Physical Communication最新文献_第5页

Adaptive coding and modulation in direct A2G communication link for inflight broadband connectivity 机载宽带直接A2G通信链路的自适应编码和调制

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

Physical Communication

Pub Date : 2025-12-25 DOI: 10.1016/j.phycom.2025.102968

Amjed Ali, Noor Muhammad Khan

The growing demand for high-speed and reliable inflight broadband connectivity (IFC) presents unique challenges for direct air-to-ground communication (DA2GC) systems, especially when they operate in the millimeter-wave (mmWave) band. While mmWave frequencies offer high capacity and spectral efficiency, they are highly susceptible to atmospheric impairments such as rain attenuation, gaseous absorption, and Doppler effects (caused by aircraft mobility).

This study proposes a signal-to-noise ratio (SNR)-driven adaptive coding and modulation (ACM) framework specifically designed for DA2GC links. The goal is to enhance spectral efficiency and link reliability under time-varying channel conditions. The proposed ACM system dynamically selects the modulation and coding scheme (MCS) based on real-time channel feedback, ensuring robust performance in both favorable and adverse weather conditions.

A comprehensive DA2GC channel model is developed based on ITU-R recommendations. The model accounts for rain attenuation, polarization effects, antenna tilt, and altitude-dependent gas absorption. To capture the dynamics of high-mobility aeronautical environments, simulations employ a Rician fading model, with the Doppler coherence time used to define ACM block duration. The results indicate that rainfall can cause variations of up to 100 dB in the link budget, highlighting the importance of adaptive transmission strategies. Lower-order quadrature amplitude modulation (QAM) schemes with stronger coding ensure link availability during rain fades, while higher-order QAM schemes maximize throughput under clear-sky conditions.

The proposed ACM scheme demonstrates promising real-time adaptability, indicating its potential to support high-speed and reliable inflight connectivity. Future work will focus on uplink adaptation, energy-efficiency trade-offs, and the practical (particularly hardware) feasibility of onboard ACM implementation.

对高速可靠的飞行宽带连接（IFC）日益增长的需求给直接空对地通信（DA2GC）系统带来了独特的挑战，特别是当它们在毫米波（mmWave）频段运行时。虽然毫米波频率提供高容量和频谱效率，但它们极易受到大气损伤的影响，如雨衰减、气体吸收和多普勒效应（由飞机机动性引起）。本研究提出了一个专为DA2GC链路设计的信噪比（SNR）驱动的自适应编码和调制（ACM）框架。目标是提高时变信道条件下的频谱效率和链路可靠性。提出的ACM系统基于实时信道反馈动态选择调制和编码方案（MCS），保证了在有利和不利天气条件下的鲁棒性能。根据ITU-R的建议，开发了一个全面的DA2GC信道模型。该模式考虑了降雨衰减、极化效应、天线倾斜和与高度有关的气体吸收。为了捕捉高机动性航空环境的动态，仿真采用了一个专家衰落模型，并使用多普勒相干时间来定义ACM块持续时间。结果表明，降雨可导致链路预算的变化高达100 dB，突出了自适应传输策略的重要性。具有较强编码的低阶正交调幅（QAM）方案可确保在降雨期间的链路可用性，而高阶QAM方案可在晴天条件下最大化吞吐量。提出的ACM方案显示出良好的实时适应性，表明其支持高速可靠的飞行连接的潜力。未来的工作将集中在上行链路适应、能源效率权衡以及板载ACM实现的实际（特别是硬件）可行性上。

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引用次数: 0

Resource scheduling for multi-UAVs-Enabled spectrum-Sharing semantic communications 多无人机支持频谱共享语义通信的资源调度

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

Physical Communication

Pub Date : 2025-12-25 DOI: 10.1016/j.phycom.2025.102975

Zhen Wang , Ning Wang

This paper investigates a multiple unmanned aerial vehicles (Multi-UAVs) enabled semantic communication framework operating in a spectrum-sharing system, where multiple UAVs act as secondary transmitters to serve ground secondary users (SUs) while coexisting with primary users (PUs). By employing orthogonal frequency-division multiplexing (OFDM), each UAV transmits over orthogonal subcarriers to eliminate inter-UAV interference and achieve flexible subcarrier allocation. A learning-based semantic communication model is integrated, in which the end-to-end task distortion is characterized as an exponential function of the semantic rate and the source complexity, thus bridging the semantic and physical layers. Considering the UAVs’ mobility, energy, and interference limitations, a joint optimization problem is formulated to minimize the weighted sum of expected semantic distortions by jointly optimizing UAV trajectories, subcarrier assignment, transmit power, and semantic encoding rates. To efficiently solve the formulated mixed-integer and nonconvex problem, we propose a novel semantic-aware alternating optimization framework, named AO-MUSC, which combines successive convex approximation (SCA) for trajectory and power optimization, a Hungarian-based subcarrier assignment, and a closed-form semantic water-filling procedure for rate adaptation. Extended simulation results demonstrate that the proposed scheme significantly reduces the task-level semantic distortion and enhances communication efficiency while maintaining the interference below the prescribed thresholds of the primary network.

