Reconfigurable intelligent surfaces (RIS) have emerged as a promising technology for 6G wireless systems. This paper investigates a space-time-coding metasurface (STCM)-based frequency-mixing RIS (SFMx-RIS) architecture for high-precision angular sensing. We first establish the fundamental operating principles of SFMx-RIS, theoretically deriving the amplitude-frequency and phase-frequency responses, and further revealing its capability for precise delay-phase conversion in the reflective pattern. Building on this foundation, we construct a frequency-decoupled communication system enabled by SFMx-RIS and examine the feasibility of such architecture for angular sensing applications. Leveraging the effective delay-phase conversion capability of SFMx-RIS, we propose a weighted iterative algorithm that utilizes spatial information from the propagation channel to achieve accurate angular sensing. Numerical results reveal that SFMx-RIS can achieve extremely high-precision angular estimation accuracy in line-of-sight environments, highlighting the strong potential of SFMx-RIS for angular sensing applications.
{"title":"Frequency-mixing RIS-induced channel decoupling and delay-Phase conversion for angular sensing","authors":"Xianglin Shi, Jiangtian Gu, Fengkai Chen, Jide Yuan","doi":"10.1016/j.phycom.2025.102988","DOIUrl":"10.1016/j.phycom.2025.102988","url":null,"abstract":"<div><div>Reconfigurable intelligent surfaces (RIS) have emerged as a promising technology for 6G wireless systems. This paper investigates a space-time-coding metasurface (STCM)-based frequency-mixing RIS (SFMx-RIS) architecture for high-precision angular sensing. We first establish the fundamental operating principles of SFMx-RIS, theoretically deriving the amplitude-frequency and phase-frequency responses, and further revealing its capability for precise delay-phase conversion in the reflective pattern. Building on this foundation, we construct a frequency-decoupled communication system enabled by SFMx-RIS and examine the feasibility of such architecture for angular sensing applications. Leveraging the effective delay-phase conversion capability of SFMx-RIS, we propose a weighted iterative algorithm that utilizes spatial information from the propagation channel to achieve accurate angular sensing. Numerical results reveal that SFMx-RIS can achieve extremely high-precision angular estimation accuracy in line-of-sight environments, highlighting the strong potential of SFMx-RIS for angular sensing applications.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"74 ","pages":"Article 102988"},"PeriodicalIF":2.2,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938679","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}
Pub Date : 2025-12-30DOI: 10.1016/j.phycom.2025.102990
Ying Lin, Haomin Li, Bowen Zheng, Xuefeng Jing, Xiangcheng Wang
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.
{"title":"DQN-based optimization for enhancing the performance of RIS-NOMA system","authors":"Ying Lin, Haomin Li, Bowen Zheng, Xuefeng Jing, 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":"2025-12-30","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}
Pub Date : 2025-12-29DOI: 10.1016/j.phycom.2025.102969
Maadoud Djihane, Hamza Abdelkrim, Chabane Dhiya Eddine
This paper addresses the challenge of unmanned aerial vehicle (UAV) connectivity in dense Aerial Highway (AH) environments, where conventional massive MIMO deployments suffer from severe inter-user channel correlation due to line-of-sight (LoS)-dominant propagation. To overcome these limitations, we propose a joint UAV association and Synchronization Signal Block (SSB) beam planning framework for multi-tier 5G networks integrating terrestrial base stations (TBSs) and HAPS-mounted aerial base stations (ABSs). A tier-aware association metric is designed to capture spatial multiplexing constraints, interference exposure, and large-scale access gain, allowing UAVs to dynamically associate with terrestrial or aerial sectors based on network conditions. To solve the resulting combinatorial optimization problem, an Enhanced Teaching-Learning-Based Optimization (ETLBO) algorithm is developed, which jointly optimizes UAV association, beam activation, and per-beam power allocation without requiring algorithm-specific parameters. Extensive simulations confirm that the proposed framework significantly improves the signal-to-interference-plus-noise ratio (SINR) and data rate performance of UAVs, particularly in the 5%-tile regime, while ensuring reliable coverage for terrestrial users and facilitating seamless UAV-ground coordination in practical deployments.
