Physical Communication最新文献_第7页

Integrated approach for antenna reduction and enhanced channel estimation in massive MIMO systems using semi-passive intelligent reflecting surfaces 基于半被动智能反射面的大规模MIMO系统天线减小和信道估计集成方法

IF 2.2 4区计算机科学

Physical Communication Pub Date : 2025-08-12 DOI: 10.1016/j.phycom.2025.102793

Ahmed S. Alwakeel

{"title":"Integrated approach for antenna reduction and enhanced channel estimation in massive MIMO systems using semi-passive intelligent reflecting surfaces","authors":"Ahmed S. Alwakeel","doi":"10.1016/j.phycom.2025.102793","DOIUrl":"10.1016/j.phycom.2025.102793","url":null,"abstract":"<div><div>Although massive multiple-input multiple-output (MIMO) systems offer substantial gains in spectral and energy efficiency, they require a large number of expensive active antennas at base station (BS). Intelligent reflecting surface (IRS), comprising semi-passive elements, present a potential solution to reduce deployment costs. This paper investigates the integration of IRS semi-passive elements to achieve a substantial reduction in the number of BS antennas while enhancing channel estimation techniques in massive MIMO systems. The main goal is to find the optimal number of IRS elements to reduce BS antennas cost-effectively, offering guidance to network designers. In addition, we propose a novel channel estimation approach tailored for IRS-integrated systems. Through rigorous analysis, we derive a closed-form formula that clarifies the relationship between the number of BS antennas and the number of IRS elements required for optimal reduction. Furthermore, we present a comparative evaluation of our proposed channel estimation technique against state-of-the-art methods, demonstrating its novelty, advantages, and performance characteristics. According to our findings, integrating approximately 30 IRS elements achieves a decrease of nearly 50% in the number of BS antennas. The suggested channel estimate method exhibits superior performance in IRS-integrated systems, offering valuable insights for practical implementation.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"72 ","pages":"Article 102793"},"PeriodicalIF":2.2,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144864600","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}

引用次数: 0

Performance evaluation of AI-based CSI feedback schemes compliant with 3GPP standards 基于ai的符合3GPP标准的CSI反馈方案的性能评价

IF 2.2 4区计算机科学

Physical Communication Pub Date : 2025-08-10 DOI: 10.1016/j.phycom.2025.102803

Rahul Pal , Vikram Singh , Vijaya Mareedu

{"title":"Performance evaluation of AI-based CSI feedback schemes compliant with 3GPP standards","authors":"Rahul Pal , Vikram Singh , Vijaya Mareedu","doi":"10.1016/j.phycom.2025.102803","DOIUrl":"10.1016/j.phycom.2025.102803","url":null,"abstract":"<div><div>This paper addresses the limitations of traditional CSI feedback schemes in 5G massive MIMO-OFDM systems, particularly the challenges of high feedback overhead and computational complexity. To overcome these issues, this paper proposes and presents an in-depth evaluation of “M-CsiNet”, an AI-based feedback channel state information compression and reconstruction model adhering to 3GPP standards using the “CDL-C”MIMO channel model. The innovation of “M-CsiNet” lies in the extension of CsiNet to “M-CsiNet” and conducting an in-depth evaluation. Unlike legacy methods such as Type-II and Enhanced Type-II codebook-based schemes, “M-CsiNet” demonstrates significant improvements. Experimental results demonstrate that “M-CsiNet” achieves up to <em>10–15 dB SNR gain</em> in <em>link-level</em> block error rate (BLER) and throughput performance while reducing feedback overhead by <em>two orders</em> and with reduced complexity. These advantages make “M-CsiNet” a promising solution for practical deployment in capacity-constrained 5G and future wireless systems across both rank-1 and rank-2 transmission scenarios.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"72 ","pages":"Article 102803"},"PeriodicalIF":2.2,"publicationDate":"2025-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831310","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}

引用次数: 0

Federated Deep Reinforcement Learning-based task offloading strategy with game theory in vehicular edge computing 基于博弈论的基于深度强化学习的车辆边缘计算任务卸载策略

