Physical Communication最新文献

{"title":"Structure constrained blind clustering detector for RIS-Assisted spatial modulation systems","authors":"Lijuan Zhang ,&nbsp;Meiqi Liu ,&nbsp;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":"2026-02-01","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}

{"title":"Semantic security-aware multi-user resource allocation: A novel meaning-first scheduling","authors":"Jingyi Lang,&nbsp;Mengdan Ji","doi":"10.1016/j.phycom.2025.102928","DOIUrl":"10.1016/j.phycom.2025.102928","url":null,"abstract":"<div><div>This work investigates multi-user task-oriented semantic communications where bandwidth, transmit power, and edge compute cycles are orchestrated jointly under a passive eavesdropper. We define a system-level task utility that captures semantic importance and downstream inference accuracy, and we place two competing scheduling philosophies: a channel-state-first policy that prioritizes favorable propagation conditions, and a semantic-importance-first policy that prioritizes information units with the highest task value per resource. Building on a drift-plus-penalty framework, we develop an online secrecy-aware orchestration algorithm that relies only on causal state information, enforces latency and secrecy through virtual queues and a conditional value-at-risk controller, and exposes a tunable tradeoff between utility optimality and backlog. Each slot decomposes into weighted water-filling for bandwidth and power, lightweight scoring for compute allocation, and optional artificial noise or rate back-off when secrecy risk rises. We conduct extensive simulations for image classification on the ImageNet-100 benchmark using task-driven semantic encoders over Rayleigh and Rician fading with partial knowledge of the eavesdropper channel. Simulation results demonstrate the superior gains of the semantic-importance-first policy over channel-state-first at comparable delay, and a marked reduction in secrecy-outage events when risk control is enabled.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"74 ","pages":"Article 102928"},"PeriodicalIF":2.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145625380","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":"Data augmentation via noise injection and VMD for deep learning-based modulation recognition in few-shot scenario","authors":"Tao Chen ,&nbsp;Shilian Zheng","doi":"10.1016/j.phycom.2025.102945","DOIUrl":"10.1016/j.phycom.2025.102945","url":null,"abstract":"<div><div>Deep learning has been widely adopted in radio modulation recognition. However, when training data is insufficient, deep learning models are prone to overfitting, leading to a lack of generalization ability. In this letter, we use noise injection as a data augmentation strategy and combine it with variational mode decomposition (VMD) to suppress irrelevant noise components that maintain structural fidelity while exhibiting perturbation diversity. This modulation recognition framework is referred to as VMD-ResNet. We conduct experiments by adding additive white Gaussian noise (AWGN), additive general Gaussian noise (AGGN), and pink noise. Compared to the IQ-ResNet method without augmentation, the findings indicate that the level of improvement varies among different modulation types depending on the specific noise added. Comparative experiments confirm that the proposed VMD-ResNet achieves higher overall recognition performance than other few-shot methods. Additionally, we assess the model using unseen noise sequences to further validate its generalization capability. The experimental results demonstrate the method’s enhanced effectiveness for unseen noise.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"74 ","pages":"Article 102945"},"PeriodicalIF":2.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145748480","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":"UAV association and beam planning in NTN-assisted 5G networks: A TLBO framework","authors":"Maadoud Djihane,&nbsp;Hamza Abdelkrim,&nbsp;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":"2026-02-01","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}