{"title":"UAV association and beam planning in NTN-assisted 5G networks: A TLBO framework","authors":"Maadoud Djihane, Hamza Abdelkrim, Chabane Dhiya Eddine","doi":"10.1016/j.phycom.2025.102969","DOIUrl":"10.1016/j.phycom.2025.102969","url":null,"abstract":"<div><div>This paper addresses the challenge of unmanned aerial vehicle (UAV) connectivity in dense Aerial Highway (AH) environments, where conventional massive MIMO deployments suffer from severe inter-user channel correlation due to line-of-sight (LoS)-dominant propagation. To overcome these limitations, we propose a joint UAV association and Synchronization Signal Block (SSB) beam planning framework for multi-tier 5G networks integrating terrestrial base stations (TBSs) and HAPS-mounted aerial base stations (ABSs). A tier-aware association metric is designed to capture spatial multiplexing constraints, interference exposure, and large-scale access gain, allowing UAVs to dynamically associate with terrestrial or aerial sectors based on network conditions. To solve the resulting combinatorial optimization problem, an Enhanced Teaching-Learning-Based Optimization (ETLBO) algorithm is developed, which jointly optimizes UAV association, beam activation, and per-beam power allocation without requiring algorithm-specific parameters. Extensive simulations confirm that the proposed framework significantly improves the signal-to-interference-plus-noise ratio (SINR) and data rate performance of UAVs, particularly in the 5%-tile regime, while ensuring reliable coverage for terrestrial users and facilitating seamless UAV-ground coordination in practical deployments.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"74 ","pages":"Article 102969"},"PeriodicalIF":2.2,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938673","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}
Pub Date : 2025-12-29DOI: 10.1016/j.phycom.2025.102978
Yi Shen , Yuxin Xin , Ping Tan
We propose a secure transmission system in the presence of an illegal user on the ground. Due to the shielding of obstacles, the ground source is unable to directly transmit confidential information to the ground destination. Therefore, the ground source relays the confidential information to the ground destination through a jointly deployed relay system comprising intelligent reflecting surface and unmanned aerial vehicle (IRS-UAV). The illegal user either monitors the legitimate communication link or actively eavesdrops on legitimate information. Consequently, we consider that the destination employs an active jamming strategy to disrupt the eavesdropper’s reception, thereby enhancing the security and covert performance of the legitimate communication. Based on this scenario, we derive approximate expressions for the detection error probability, covert rate, transmission outage probability, and secrecy outage probability, and analyze the corresponding secure and covert performance. Simulations verify the correctness of the derived formulas and determine the optimal parameters for the destination and illegal user. Analysis demonstrates that introducing active interference by the legitimate destination can prevent the warden from detecting the covert communication with absolute certainty. Furthermore, the deployment of an active IRS is shown to significantly enhance the overall security performance of the system. Conversely, active interference by the eavesdropper increases the secrecy outage probability, thereby degrading secure transmission performance.
{"title":"Performance analysis of IRS-UAV-assisted active jamming receiver against active eavesdropper","authors":"Yi Shen , Yuxin Xin , Ping Tan","doi":"10.1016/j.phycom.2025.102978","DOIUrl":"10.1016/j.phycom.2025.102978","url":null,"abstract":"<div><div>We propose a secure transmission system in the presence of an illegal user on the ground. Due to the shielding of obstacles, the ground source is unable to directly transmit confidential information to the ground destination. Therefore, the ground source relays the confidential information to the ground destination through a jointly deployed relay system comprising intelligent reflecting surface and unmanned aerial vehicle (IRS-UAV). The illegal user either monitors the legitimate communication link or actively eavesdrops on legitimate information. Consequently, we consider that the destination employs an active jamming strategy to disrupt the eavesdropper’s reception, thereby enhancing the security and covert performance of the legitimate communication. Based on this scenario, we derive approximate expressions for the detection error probability, covert rate, transmission outage probability, and secrecy outage probability, and analyze the corresponding secure and covert performance. Simulations verify the correctness of the derived formulas and determine the optimal parameters for the destination and illegal user. Analysis demonstrates that introducing active interference by the legitimate destination can prevent the warden from detecting the covert communication with absolute certainty. Furthermore, the deployment of an active IRS is shown to significantly enhance the overall security performance of the system. Conversely, active interference by the eavesdropper increases the secrecy outage probability, thereby degrading secure transmission performance.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"74 ","pages":"Article 102978"},"PeriodicalIF":2.2,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938680","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}