IF 2.2 4区计算机科学

Physical Communication Pub Date : 2025-08-10 DOI: 10.1016/j.phycom.2025.102802

Ming Deng , Wensheng Liang , Bin Qiu , Xiaolan Xie

{"title":"Federated Deep Reinforcement Learning-based task offloading strategy with game theory in vehicular edge computing","authors":"Ming Deng , Wensheng Liang , Bin Qiu , Xiaolan Xie","doi":"10.1016/j.phycom.2025.102802","DOIUrl":"10.1016/j.phycom.2025.102802","url":null,"abstract":"<div><div>The increasing complexity of Internet of Vehicles (IOV) applications poses significant challenges to vehicular onboard computing resources, leading to heightened latency and energy consumption. Task offloading techniques in vehicular edge computing (VEC) offer a promising solution by transferring computational tasks to distributed edge servers with enhanced processing power. However, in highly dynamic VEC scenarios, multiple vehicles tend to offload tasks concurrently, exacerbating system challenges. An inappropriate offloading strategy can result not only in increased system latency but also in severe privacy breaches. To address these issues, a federated deep reinforcement learning-based task offloading strategy with game theory (FDRLGT) is proposed to minimize total system delay and protect user privacy. Specifically, Deep Reinforcement Learning (DRL) is used to train a local offloading strategy model with local data, while Federated Learning (FL) aggregates local model parameters instead of raw data to ensure privacy. In multi-vehicle simultaneous task offloading contexts, we address the problem of policy homogeneity in FDRL, which can lead to locally suboptimal solutions. To overcome this, we design a game theory model integrated into the FDRL algorithm to enhance optimization. Simulation results demonstrate that the proposed FDRLGT algorithm enhances system efficiency, ensures privacy, and effectively reduces total system delay compared to other baseline algorithms.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"72 ","pages":"Article 102802"},"PeriodicalIF":2.2,"publicationDate":"2025-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831309","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}

引用次数: 0

Simulation-based comparison of energy and spectral efficiency in IRS-assisted wireless communication systems 基于仿真的irs辅助无线通信系统能量和频谱效率比较

IF 2.2 4区计算机科学

Physical Communication Pub Date : 2025-08-09 DOI: 10.1016/j.phycom.2025.102800

Ayalew Tadese Kibret , Amare Kassaw Yimer , Belayneh Sisay Alemu

引用次数: 0

An analytical study on secure communication over fading wiretap channels with encoder-assisted side information and generalized shadow fading 基于编码器辅助侧信息和广义阴影衰落的衰落窃听信道安全通信分析研究

IF 2.2 4区计算机科学

Physical Communication Pub Date : 2025-08-09 DOI: 10.1016/j.phycom.2025.102801

Ali Mohammad Khodadoust , Mario Eduardo Rivero-Ángeles , Víctor Barrera-Figueroa

引用次数: 0

Adaptive acknowledgment control in ultra-dense LoRaWAN using lightweight machine learning 基于轻量级机器学习的超密集LoRaWAN自适应确认控制

IF 2.2 4区计算机科学

Physical Communication Pub Date : 2025-08-05 DOI: 10.1016/j.phycom.2025.102799

Leila Aissaoui Ferhi

{"title":"Adaptive acknowledgment control in ultra-dense LoRaWAN using lightweight machine learning","authors":"Leila Aissaoui Ferhi","doi":"10.1016/j.phycom.2025.102799","DOIUrl":"10.1016/j.phycom.2025.102799","url":null,"abstract":"<div><div>Ultra-dense Low-Power Wide-Area Networks (LPWANs) face critical challenges in maintaining reliable and scalable communication due to increased contention, stringent duty-cycle regulations and energy constraints. These limitations are particularly pronounced in LoRaWAN deployments where thousands of end-devices compete for severely constrained downlink capacity. This paper addresses the pressing issue of sustaining efficient bidirectional communication in such environments by introducing a novel, context-aware acknowledgment control mechanism. Our approach replaces the conventional static confirmed mode with a lightweight, online logistic regression model embedded at the gateway enabling real-time, probabilistic ACK decisions informed by dynamic network conditions. Extensive MATLAB-based simulations involving up to 3000 devices show that the proposed strategy increases uplink delivery rates by over 50 % at scale (compared to 32 % with the standard approach), maintains downlink responsiveness above 15 % and reduces energy consumption by up to 15 % in sparse and 8–10 % in ultra-dense deployments. These results demonstrate the feasibility and effectiveness of integrating lightweight machine learning at the MAC layer as a protocol-compliant solution to improve the scalability, efficiency and resilience of next-generation LoRaWAN systems.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"72 ","pages":"Article 102799"},"PeriodicalIF":2.2,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841870","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}

