architecture involving UAVs and high-altitude platform (HAP). To reduce the number of dispatched UAVs and their energy consumption, a mixed-integer nonlinear programming problem is formulated aiming at minimizing the execution cost of computation tasks. Addressing the core challenge posed by the non-convex strong coupling between discrete and continuous variables, we propose a novel bi-level collaborative optimization strategy (BCOS). The outer layer employs the covariance matrix adaptation evolution strategy (CMA-ES) to efficiently search for optimal task offloading ratios within the continuous domain, while the inner layer, given the offloading strategy, precisely solves the discrete UAV route planning subproblem based on the branch-and-price algorithm. Through the iterative interaction between the inner and outer layer algorithms, the joint optimization of discrete and continuous variables is achieved upon convergence. Simulation results demonstrate that BCOS can effectively coordinate the optimization of computational resources and UAVs scheduling strategies, significantly reducing the number of dispatched UAVs and energy consumption, thereby validating its effectiveness and superiority in complex task execution scenarios.

在周期性环境监测、应急救援、偏远地区军事侦察等地空协同感知场景中，计算任务往往受到严格的时间窗约束，迫切需要高效的计算机制来保证其时效性。在无人机与高空平台协同计算架构下，研究了时间窗约束下的计算卸载与无人机调度的联合优化问题。为了减少无人机的调度数量和能耗，提出了以最小化计算任务执行成本为目标的混合整数非线性规划问题。针对离散变量和连续变量之间的非凸强耦合所带来的核心挑战，提出了一种新的双层协同优化策略（BCOS）。外层采用协方差矩阵自适应进化策略（CMA-ES）在连续域内高效搜索最优任务卸载比例，内层在给定卸载策略的情况下，基于分支价格算法精确求解离散型无人机航路规划子问题。通过内外两层算法的迭代交互，在收敛时实现离散变量和连续变量的联合优化。仿真结果表明，BCOS能够有效地协调优化计算资源和无人机调度策略，显著减少被调度无人机数量和能耗，从而验证了其在复杂任务执行场景下的有效性和优越性。

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引用次数: 0

Signal classification based on multi-Scale time-Frequency transformer 基于多尺度时频变压器的信号分类

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

Physical Communication

Pub Date : 2026-03-01 Epub Date: 2026-02-06 DOI: 10.1016/j.phycom.2026.103022

Jiahuang Yang, Yuhao Wang, Meng Yang, Hua Meng

Recognizing communication signals under non-cooperative remains challenging due to long waveform durations, noise corruption, and complex time-frequency structures. Existing convolutional neural network-, recurrent neural network-, and Transformer-based methods often rely on a single modeling paradigm and struggle to jointly capture local waveform patterns, long-range temporal dependencies, and complementary spectral information. To address these limitations, we propose MTF-Former, a Multi-scale time-frequency Transformer tailored for one-dimensional radio-frequency (RF) signals. MTF-Former combines signal-oriented data augmentation, a lightweight Time-Frequency Enhancement (TFE) block that injects frequency-aware modulation, and a hierarchical windowed Transformer encoder for efficient multi-scale temporal modeling. This unified design effectively integrates temporal and spectral cues while reducing computational cost compared with standard global self-attention Transformers for long sequences. Experiments on specific emitter identification (SEI) and automatic modulation recognition (AMR) benchmarks demonstrate that MTF-Former consistently outperforms many methods, achieving more notable performance gains at lower signal to noise ratio, with accuracy improvements of 1.30% on SEI (5 dB) and 2.42% on AMR (0 dB), and ablation studies further validate the contribution of each component.

由于波形持续时间长、噪声破坏和复杂的时频结构，非合作通信信号的识别仍然具有挑战性。现有的卷积神经网络、循环神经网络和基于变压器的方法通常依赖于单一的建模范式，难以联合捕获局部波形模式、长期时间依赖性和互补的频谱信息。为了解决这些限制，我们提出了MTF-Former，一种针对一维射频（RF）信号量身定制的多尺度时频变压器。MTF-Former结合了面向信号的数据增强，轻量级时频增强（TFE）块，注入频率感知调制，以及用于高效多尺度时间建模的分层加窗变压器编码器。与长序列的标准全局自关注变压器相比，这种统一的设计有效地集成了时间和光谱线索，同时降低了计算成本。在特定发射器识别（SEI）和自动调制识别（AMR）基准上的实验表明，MTF-Former始终优于许多方法，在较低信噪比下取得了更显著的性能提升，SEI精度提高了1.30%(5 dB)， AMR精度提高了2.42%(0 dB)，烧烧研究进一步验证了每个组件的贡献。

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引用次数: 0