Pub Date : 2025-12-29DOI: 10.1016/j.phycom.2025.102985
Yijia Chen, Jun Hu, Xuan Liu
Visible Light Positioning (VLP) has emerged as a compelling technology for indoor Industrial Internet of Things (IIoT) due to its electromagnetic interference-free nature and high-precision potential, particularly in centralized uplink architectures. However, existing systems face critical challenges, including complex signal aggregation from multiple mobile terminals (MTs), computational scalability issues, and the absence of complete 6-DoF pose estimation. To address these limitations, this paper proposes a novel framework named HECC-VLP (Hierarchical Enhanced CS-GAN and CNN framework), which integrates compressed sensing and deep learning for robust multi-target localization. The novelty of HECC-VLP lies in its task decoupling architecture that systematically decomposes localization into global sparse recovery and local pose regression. Specifically, we introduce an enhanced CS-GAN with lightweight modules (CAM, SFEM, ASRM) for sparse indication, bridging the two stages via a unique soft-mask channel separation mechanism to preserve multi-modal features. Comprehensive experiments in a 5 × 5 × 3 m3 indoor environment with 16 access points demonstrate that HECC-VLP achieves position errors of 4.51 cm (MAE) / 6.02 cm (RMSE) and attitude errors of 3.76∘ / 4.71∘. Notably, compared to traditional compressed sensing baselines (OMP/BP), the proposed method achieves a 70–76% reduction in localization errors while delivering a 4.3–4.8 × computational acceleration. Overall, the framework supports dynamic target scaling (2–10 MTs) with real-time inference (87.3 ms), demonstrating its effectiveness for precision-critical and scalable industrial applications.
可见光定位（VLP）由于其无电磁干扰的特性和高精度的潜力，特别是在集中式上行链路架构中，已成为室内工业物联网（IIoT）的一项引人注目的技术。然而，现有系统面临着严峻的挑战，包括来自多个移动终端（mt）的复杂信号聚合、计算可扩展性问题以及缺乏完整的6自由度姿态估计。为了解决这些限制，本文提出了一种名为HECC-VLP（分层增强CS-GAN和CNN框架）的新框架，该框架集成了压缩感知和深度学习，用于鲁棒多目标定位。HECC-VLP的新颖之处在于其任务解耦架构，将定位系统地分解为全局稀疏恢复和局部姿态回归。具体来说，我们引入了一种带有轻量级模块（CAM， SFEM， ASRM）的增强型CS-GAN，用于稀疏指示，通过独特的软掩模通道分离机制连接两个阶段，以保持多模态特征。在5 × 5 × 3 m3、16个接入点的室内环境中进行的综合实验表明，HECC-VLP的位置误差为4.51 cm (MAE) / 6.02 cm (RMSE)，姿态误差为3.76°/ 4.71°。值得注意的是，与传统的压缩感知基线（OMP/BP）相比，该方法在提供4.3-4.8 × 计算加速的同时，将定位误差降低了70-76%。总体而言，该框架支持动态目标缩放（2-10 mt）和实时推理（87.3 ms），证明了其在精度关键和可扩展的工业应用中的有效性。
{"title":"HECC-VLP: A hierarchical enhanced CS-GAN and CNN framework for multi-target 6-DoF localization in centralized uplink visible light positioning systems","authors":"Yijia Chen, Jun Hu, Xuan Liu","doi":"10.1016/j.phycom.2025.102985","DOIUrl":"10.1016/j.phycom.2025.102985","url":null,"abstract":"<div><div>Visible Light Positioning (VLP) has emerged as a compelling technology for indoor Industrial Internet of Things (IIoT) due to its electromagnetic interference-free nature and high-precision potential, particularly in centralized uplink architectures. However, existing systems face critical challenges, including complex signal aggregation from multiple mobile terminals (MTs), computational scalability issues, and the absence of complete 6-DoF pose estimation. To address these limitations, this paper proposes a novel framework named HECC-VLP (Hierarchical Enhanced CS-GAN and CNN framework), which integrates compressed sensing and deep learning for robust multi-target localization. The novelty of HECC-VLP lies in its task decoupling architecture that systematically decomposes localization into global sparse recovery and local pose regression. Specifically, we introduce an enhanced CS-GAN with lightweight modules (CAM, SFEM, ASRM) for sparse indication, bridging the two stages via a unique soft-mask channel separation mechanism to preserve multi-modal features. Comprehensive experiments in a 5 × 5 × 3 m<sup>3</sup> indoor environment with 16 access points demonstrate that HECC-VLP achieves position errors of 4.51 cm (MAE) / 6.02 cm (RMSE) and attitude errors of 3.76<sup>∘</sup> / 4.71<sup>∘</sup>. Notably, compared to traditional compressed sensing baselines (OMP/BP), the proposed method achieves a 70–76% reduction in localization errors while delivering a 4.3–4.8 × computational acceleration. Overall, the framework supports dynamic target scaling (2–10 MTs) with real-time inference (87.3 ms), demonstrating its effectiveness for precision-critical and scalable industrial applications.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"74 ","pages":"Article 102985"},"PeriodicalIF":2.2,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883589","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}