引用次数: 0

Analysis of physics-based transmission mechanism for air-to-ground wireless communication systems 空对地无线通信系统物理传输机制分析

IF 2.2 4区计算机科学

Physical Communication Pub Date : 2025-08-05 DOI: 10.1016/j.phycom.2025.102794

Shichen Jia , Zhimin Chen , Shuran Sheng

{"title":"Analysis of physics-based transmission mechanism for air-to-ground wireless communication systems","authors":"Shichen Jia , Zhimin Chen , Shuran Sheng","doi":"10.1016/j.phycom.2025.102794","DOIUrl":"10.1016/j.phycom.2025.102794","url":null,"abstract":"<div><div>This paper presents a three-dimensional (3D) geometry-based stochastic channel model (GBSM) specifically designed for air-to-ground (A2G) communication systems. The proposed model integrates dynamic environmental interactions by modeling the mobilities of unmanned aerial vehicles (UAVs), ground terminals, and stochastic behaviors of scattering clusters. By capturing UAV trajectory dynamics, including rotation angles and varying flight attitudes, the model accurately reflects the non-stationary characteristics inherent to aerial communication environments. Moreover, dynamic blockage effects caused by buildings, vegetation, and other obstacles commonly encountered in urban and suburban areas are incorporated to enhance environmental realism. Both line-of-sight (LoS) and non-line-of-sight (NLoS) propagation scenarios are considered to comprehensively characterize the channel behavior under diverse operational conditions. Subsequently, analytical expressions for key channel characteristics, including spatial cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), frequency-correlation functions (FCFs), and channel capacity, are systematically derived and examined. Simulation results demonstrate that the proposed model effectively characterizes the rapidly time-varying A2G propagation conditions, offering a reliable framework for performance evaluation and optimization in next-generation aerial wireless networks.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"72 ","pages":"Article 102794"},"PeriodicalIF":2.2,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144781070","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}

引用次数: 0

FD-NOMA-enabled UAV-vehicle collaborative networks: Channel capacity analysis with imperfect CSI under Rayleigh fading 基于fd - noma的无人机协同网络：瑞利衰落下不完美CSI信道容量分析

IF 2.2 4区计算机科学

Physical Communication Pub Date : 2025-08-05 DOI: 10.1016/j.phycom.2025.102795

Wei Zhou , Yixin He , Fanghui Huang , Dawei Wang , CongLing Xi , Ruonan Zhang , Xingchen Zhou

{"title":"FD-NOMA-enabled UAV-vehicle collaborative networks: Channel capacity analysis with imperfect CSI under Rayleigh fading","authors":"Wei Zhou , Yixin He , Fanghui Huang , Dawei Wang , CongLing Xi , Ruonan Zhang , Xingchen Zhou","doi":"10.1016/j.phycom.2025.102795","DOIUrl":"10.1016/j.phycom.2025.102795","url":null,"abstract":"<div><div>The continuous growth in the number of vehicles has led to increasingly scarce spectrum resources. However, uncrewed aerial vehicles (UAVs), with their flexibility and mobility, combined with full-duplex non-orthogonal multiple access (FD-NOMA) technology, form a UAV-vehicle collaborative networks that offers a potential solution for improving spectrum efficiency. Influenced by the mobility of UAVs and vehicles, it is crucial to study how to quickly and accurately analyze the total channel capacity. Therefore, we derive closed expressions and approximate solutions for the total channel capacity in FD-NOMA-enhanced UAV-vehicle collaborative networks. In addition, considering the difficulty of accurately obtaining channel state information (CSI) in real time, a deep learning-based CSI estimation method is designed. By incorporating least square (LS) coarse estimation, deep neural network (DNN) denoising, bidirectional long short-time memory (BiLSTM) time-domain prediction, and weighted dimensionality reduction processing, the estimation accuracy in high-speed scenarios is significantly improved. Finally, the simulation results show that the capacity of the constructed FD-NOMA system in the low signal to noise ratio (SNR) region is improved by about 1.8–2.5 bps/Hz compared with that of full-duplex orthogonal multiple access (FD-OMA), and the CSI estimation error based on deep learning is reduced by 85% compared with that of the traditional LS algorithm. In addition, stable channel capacity is maintained at vehicle speeds up to 80 km/h.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"72 ","pages":"Article 102795"},"PeriodicalIF":2.2,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771038","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}