{"title":"Fast low-sidelobe and multinull array pattern synthesis using power transmission efficiency","authors":"Yida Fan ,&nbsp;Lijuan Li ,&nbsp;Ravi Kumar Arya ,&nbsp;Junwei Dong ,&nbsp;Shiyuan Kong","doi":"10.1016/j.phycom.2025.102959","DOIUrl":"10.1016/j.phycom.2025.102959","url":null,"abstract":"<div><div>This paper introduces a fast low-sidelobe and multinull pattern synthesis method for arbitrarily configured array antennas by leveraging wireless power transmission theory. In this method, we formulate the pattern optimization objective, derive the optimal excitation distribution function, and detail the step-by-step implementation of the proposed method. By transforming joint constraints into a single-variable implicit equation through mathematical reformulation, the array excitation can be solved rapidly. Simulation results demonstrate the method’s capability to synthesize low-sidelobe radiation patterns with deep nulling interference suppression in arrays featuring non-uniform element distributions, conformal antennas, and sparse 2D configurations. Compared to Singular Value Decomposition (SVD) and traditional Chebyshev synthesis techniques, our approach achieves faster computation speeds and lower complexity while maintaining equivalent sidelobe levels, reducing the required element count by 11.1 % for equivalent performance. Experimental validation via near-field measurements of radiation patterns of 1D patch array and a 2D phased array confirms the method’s efficacy. The proposed technique is particularly suitable for low-sidelobe antenna design, directional interference suppression array design, and time-sensitive applications such as real-time phased array beam steering.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"74 ","pages":"Article 102959"},"PeriodicalIF":2.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797312","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":"Hybrid AI-driven optimization for real-time 6G ad hoc communications using fluid antenna systems","authors":"El Miloud Ar-Reyouchi ,&nbsp;Ayoub Hadj-Sadek ,&nbsp;Kamal Ghoumid ,&nbsp;Sami Hage-Ali ,&nbsp;Omar Elmazria","doi":"10.1016/j.phycom.2025.102948","DOIUrl":"10.1016/j.phycom.2025.102948","url":null,"abstract":"<div><div>Future sixth-generation (6G) Ad Hoc networks must sustain ultra-reliable, low-latency, and energy-efficient connectivity in highly dynamic wireless environments, where interference management, real-time antenna reconfiguration, and computational constraints remain major challenges. Fluid Antenna Systems (FAS) provide additional spatial degrees of freedom through position- and shape-reconfigurable radiating elements, but existing optimization schemes for FAS and next-generation reconfigurable antennas either treat beamforming, phase control, and antenna positioning separately or rely on high-complexity Artificial Intelligence (AI) models that are difficult to deploy under slot-level latency and power budgets. This paper aims to design a unified, low-complexity framework for real-time control of FAS in 6G ad hoc networks. We propose <strong>AI-HFASO</strong>, a hybrid AI framework in which a Multi-Task Coordination Controller (MTCC) jointly optimizes beamforming, interference mitigation, and antenna positioning by integrating deep learning (DL) for fast beamforming initialization, reinforcement learning (RL) for adaptive element positioning, and a delay-aware genetic algorithm (GA) for phase refinement under latency constraints. The main novelty lies in the joint multi-objective optimization of spectral efficiency, interference suppression, and energy efficiency at the slot level, while reducing computational complexity through lightweight AI modules and a hybrid AI-traditional optimization loop. Simulation results under realistic multi-user, multi-cell 6G scenarios show that AI-HFASO achieves up to <strong>31 %</strong> interference reduction, <strong>21 %</strong> throughput improvement, and <strong>18 %</strong> spectral-efficiency gain, while lowering computational overhead by about <strong>30 %</strong> compared to state-of-the-art MIMO, RIS, and AI-based baselines, demonstrating its potential as a scalable and latency-aware solution for FAS-enabled 6G ad hoc networks.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"74 ","pages":"Article 102948"},"PeriodicalIF":2.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145748479","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":"Downlink optimization for direct-to-satellite IoT with LEO satellites and LoRaWAN","authors":"Hassan Haghighi ,&nbsp;Maamar El Amine Hamri ,&nbsp;Davood Asadi ,&nbsp;Daniel Delahaye","doi":"10.1016/j.phycom.2025.102971","DOIUrl":"10.1016/j.phycom.2025.102971","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"74 ","pages":"Article 102971"},"PeriodicalIF":2.