With the rapid development of intelligent transportation systems (ITS), vehicle-to-everything (V2X) communications face growing security and reliability challenges due to malicious interference and high-speed movement can lead to fast time-varying channels, which makes it difficult for traditional security mechanisms to maintain secrecy. To address these challenges, this paper proposes a deep reinforcement learning (DRL) approach based on improved soft actor-critic (SAC) algorithm for physical layer security (PLS) in V2X. Based on three constructed vehicular communication modes, this paper addresses the insufficient exploration efficiency of the conventional SAC algorithm in dynamic vehicular environments by proposing a DRL method with enhanced exploration capability. By incorporating a learnable NoisyNet, the proposed approach achieves adaptive noise variance adjustment while optimizing for maximum entropy and immediate rewards. This improvement not only preserves the exploration advantages of SAC but also enables dynamic adjustment of exploration intensity, significantly enhancing the method’s adaptability in complex environments. Furthermore, to accelerate convergence, a prioritized experience replay (PER) mechanism is adopted to optimize the experience sampling process, effectively improving training efficiency. Simulation results under 3GPP urban scenarios show that our approach improves average secrecy probability (ASP) by 12.04% and average secrecy capacity (ASC) by 6.74% compared with the conventional SAC, while satisfying resource constraints and real-time latency requirements.
{"title":"Physical layer eavesdropping defense scheme for V2X based on improved SAC algorithm","authors":"Zhaodi Li , Longxia Liao , Shuang Gu , Junhui Zhao","doi":"10.1016/j.phycom.2025.102980","DOIUrl":"10.1016/j.phycom.2025.102980","url":null,"abstract":"<div><div>With the rapid development of intelligent transportation systems (ITS), vehicle-to-everything (V2X) communications face growing security and reliability challenges due to malicious interference and high-speed movement can lead to fast time-varying channels, which makes it difficult for traditional security mechanisms to maintain secrecy. To address these challenges, this paper proposes a deep reinforcement learning (DRL) approach based on improved soft actor-critic (SAC) algorithm for physical layer security (PLS) in V2X. Based on three constructed vehicular communication modes, this paper addresses the insufficient exploration efficiency of the conventional SAC algorithm in dynamic vehicular environments by proposing a DRL method with enhanced exploration capability. By incorporating a learnable NoisyNet, the proposed approach achieves adaptive noise variance adjustment while optimizing for maximum entropy and immediate rewards. This improvement not only preserves the exploration advantages of SAC but also enables dynamic adjustment of exploration intensity, significantly enhancing the method’s adaptability in complex environments. Furthermore, to accelerate convergence, a prioritized experience replay (PER) mechanism is adopted to optimize the experience sampling process, effectively improving training efficiency. Simulation results under 3GPP urban scenarios show that our approach improves average secrecy probability (ASP) by 12.04% and average secrecy capacity (ASC) by 6.74% compared with the conventional SAC, while satisfying resource constraints and real-time latency requirements.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"74 ","pages":"Article 102980"},"PeriodicalIF":2.2,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938672","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}
Pub Date : 2025-12-28DOI: 10.1016/j.phycom.2025.102976
Lijuan Zhang , Meiqi Liu , Zhongpeng Wang
In this letter, we propose the structure constrained clustering detector (SCCD), a CSI-free blind detector for RIS-assisted received spatial modulation (RIS-RSM) systems. We formulate detection as an unsupervised clustering task and incorporate the RIS-RSM signal structure via a geometric regularizer. First, SCCD adopts a distribution-driven centroid initialization that leverages established amplitude-phase regularities of the RIS-induced equivalent channel, so each initial center approximates the expected received signal for its antenna-symbol pair without using CSI. Then, we augment K-means with a structure-constrained geometric regularizer that aligns every centroid with the model-consistent received-signal pattern, estimate a single global scale from unlabeled data, and update the centroids via a closed-form update that retains K-means-level complexity. Simulation results show that SCCD achieves near maximum likelihood performance across modulation orders and antenna configurations, outperforming existing detection methods in both accuracy and robustness while remaining entirely CSI-free and low complexity.
