Physical Communication最新文献

{"title":"MA-PPO driven autonomous decision system for UAV swarms: Integrating semantic parsing and anti-jamming RL control","authors":"Yiming Xiang ,&nbsp;Han Yang","doi":"10.1016/j.phycom.2026.103016","DOIUrl":"10.1016/j.phycom.2026.103016","url":null,"abstract":"<div><div>The operational effectiveness of drone swarms in complex electromagnetic environments is fundamentally limited by autonomous decision-making capabilities, particularly under dynamic interference and stringent real-time constraints. This study develops an autonomous decision-making system centered on an enhanced Multi-Agent Proximal Policy Optimization (MA-PPO) algorithm, with a core focus on joint communication-control optimization. A hierarchical policy network architecture is designed to tightly couple global task planning with local anti-interference control. Crucially, a dynamic interference model is integrated to co-optimize communication power allocation and flight trajectory planning in real-time, thereby enhancing robustness against channel uncertainty and adversarial jamming. Experimental results under -80 dBm interference intensity demonstrate a 9.4% improvement in task completion rate over MADDPG, a communication interruption rate reduced to 7.1% (19.5% of traditional PID methods), and a 107% enhancement in energy efficiency (8.9 tasks/kWh). The primary contributions are threefold: (1) a hierarchical decision architecture that enables deep coupling between planning and interference-aware control; (2) a joint optimization framework that dynamically balances communication quality with motion constraints; (3) quantitative validation in a realistic electromagnetic environment, confirming the engineering feasibility of the proposed approach for reliable swarm operations. This work provides a scalable and robust solution for autonomous drone swarms, advancing the state-of-the-art in physical-layer aware cooperative control.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103016"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DQN-based optimization for enhancing the performance of RIS-NOMA system","authors":"Ying Lin,&nbsp;Haomin Li,&nbsp;Bowen Zheng,&nbsp;Xuefeng Jing,&nbsp;Xiangcheng Wang","doi":"10.1016/j.phycom.2025.102990","DOIUrl":"10.1016/j.phycom.2025.102990","url":null,"abstract":"<div><div>In recent years, with the continuous deepening of research in the field of communication, the utilization rate of spectrum resources and the performance improvement of communication systems in specific scenarios have become the focus of attention. In this context, the integration of non-orthogonal multiple access (NOMA) technology for multi-user spectrum resource sharing with the groundbreaking innovation of reconfigurable intelligent surfaces (RIS) represents a promising direction for in-depth exploration in the era of 6 G wireless communications.This study addresses the challenges posed by complex channel environments and introduces deep reinforcement learning into RIS-NOMA systems.By achieving real-time optimization in ultra-high-dimensional spaces, the aim is to determine novel and effective transmission strategies.Specifically, the Deep Q-Network (DQN) algorithm is employed to optimize high-dimensional decision-making in the dynamic environment of RIS-NOMA systems. By leveraging the adaptive optimization capability of DQN for dynamic channel reconstruction, this method is integrated into the RIS-NOMA system.Simulation results demonstrate that the proposed DQN-based RIS-NOMA system achieves significant improvements in key performance metrics such as achievable data rate, system throughput, and energy efficiency, substantially outperforming traditional schemes. The system throughput is increased by approximately 29 % compared to conventional methods, thereby validating the effectiveness and advancement of the proposed design. The synergistic mechanism between RIS phase regulation and NOMA power allocation provides both theoretical support and practical guidance for the future deployment of RIS-NOMA systems.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 102990"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive fluid antenna deployment for improved wireless reliability","authors":"Ahmed S. Alwakeel ,&nbsp;Mohamed H. Saad ,&nbsp;Mohamed S. Elbakry","doi":"10.1016/j.phycom.2026.103002","DOIUrl":"10.1016/j.phycom.2026.103002","url":null,"abstract":"<div><div>Fluid Antenna System (FAS) have emerged as a promising solution for improving wireless communication by allowing an antenna’s placement within a device to dynamically adjust to its surroundings. This flexibility improves signal quality, link stability, and spectrum efficiency without requiring the deployment of extra antennas. However, realizing the full potential of FAS necessitates determining the ideal antenna arrangement, which is a difficult, multidimensional challenge driven by user locations and signal propagation parameters. To address this issue, this research proposes using the Whale Optimization Algorithm (WHO) for efficient FAS tuning. WHO automatically searches the solution space for ideal antenna placements that improve network performance while reducing deployment complexity. Simulation results show that WHO outperforms traditional methods such as Gaussian approximation (GA) and Particle Swarm Optimization (PSO), achieving better connection with fewer antennas–only three vs four and five for GA and PSO, respectively. WHO improves convergence by 49.6% compared to GA and reduces inference time by 35% compared to Differential Evolution (DE), making it suitable for real-time, adaptive, and resource-efficient wireless networks.