本文研究了在频谱共享系统中运行的多无人机（multi - uav）语义通信框架，其中多无人机作为辅助发射机，为地面辅助用户（su）服务，同时与主用户（pu）共存。采用正交频分复用（OFDM）技术，每架无人机在正交子载波上传输，消除了无人机间的干扰，实现了灵活的子载波分配。集成了一种基于学习的语义通信模型，该模型将端到端任务失真描述为语义率和源复杂性的指数函数，从而架起了语义层和物理层的桥梁。考虑到无人机的机动性、能量和干扰限制，通过联合优化无人机轨迹、子载波分配、发射功率和语义编码速率，提出了最小化期望语义扭曲加权和的联合优化问题。为了有效地解决混合整数和非凸问题，我们提出了一种新的语义感知交替优化框架AO-MUSC，该框架将连续凸近似（SCA）用于轨迹和功率优化，基于匈牙利的子载波分配和封闭形式的语义填充过程用于速率适应相结合。扩展仿真结果表明，该方案显著降低了任务级语义失真，提高了通信效率，同时将干扰保持在主网络规定的阈值以下。

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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 : 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

DRL-Assisted joint power allocation and antenna positioning in movable-Antenna systems with finite alphabet inputs 有限字母输入的移动天线系统中drl辅助联合功率分配和天线定位

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

Physical Communication

Pub Date : 2025-12-22 DOI: 10.1016/j.phycom.2025.102965

Ayaz Ahmad

Recently, movable antennas (MAs) have emerged as a promising candidate for sixth-generation (6G) wireless systems. Unlike fixed arrays or reconfigurable intelligent surfaces, movable antennas provide an additional spatial degree of freedom that better mitigates blockage, exploits favorable fading, and balances multi-user links by allowing antenna elements to physically reposition within a bounded area. Moreover, real-world wireless networks operate with finite alphabet (FA)/discrete inputs pertaining to digital modulation schemes such as quadrature phase shift keying (QPSK) or quadrature amplitude modulation (QAM) (such as 8-QAM/16-QAM), etc. However, most studies in the literature assume Gaussian inputs whose mutual information (MI) performance deviates significantly from practical FA inputs. Thus, a better exploitation of the spatial flexibility of MAs and the need to consider FA inputs motivates the importance of a new optimization framework. In this work, we consider a downlink network scenario where a base station equipped with an MA serves multiple ground users. In view of the complex mathematical expression of the discrete-input MI, we adopt the cutoff rate (CR) as its tractable surrogate. We formulate a joint optimization framework for antenna positioning and transmit power allocation and devise a two-step solution combining a soft actor-critic (SAC) deep reinforcement learning (DRL) algorithm for antenna positioning with convex optimization for power allocation. Numerical results show that the proposed MA positioning and power allocation under the realistic discrete signaling scenarios yields significantly high achieved data rate compared to the fixed-antenna benchmark while providing an accurate representation of practical systems compared to the theoretical Gaussian input-based approaches.

最近，移动天线（MAs）已成为第六代（6G）无线系统的有希望的候选者。与固定阵列或可重新配置的智能表面不同，可移动天线提供了额外的空间自由度，可以更好地减轻阻塞，利用有利的衰落，并通过允许天线元件在限定区域内物理重新定位来平衡多用户链路。此外，现实世界的无线网络在有限字母(FA)/离散输入下运行，这些输入与数字调制方案有关，如正交相移键控（QPSK）或正交调幅（QAM）（如8-QAM/16-QAM）等。然而，文献中的大多数研究假设高斯输入，其互信息（MI）性能明显偏离实际FA输入。因此，更好地利用MAs的空间灵活性和考虑FA输入的需要激发了新的优化框架的重要性。在这项工作中，我们考虑了一个下行网络场景，其中配备了MA的基站为多个地面用户提供服务。考虑到离散输入MI的复杂数学表达式，我们采用截止率（CR）作为其可处理的替代物。我们制定了天线定位和发射功率分配的联合优化框架，并设计了一种两步解决方案，将天线定位的软行为者评价（SAC）深度强化学习（DRL）算法与功率分配的凸优化相结合。数值结果表明，在实际离散信号场景下，与固定天线基准相比，所提出的MA定位和功率分配方法获得了显着高的数据速率，同时与基于高斯输入的理论方法相比，提供了对实际系统的准确表示。