{"title":"Structure constrained blind clustering detector for RIS-Assisted spatial modulation systems","authors":"Lijuan Zhang , Meiqi Liu , Zhongpeng Wang","doi":"10.1016/j.phycom.2025.102976","DOIUrl":"10.1016/j.phycom.2025.102976","url":null,"abstract":"<div><div>In this letter, we propose the structure constrained clustering detector (SCCD), a CSI-free blind detector for RIS-assisted received spatial modulation (RIS-RSM) systems. We formulate detection as an unsupervised clustering task and incorporate the RIS-RSM signal structure via a geometric regularizer. First, SCCD adopts a distribution-driven centroid initialization that leverages established amplitude-phase regularities of the RIS-induced equivalent channel, so each initial center approximates the expected received signal for its antenna-symbol pair without using CSI. Then, we augment K-means with a structure-constrained geometric regularizer that aligns every centroid with the model-consistent received-signal pattern, estimate a single global scale from unlabeled data, and update the centroids via a closed-form update that retains K-means-level complexity. Simulation results show that SCCD achieves near maximum likelihood performance across modulation orders and antenna configurations, outperforming existing detection methods in both accuracy and robustness while remaining entirely CSI-free and low complexity.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"74 ","pages":"Article 102976"},"PeriodicalIF":2.2,"publicationDate":"2025-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883591","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}
Pub Date : 2025-12-28DOI: 10.1016/j.phycom.2025.102986
Shuang Ma , Xuenan Li , Ju Zhang , Zhandong Li , Hongmei Li , Xiaoyu Lan
Direction of Arrival (DOA) estimation is a core technology for enhancing radar and communication performance. However, the large size and complexity of radio frequency (RF) systems hinder the miniaturization and portability of existing DOA estimation systems. This paper presents an innovative DOA method by fixed-frequency beam scanning Leaky Wave Antenna (LWA) to achieve a compact, affordable, and energy-efficient system for coherent signals spatial spectrum estimation. The multi-channel receiver mode is constructed by replacing the traditional array antennas with fixed-frequency beam scanning LWAs. Furthermore, the approach integrates the radiation characteristic of the LWA with a fixed-frequency scanning beam smoothing to create virtual LWA subarrays, which are capable of effectively receiving multiple coherent signals. A Virtual Sub-Arrays Estimation of Signal Parameters via Rotational Invariance Techniques (VSA-ESPRIT) algorithm is developed to address the rank deficiency problem that arises when using an LWA as the receiving device for coherent signals. Simulation results confirm the method's efficacy in precisely estimating the DOA of coherent signals.
{"title":"DOA estimation of coherent signals via fixed-frequency beam scanning leaky wave antenna and VSA-ESPRIT algorithm","authors":"Shuang Ma , Xuenan Li , Ju Zhang , Zhandong Li , Hongmei Li , Xiaoyu Lan","doi":"10.1016/j.phycom.2025.102986","DOIUrl":"10.1016/j.phycom.2025.102986","url":null,"abstract":"<div><div>Direction of Arrival (DOA) estimation is a core technology for enhancing radar and communication performance. However, the large size and complexity of radio frequency (RF) systems hinder the miniaturization and portability of existing DOA estimation systems. This paper presents an innovative DOA method by fixed-frequency beam scanning Leaky Wave Antenna (LWA) to achieve a compact, affordable, and energy-efficient system for coherent signals spatial spectrum estimation. The multi-channel receiver mode is constructed by replacing the traditional array antennas with fixed-frequency beam scanning LWAs. Furthermore, the approach integrates the radiation characteristic of the LWA with a fixed-frequency scanning beam smoothing to create virtual LWA subarrays, which are capable of effectively receiving multiple coherent signals. A Virtual Sub-Arrays Estimation of Signal Parameters via Rotational Invariance Techniques (VSA-ESPRIT) algorithm is developed to address the rank deficiency problem that arises when using an LWA as the receiving device for coherent signals. Simulation results confirm the method's efficacy in precisely estimating the DOA of coherent signals.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"74 ","pages":"Article 102986"},"PeriodicalIF":2.2,"publicationDate":"2025-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938678","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}
Pub Date : 2025-12-27DOI: 10.1016/j.phycom.2025.102987
Sanjeev Kumar , Manjeet Singh