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103002"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Autoencoder-Pelican optimization for nonlinear impairment mitigation in long-haul optical fiber systems","authors":"Zahid Zaman ,&nbsp;Yousaf Khan ,&nbsp;Farman Ali ,&nbsp;Ammar Armghan ,&nbsp;Muhammad Kamran Shereen ,&nbsp;Sultan S. Aldkeelalah ,&nbsp;Mardeni Roslee","doi":"10.1016/j.phycom.2025.102972","DOIUrl":"10.1016/j.phycom.2025.102972","url":null,"abstract":"<div><div>Long-haul optical transmission (LHOT) systems are affected by nonlinear impairments (NIs), including self-phase modulation (SPM), cross-phase modulation (XPM), four-wave mixing (FWM), amplified spontaneous emission (ASE) noise, and Kerr nonlinearities, which limit achievable data rates and system reach. Conventional methods, such as digital back-propagation (DBP), optical phase conjugation (OPC), and DSP-assisted receivers, have demonstrated mitigation capabilities but suffer from high computational complexity, latency, and power consumption, making them impractical for large-scale networks. Machine learning (ML) approaches, including label propagation and transformer-based schemes, reduce some processing overhead yet do not perform dimensionality reduction for feature compression and lack a mechanism to jointly handle multiple nonlinear effects across LHOT. Furthermore, most reported works do not align with optical communication standards, such as ITU-T G.652.D or OS1/OS2 fibers, which limits their practical implementation in standardized infrastructures.</div><div>This work proposes an autoencoder-based pelican optimization algorithm (APOA) for NIs mitigation in LHOT systems. The autoencoder compresses high-dimensional signal distortions into a latent space that preserves nonlinear mappings, reducing computational load while maintaining representation accuracy. The POA performs parameter tuning to optimize signal recovery in the presence of nonlinear effects and noise. The transmission channel is modeled using the nonlinear Schrŏdinger equation (NLSE), with propagation distortions characterized by ITU-T G.652.D single-mode fiber (SMF) parameters: attenuation of 0.20 dB/km, chromatic dispersion of  ∼ 17 ps/nm/km at 1550 nm, effective area of 80 µm², and nonlinear coefficient <em>γ</em> ≈ 1.3 W<span><math><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></math></span>km<span><math><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></math></span>. Simulations are conducted using parameter settings aligned with OS1/OS2 fiber specifications (9 µm core diameter) and representative optical communication terminal (OCT) configurations, to reflect realistic long-haul transmission environments. Performance evaluation across multiple OSNR levels, fiber lengths, and modulation formats uses FEC thresholds and operating ranges that are consistent with IEEE 802.3 Ethernet and ITU-T G.709 OTN reference values, showing that APOA achieves BER values below the adopted FEC thresholds, increases spectral efficiency, and extends transmission reach.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 102972"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint optimization of resource and position for UAV secure two-Way relay systems using reinforcement learning","authors":"Bin Li ,&nbsp;Jie Ding ,&nbsp;Hui Li ,&nbsp;Jinlong Shi ,&nbsp;Xin Zuo","doi":"10.1016/j.phycom.2026.103004","DOIUrl":"10.1016/j.phycom.2026.103004","url":null,"abstract":"<div><div>With the rapid development of 5G and the forthcoming B5G/6G networks, unmanned aerial vehicles (UAVs) have been widely adopted in communication systems for their flexible deployment and integrated air-space-ground coverage capabilities. However, UAV communications are highly vulnerable to eavesdropping and jamming attacks, posing a severe threat to communication security. To address this problem, we construct a joint resource and position optimization framework based on the soft Actor-Critic (SAC) algorithm for a secure Two-Way Relay (TWR) system of UAV enabled with Non-Orthogonal Multiple Access (NOMA) technology. In this framework, NOMA technology is incorporated into the TWR relay transmission to achieve spectrum reuse and multi-user parallel communication. The UAV’s position and power allocation are modeled as a Markov Decision Process (MDP), which is intelligently optimized using deep reinforcement learning. We aim to maximize the overall secrecy rate of the system in a dynamic environment while minimizing constraint violations and eavesdropping risks. Simulation results demonstrate that, compared with A2C and PPO algorithms, the proposed SAC-based approach achieves superior convergence speed, stability, and anti-eavesdropping performance, providing technical references for NOMA-based secure UAV communications in B5G/6G networks.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103004"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shortwave signal modulation recognition method using adaptive time-Frequency threshold denoising and feature fusion","authors":"Chen Shen ,&nbsp;Tingting Lyu ,&nbsp;Yu Li ,&nbsp;Tianqi Lin ,&nbsp;Yulong Liu","doi":"10.1016/j.phycom.2026.102994","DOIUrl":"10.1016/j.phycom.2026.102994","url":null,"abstract":"<div><div>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%.