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145839958","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":"Optimizing UAV trajectories for quality of service enhancement in 5G urban deployments","authors":"Joaquin Salazar,&nbsp;Karel Toledo","doi":"10.1016/j.phycom.2025.102951","DOIUrl":"10.1016/j.phycom.2025.102951","url":null,"abstract":"<div><div>The rapid proliferation of bandwidth-hungry applications and the limitations of fixed terrestrial infrastructure in urban environments have driven interest in deploying unmanned aerial vehicles (UAVs) as adaptive aerial base station (BS) within fifth generation (5G) wireless networks. In this work, we develop a three-dimensional (3D) simulation framework that incorporates 3GPP-standardized clustered delay line (CDL) channel models, probabilistic user mobility, 5G-compliant protocol stacks, and both single-input single-output (SISO) and multiple-input multiple-output (MIMO) transmission schemes in an urban macro cell (UMa) setting. We formulate a trajectory planning optimization problem that balances average signal-to-noise ratio (SNR) and its variability to derive flight paths under geographic constraints. Through extensive simulations, we evaluate multiple UAV mobility strategies including centroid following, maximum SNR expected value, minimum SNR standard deviation, weighted SNR trade-off, and fixed hovering, using throughput and block error rate (BLER) as the primary quality of service (QoS) performance indicators. Results show that the weighted SNR strategy achieves consistently high throughput with reduced variability, while its MIMO configuration yields an average throughput gain of 193% compared to SISO, highlighting the combined benefits of trajectory optimization and spatial diversity. These findings validate the proposed optimization criterion and underscore the substantial benefits of aerial mobility and spatial diversity in next-generation wireless networks.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"74 ","pages":"Article 102951"},"PeriodicalIF":2.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797305","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":"Channel estimation in RIS-NOMA enabled 5G/B5G systems using meta-learned spatio-temporal graph neural networks","authors":"Aryan Mittal ,&nbsp;Sandeep Kumar Singh ,&nbsp;Amit Agarwal","doi":"10.1016/j.phycom.2025.102984","DOIUrl":"10.1016/j.phycom.2025.102984","url":null,"abstract":"<div><div>Accurate and adaptive channel estimation is essential for realizing intelligent and energy-efficient reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) systems. However, conventional learning-based channel estimation (CE) models often struggle to generalize across varying SNR, frequency, and environmental dynamics. This paper proposes a novel First-Order Model-Agnostic Meta-Learning (FOMAML)-based Spatio-Temporal Graph Neural Network (STGNN) framework for robust and frequency-adaptive CE in RIS-assisted multi-user systems. The proposed model jointly captures spatial dependencies among RIS elements and temporal channel variations using edge-conditioned graph convolutions and gated recurrent units (GRUs), while meta-learning enables rapid adaptation to unseen conditions with minimal fine-tuning. A synthetic yet realistic dataset covering multiple carrier frequencies (3.5 GHz, 28 GHz, and 60 GHz) and SNR ranges (10-30 dB) is generated to evaluate performance. Simulation results demonstrate that the proposed FOMAML-STGNN achieves superior generalization, attaining up to 15% lower NRMSE and higher <em>R</em>² scores compared to conventional CNN-BiLSTM and full MAML-GNN baselines, while maintaining reduced computational complexity. Moreover, system-level evaluations confirm that RIS-NOMA consistently outperforms RIS-OMA in achievable sum rate, and controlled ablation studies validate the scalability and robustness of the proposed framework under varying SNRs, RIS sizes, and user densities. These results highlight the suitability of the proposed approach for real-time and scalable CE in beyond-5G (B5G) networks.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"74 ","pages":"Article 102984"},"PeriodicalIF":2.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938676","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":"Frequency-mixing RIS-induced channel decoupling and delay-Phase conversion for angular sensing","authors":"Xianglin Shi,&nbsp;Jiangtian Gu,&nbsp;Fengkai Chen,&nbsp;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":"2026-02-01","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}