Three dimensional localization in Mobile Wireless Sensor Networks (MWSNs) is essential for emerging applications ranging from autonomous monitoring to smart urban infrastructure where accurate node positioning is essential for operational efficiency. However, mobility, signal fluctuations, and limited anchor availability often degrade localization accuracy. To tackle these issues, the present work proposes a Coordinate Based 3D (CB3D) localization algorithm that combines a secondary coordinate system with an iterative gradient-boosting refinement mechanism. It employs RSSI-based distance measurements within a mathematical framework, enabling iterative refinement of node coordinates. The present study conducted in a 200 × 200 × 200 m³ deployment region and simulations were performed across 500 independent runs, varying node densities (10 to 150 nodes), anchor availability (1 to 10 anchors), mobility conditions (0 to 7 m/s), and path-loss, fading parameters. The results show that, the proposed algorithm achieves a mean localization error of 0.72 m, average accuracy of 96.53 %, along with consistent variance as low as 0.13 m under high mobility. It also exhibits rapid convergence, reducing error from 2.9 m to 0.3 m within 500 iterations, and conserves computational efficiency with an execution time of 10.1 s. Compared to its counterpart like Regression Tree, Optimized Localization Learning Algorithm, Multi-Linear Regression, 3D Manifold Learning, and Artificial Neural Networks the proposed method achieves 15 to 37 % higher accuracy and demonstrates the lowest RMSE of 3.64 m across all scenarios. Further statistical validation through Wilcoxon rank sum tests shows that CB3D surpass over existing methods (p < 0.001), with the lowest mean ALE of 3.11 m and standard deviation of 0.39 m.
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Pub Date : 2025-12-27DOI: 10.1016/j.phycom.2025.102979
Xiangkun He , Jiacheng Liu , Yue Su , Da Li , Jiayuan Cui , Jiabiao Zhao , Mingxia Zhang , Wenbo Liu , Fei Song , Jianjun Ma
Terahertz (THz) communication technology has emerged as a promising candidate for next-generation vehicular networks by enabling high-speed data transmission and low-latency performance. However, it faces significant challenges from channel propagation through vehicular components, such as signal attenuation through window glass, blockage by metallic pillars and vehicle body, and complex reflection and scattering effects from multilayer window structures. This article presents a systematic investigation of THz channel propagation through vehicle windows, examining both static and dynamic scenarios through extensive experimental measurements and theoretical modeling. Using a precision measurement system operating at 120–165 GHz and 220–320 GHz frequency bands, we characterize power loss through single and dual-layer vehicle window glass under various window open-close configurations. We develop and validate theoretical models, based on multilayer Fresnel theory, for both single and dual-layer configurations, achieving excellent agreement with experimental measurements across varying frequencies and incidence angles. These findings provide essential insights for optimizing THz vehicular communication systems, particularly regarding antenna placement and link budget calculations.
{"title":"Experimental and theoretical investigation of terahertz channel propagation through vehicle windows","authors":"Xiangkun He , Jiacheng Liu , Yue Su , Da Li , Jiayuan Cui , Jiabiao Zhao , Mingxia Zhang , Wenbo Liu , Fei Song , Jianjun Ma","doi":"10.1016/j.phycom.2025.102979","DOIUrl":"10.1016/j.phycom.2025.102979","url":null,"abstract":"<div><div>Terahertz (THz) communication technology has emerged as a promising candidate for next-generation vehicular networks by enabling high-speed data transmission and low-latency performance. However, it faces significant challenges from channel propagation through vehicular components, such as signal attenuation through window glass, blockage by metallic pillars and vehicle body, and complex reflection and scattering effects from multilayer window structures. This article presents a systematic investigation of THz channel propagation through vehicle windows, examining both static and dynamic scenarios through extensive experimental measurements and theoretical modeling. Using a precision measurement system operating at 120–165 GHz and 220–320 GHz frequency bands, we characterize power loss through single and dual-layer vehicle window glass under various window open-close configurations. We develop and validate theoretical models, based on multilayer Fresnel theory, for both single and dual-layer configurations, achieving excellent agreement with experimental measurements across varying frequencies and incidence angles. These findings provide essential insights for optimizing THz vehicular communication systems, particularly regarding antenna placement and link budget calculations.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"74 ","pages":"Article 102979"},"PeriodicalIF":2.2,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883587","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}
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