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引用次数: 0

UAV-AGV collaborative beamforming for efficient maritime-terrestrial two-way communications 高效海陆双向通信的无人机- agv协同波束形成

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

Physical Communication

Pub Date : 2025-12-22 DOI: 10.1016/j.phycom.2025.102974

Jiajun Zhang , Xinying Wang , Shengsheng Wang

The robust integration of maritime and terrestrial networks is a pivotal yet challenging goal for 6G systems, especially in emergency scenarios characterized by a lack of infrastructure and dynamic channel conditions. To overcome the limitations of traditional methods, this paper proposes a novel collaborative beamforming (CB)-based bidirectional communication system that leverages a cooperative cyber-physical network of unmanned aerial vehicles (UAVs) and automated guided vehicles (AGVs). Within this framework, we formulate a Maritime-Terrestrial Communication Multi-Objective Optimization Problem (MTCMOP) that simultaneously aims to maximize the shore-to-ship and ship-to-shore transmission rates while minimizing the total propulsion energy consumption of the UAVs and AGVs. Solving the NP-hard MTCMOP necessitates an efficient optimizer. Thus, we propose a novel Multi-Objective Evolutionary Algorithm based on Angle Guidance (MOEA-AG). Its core innovation is an Angle-guided Evolutionary (AE) operator that explicitly utilizes direction information in the objective space to navigate complex trade-offs. This is synergistically combined with adaptive mating selection and diversity-aware environmental selection mechanisms to achieve a superior balance between convergence and diversity. Extensive experiments demonstrate the superiority of our approach. On benchmark problems, MOEA-AG outperforms five state-of-the-art algorithms, respectively. When applied to the customized MTCMOP, MOEA-AG finds solutions that achieve higher transmission rates, outperforming all competitors across all three objectives. Furthermore, the proposed system exhibits strong robustness under practical perturbations. The results conclusively show that the proposed UAV-AGV collaborative framework, empowered by MOEA-AG, constitutes an efficient, reliable, and robust solution for future maritime-terrestrial emergency communications.

海上和陆地网络的强大集成是6G系统的关键但具有挑战性的目标，特别是在缺乏基础设施和动态信道条件的紧急情况下。为了克服传统方法的局限性，本文提出了一种基于协同波束形成（CB）的新型双向通信系统，该系统利用无人机（uav）和自动制导车辆（agv）的协同网络物理网络。在此框架内，我们制定了一个海陆通信多目标优化问题（MTCMOP），该问题同时旨在最大化岸对舰和舰对岸传输速率，同时最小化无人机和agv的总推进能耗。求解NP-hard MTCMOP需要一个高效的优化器。为此，我们提出了一种新的基于角度制导的多目标进化算法。它的核心创新是一个角度引导进化（AE）算子，它明确地利用目标空间中的方向信息来导航复杂的权衡。这与适应性交配选择和多样性意识环境选择机制协同结合，在趋同与多样性之间实现了良好的平衡。大量的实验证明了我们方法的优越性。在基准问题上，MOEA-AG分别优于五种最先进的算法。当应用于定制MTCMOP时，MOEA-AG找到了实现更高传输速率的解决方案，在这三个目标上都优于所有竞争对手。此外，该系统在实际扰动下具有较强的鲁棒性。结果最终表明，拟议的无人机- agv协作框架，由MOEA-AG授权，构成了未来海陆空应急通信的高效、可靠和强大的解决方案。

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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 : 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

Investigation of the effects of digital-to-analog converters on OFDM-based modulations with gaussian signal distribution 数模转换器对基于高斯信号分布的ofdm调制影响的研究

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

Physical Communication

Pub Date : 2025-12-21 DOI: 10.1016/j.phycom.2025.102962

Zsolt Kollár

This paper investigates the impact of Digital-to-Analog Converters (DACs) on the generation of multicarrier signals, with a focus on Orthogonal Frequency Division Multiplexing (OFDM). A mathematical framework is developed to model DAC behavior in this context, and its performance is analytically evaluated. First, a closed-form expression is derived for the optimal clipping threshold under uniform quantization, expressed as a function of DAC bit resolution. Then, an improved optimization strategy is introduced for allocating non-uniform quantization levels to maximize the Signal-to-Distortion and Quantization Ratio (SDQR) at the DAC output. An upper bound on SDQR is further established using the Lloyd-Max quantization method. The influence of these quantization schemes on overall system performance—including bit error rate and spectral characteristics—is also assessed. The results apply to multicarrier systems with Gaussian-distributed signals and can be extended to related communication and signal-processing scenarios.