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 102994"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive transmit antenna grouping scheme for signed quadrature spatial modulation","authors":"Yu Guan ,&nbsp;Lexi Xu ,&nbsp;Yuyang Peng ,&nbsp;Enjian Bai ,&nbsp;Xue Qin Jiang ,&nbsp;Han Hai","doi":"10.1016/j.phycom.2026.103049","DOIUrl":"10.1016/j.phycom.2026.103049","url":null,"abstract":"<div><div>Recently, signed quadrature spatial modulation (SQSM) has been proposed to improve the spectral efficiency (SE) for spatial modulation systems. SQSM extends the real and imaginary dimensions along with their inverse counterparts to transmit more bits. However, the number of transmit antennas of SQSM is limited to a power of 2. In this paper, a novel transmit antenna grouping scheme for SQSM (AG-SQSM) and an adaptive version for AG-SQSM (AAG-SQSM) are proposed. The AG-SQSM scheme makes full use of space resources, breaking through the limitation of SQSM. The SE of AG-SQSM is higher than that of traditional modulations when the number of transmit antennas increases linearly. Moreover, AAG-SQSM improves the bit error rate (BER) of the system by adopting an adaptive algorithm. The BER upper bounds of AG-SQSM and AAG-SQSM over Rayleigh fading channels are analyzed. Simulation results reveal that AG-SQSM and AAG-SQSM both have better BER performance than conventional SQSM when using the maximum likelihood (ML) detection algorithm.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103049"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy harvesting-assisted rate-splitting multiple access under external and internal overhearing: Secrecy analysis","authors":"Khuong Ho-Van","doi":"10.1016/j.phycom.2026.103056","DOIUrl":"10.1016/j.phycom.2026.103056","url":null,"abstract":"<div><div>The study investigates the security susceptibilities of rate-splitting multiple access (RSMA) systems powered by harvested energy, focusing on threats from both external and internal eavesdropping. External eavesdropping arises from unauthorized wiretappers unrelated to RSMA, whereas internal eavesdropping is an inherent issue of successive interference cancellation, wherein any RSMA receiver may intercept others’ signals. This study also evaluates the security capability of energy harvesting-assisted RSMA considering realistic energy harvesting conditions. The proposed evaluation framework is confirmed through Monte Carlo simulation. The findings reveal that, across various operational scenarios, RSMA provides superior security compared to orthogonal multiple access and non-orthogonal multiple access in energy harvesting wireless systems.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103056"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing communication performance with DFT-S-OFDM and multi-dimensional index modulation","authors":"Yongzhi Yu ,&nbsp;Hao Li ,&nbsp;Ge Li ,&nbsp;Limin Guo","doi":"10.1016/j.phycom.2026.103027","DOIUrl":"10.1016/j.phycom.2026.103027","url":null,"abstract":"<div><div>The Discrete Fourier Transform Spread Orthogonal Frequency Division Multiplexing (DFT-S-OFDM) system provides low Peak Average Power Ratio (PAPR) and low complexity. However, the current mode of subcarrier mapping in the system leads to a waste of subcarriers, thereby reducing the transmission rate. In this paper, a DFT-S-OFDM with Multi-Dimensional Index (DFT-S-OFDM-MDI) modulation system is proposed. The MDI incorporates subcarrier index (SI) and constellation diagram index (CDI), enabling the transmission of bit information in diverse index modes, thereby enhancing the system's transmission efficiency. We have configured six constellation diagram schemes for the system and incorporated spreading matrices to significantly boost the Bit Error Rate (BER) performance. The simulation results indicate that the proposed system is superior to traditional methods in terms of Spectral Efficiency (SE), Energy Efficiency (EE), and Bit Error Rate (BER).</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103027"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward dynamic radar signal sorting via gramian dimensionality elevation fusion CNN","authors":"Hongyuan Liu ,&nbsp;Xingjun Lai ,&nbsp;Tengfei Li ,&nbsp;Zichuan Yu ,&nbsp;Dayi Lin ,&nbsp;Deyan Kong ,&nbsp;Mingming Chen","doi":"10.1016/j.phycom.2026.103057","DOIUrl":"10.1016/j.phycom.2026.103057","url":null,"abstract":"<div><div>Radar signal sorting (RSS) is a crucial component within electronic reconnaissance systems, aiming to separate multiple radar pulses from an interlaced pulse stream. However, with the increasing complexity of the electromagnetic environment, radar signals are exhibiting diversified and dynamic characteristics. Traditional RSS techniques struggle to handle the nonlinear variations inherent in complex modulated signals and are susceptible to noise interference and spurious pulses. To address these challenges, deep learning technologies, notably Convolutional Neural Networks (CNNs), offer a promising solution for sorting signals with dynamic parameters. Nevertheless, single-modality CNNs lack the capacity to simultaneously capture intricate feature representations and process sequential data effectively. Motivated by this limitation, this paper proposes a novel network named Gramian-based CNN Fusion Network (GCF-Net) designed for efficient RSS in dynamic environments. Initially, comprehensive sorting datasets are established by modeling seven types of Pulse Repetition Interval (PRI) sequences. Subsequently, a dual-branch recognition and classification network is developed based on the Gramian Angular Difference Field (GADF) representation. Finally, experiments are conducted using the proposed GCF-Net. Experimental results demonstrate that GCF-Net achieves a high recognition rate of 92.4%. Moreover, it exhibits exceptional robustness in highly dynamic environments and significantly outperforms other deep-learning-based RSS methods.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103057"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}