引用次数: 0

Fractional Doppler channel estimation for OTFS high-speed railway train-to-ground communication system OTFS高速铁路车地通信系统的分数多普勒信道估计

IF 2.2 4区计算机科学

Physical Communication Pub Date : 2025-07-30 DOI: 10.1016/j.phycom.2025.102788

Zhanjun Jiang , Kewei Liu , Haoyu Quan , Junhui Zhao

{"title":"Fractional Doppler channel estimation for OTFS high-speed railway train-to-ground communication system","authors":"Zhanjun Jiang , Kewei Liu , Haoyu Quan , Junhui Zhao","doi":"10.1016/j.phycom.2025.102788","DOIUrl":"10.1016/j.phycom.2025.102788","url":null,"abstract":"<div><div>Orthogonal time frequency space (OTFS) modulation effectively mitigates the Doppler effect in high-speed railway (HSR) train-to-ground communication, leveraging its robustness in time-frequency doubly-selective fading environments. However, current off-grid sparse Bayesian learning (OGSBL) methods based on fixed grids suffer from two primary limitations: insufficient accuracy in frequency shift quantization and the accumulation of errors from Taylor approximations. In response, this paper proposes a non-uniform grid optimization-based OGSBL channel estimation method. Firstly, a non-uniform dynamic grid partitioning strategy based on an exponential growth law is proposed to address the quantization inaccuracy caused by the Doppler effect. This method assigns higher resolution to high Doppler frequency regions while maintaining lower sampling density in low Doppler frequency regions, striking a balance between accuracy and computational complexity. Secondly, a sensing matrix optimization mechanism based on a multi-variable joint update is proposed to reduce Taylor approximation error accumulation. This mechanism facilitates dynamic reconstruction of the sensing matrix, suppressing error accumulation and accelerating convergence through the alternate update of the integer Doppler matrix, offset coupling matrix, and off-grid Doppler coefficients. Simulation results demonstrate that compared to on-grid estimation and conventional OGSBL methods, the proposed solution achieves significant improvement in channel estimation precision and convergence rate.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"72 ","pages":"Article 102788"},"PeriodicalIF":2.2,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780883","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}

引用次数: 0

Lightweight physical-layer authentication for IoT devices access against jamming attacks 针对物联网设备访问干扰攻击的轻量级物理层认证

IF 2.2 4区计算机科学

Physical Communication Pub Date : 2025-07-30 DOI: 10.1016/j.phycom.2025.102787

Xinyue Yao, Helin Yang, Weiwei Zeng

{"title":"Lightweight physical-layer authentication for IoT devices access against jamming attacks","authors":"Xinyue Yao, Helin Yang, Weiwei Zeng","doi":"10.1016/j.phycom.2025.102787","DOIUrl":"10.1016/j.phycom.2025.102787","url":null,"abstract":"<div><div>Radio frequency fingerprint identification (RFFI) is a reliable non-cryptographic physical layer security technique that leverages transmitter hardware features for device identification. However, Internet of Things (IoT) environments often contain various interference sources, which can cause physical layer access authentication failures. Furthermore, RFFI systems typically require substantial computational resources. To reduce computing resources, we propose a lightweight and scalable IoT wireless signal identity detection system against jamming attacks. We propose SE-MobileNet, a lightweight convolutional neural network that enhances MobileNetV2 by replacing its repetitive bottleneck blocks with squeeze-and-excitation (SE) modules, thereby augmenting channel-wise feature recalibration and improving representational capacity. Specifically, the received signals are firstly transformed into channel independent spectrograms through multiple processing steps, and fed into SE-MobileNet to train the radio frequency fingerprint (RFF) feature extractor. Then, the RFF feature extractor fetches the RFF features of the devices and uses k-nearest neighbor (KNN) to complete abnormal device detection and legitimate device classification. Experimental results show that the system model occupies only 2.41MB, representing a 99% reduction compared to existing benchmarks. We can achieve the highest area under curve (AUC) value of 0.971 for abnormal device detection and 97.6% accuracy rate for legitimate device classification. Even under co-channel interference attacks, SE-MobileNet maintains detection accuracy above 0.70 in weak interference environments and achieves or exceeds 0.95 at moderate-to-high SIR levels. This indicates that the model can maintain high accuracy despite significant model compression.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"72 ","pages":"Article 102787"},"PeriodicalIF":2.2,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771090","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}

引用次数: 0