本文研究了数模转换器（dac）对多载波信号产生的影响，重点研究了正交频分复用（OFDM）。在这种情况下，开发了一个数学框架来模拟DAC行为，并对其性能进行了分析评估。首先，导出了均匀量化下的最佳裁剪阈值的封闭表达式，表示为DAC位分辨率的函数。然后，介绍了一种改进的优化策略，用于分配非均匀量化电平，以最大化DAC输出的信失真比和量化比（SDQR）。利用Lloyd-Max量化方法进一步建立了SDQR的上界。评估了这些量化方案对系统整体性能的影响，包括误码率和频谱特性。研究结果适用于具有高斯分布信号的多载波系统，并可扩展到相关的通信和信号处理场景。

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 : 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

Efficient joint recognition of modulation and channel coding using a lightweight multi-type attention convolutional network 基于轻量级多类型注意卷积网络的调制和信道编码的高效联合识别

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

Physical Communication

Pub Date : 2025-12-18 DOI: 10.1016/j.phycom.2025.102970

Haoning Yang , Hongfei Tao , Chunjie Cao , Yang Sun , Jingcheng Shi , Jingzhang Sun

Blind recognition of modulation and channel coding is a key technique in non-cooperative communications, requiring the receiver to recognize signals without prior knowledge. Most existing approaches typically perform modulation recognition and channel coding recognition separately, leading to resource wastage and error accumulation, while research on joint recognition remains insufficient. To address this problem, a lightweight multitype attention convolutional network (LACNet) is proposed. The network integrates multiple attention modules and combines dilated convolutional residual blocks with the Efficient Channel Attention (ECA). In this way, LACNet extracts discriminative features from different perspectives to achieve joint recognition. The simulation results demonstrate that, compared to existing networks, LACNet achieves higher recognition accuracy on the evaluated dataset, while significantly reducing complexity and computational cost, making it suitable for practical deployment.

调制和信道编码的盲识别是非合作通信中的一项关键技术，它要求接收机在没有先验知识的情况下识别信号。现有方法大多分别进行调制识别和信道编码识别，造成资源浪费和误差积累，而对联合识别的研究不足。为了解决这一问题，提出了一种轻量级的多类型注意卷积网络（LACNet）。该网络集成了多个注意模块，并将扩展卷积残差块与有效通道注意（ECA）相结合。这样，LACNet从不同的角度提取判别特征，实现联合识别。仿真结果表明，与现有网络相比，LACNet在评估数据集上获得了更高的识别精度，同时显著降低了复杂度和计算成本，适合实际部署。

引用次数: 0

SMTrans: An efficient automatic modulation recognition network based on the scale-aware modulation transformer SMTrans：一种基于尺度感知调制变压器的高效自动调制识别网络

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

Physical Communication

Pub Date : 2025-12-18 DOI: 10.1016/j.phycom.2025.102966

Yang Huo, Chao Wang, Jiakai Liang, Keqiang Yue, Wenjun Li

In recent years, with the rapid advancement of deep learning technologies, automatic modulation recognition, as a crucial component of blind signal processing, has attracted extensive attention from the research community. However, most deep learning-based AMR models tend to focus excessively on recognition accuracy while neglecting computational efficiency, posing significant challenges for deployment on embedded and edge devices. In this paper, we propose an efficient automatic modulation recognition network based on a scale-aware modulation unit, which integrates the advantages of convolutional neural networks and Transformers. The proposed architecture effectively reduces both the number of parameters and computational complexity while maintaining high recognition accuracy. Experimental results demonstrate that the proposed model achieves recognition accuracies of 63.27 % and 65.17 % on the RadioML2016.10a and RadioML2016.10b datasets, respectively. Its performance is comparable to state-of-the-art models that require five times more parameters and fifteen times more FLOPs, while also surpassing existing lightweight AMR models in terms of recognition accuracy. Furthermore, to enhance the model’s performance, we conducted an in-depth investigation into the impact of various data augmentation sstrategies, leading to an additional 0.55 % improvement in recognition accuracy.

近年来，随着深度学习技术的飞速发展，自动调制识别作为盲信号处理的重要组成部分，受到了学术界的广泛关注。然而，大多数基于深度学习的AMR模型往往过于关注识别准确性，而忽略了计算效率，这对在嵌入式和边缘设备上的部署构成了重大挑战。本文结合卷积神经网络和变压器的优点，提出了一种基于尺度感知调制单元的高效自动调制识别网络。该结构在保持较高的识别精度的同时，有效地减少了参数的数量和计算复杂度。实验结果表明，该模型在RadioML2016.10a和RadioML2016.10b数据集上的识别准确率分别达到63.27%和65.17%。其性能可与需要5倍以上参数和15倍以上浮点数的最先进模型相媲美，同时在识别精度方面也超过了现有的轻量级AMR模型。此外，为了提高模型的性能，我们深入研究了各种数据增强策略的影响，导致识别精度提高0.55%。

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引用次数: 0