Physical Communication最新文献

{"title":"Enhanced AI–simulink hybrid framework for low-latency interference mitigation and performance optimization in 5 G RF receivers","authors":"Shatha Kareem Abbas,&nbsp;Osman Nuri Uçan,&nbsp;AlSeddiq Oday","doi":"10.1016/j.phycom.2026.102995","DOIUrl":"10.1016/j.phycom.2026.102995","url":null,"abstract":"<div><div>'The rapid expansion of 5 G wireless communication systems has accelerated the need for intelligent receiver architectures capable of adaptively mitigating interference while preserving signal integrity. This study presents an enhanced hybrid AI–Simulink framework that integrates deep learning-based estimation with a parametric Simulink RF signal chain to detect, predict, and suppress jamming and noise-induced distortion in 5 G RF receivers. A processed dataset of approximately 250,000 labeled signal samples was generated from Simulink simulations and used to train and validate the proposed model. The framework demonstrates substantial improvements in signal-to-noise ratio (SNR) and root mean square error (RMSE) when compared with traditional filtering and baseline machine learning approaches. Experimental results show an SNR enhancement of over +14 dB and consistently low error metrics across multiple interference power levels and frequency configurations. The proposed architecture maintains a compact computational footprint (≈8.4 MB) and supports low-latency inference suitable for integration into hardware-accelerated or embedded execution environments. These outcomes confirm the potential of the proposed hybrid approach as a precise and efficient solution for AI-assisted interference mitigation in 5 G receivers, while also outlining future directions toward over-the-air validation and FPGA-based deployment.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 102995"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024415","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":"Measurement-based analysis of RIS-assisted MIMO channel characteristics and capacity at 6 GHz in an outdoor scenario","authors":"Shiyu Zhang ,&nbsp;Yuxiang Zhang ,&nbsp;Jianhua Zhang ,&nbsp;Huiwen Gong ,&nbsp;Yutong Sun ,&nbsp;Jialin Wang ,&nbsp;Tao Jiang ,&nbsp;Zheng Hu","doi":"10.1016/j.phycom.2026.103007","DOIUrl":"10.1016/j.phycom.2026.103007","url":null,"abstract":"<div><div>Reconfigurable intelligent surfaces (RIS) are a promising technology for future wireless networks. However, the channel characteristics of RIS-assisted multiple-input multiple-output (MIMO) systems remain insufficiently studied, especially in the 6 GHz band. To address this, an outdoor street-corner measurement campaign was conducted using a 32  ×  56 MIMO array and an RIS operating at 6 GHz with 200 MHz bandwidth. The measured power-angle-delay profiles (PADPs) show that RIS reinforcement strengthens reflected paths and increases received power, while reduced root mean square (RMS) delay and angular spreads indicate a mitigation of temporal and angular dispersion. Channel capacity and eigenvalue analyses further reveal that although RIS improves capacity, it slightly limits spatial multiplexing. These findings suggest that the primary advantage of RIS-assisted MIMO lies in enhancing power and link robustness under non-line-of-sight (NLoS) conditions, rather than universally improving multiplexing capability. This emphasizes the need for environment-aware RIS deployment strategies that balance power gain and spatial richness in future wireless systems.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103007"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024414","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":"Beam search assisted DRX mechanism in 5G networks: A machine learning and Semi-Markov modeling integration","authors":"Anupam Gautam,&nbsp;Mahima Kumar","doi":"10.1016/j.phycom.2026.103043","DOIUrl":"10.1016/j.phycom.2026.103043","url":null,"abstract":"<div><div>The integration of 5G New Radio into unlicensed spectrum (NR-U) offers potential to expand capacity and enhance user experience. However, the shared environment is highly dynamic due to coexistence with technologies such as Wi-Fi, making conventional static Discontinuous Reception (DRX) mechanisms insufficient. These limitations lead to unnecessary wake-ups, increased delay, and reduced energy efficiency for User Equipment (UE). To address this challenge, this work proposes a beam search assisted adaptive DRX framework that enables reliable directional access under unlicensed operation. The framework integrates Machine Learning (ML) based channel status prediction to determine whether the channel is available or congested by analyzing Wi-Fi traffic patterns including Inter-Frame Space statistics and collision ratios. The DRX mechanism is further modeled using a Semi-Markov model to capture realistic state timing dynamics and non-exponential holding times. Multiple ML classifiers are evaluated on a dataset of 20,000 samples. Simulation results show that the LSTM model achieves 96.8% prediction accuracy and improves Power Saving Factor from 83.2% to 87.8%, throughput from 30.6 Mbps to 60.4 Mbps, and reduces delay from 125.3 ms to 50.9 ms compared with non-predictive DRX operation. These results demonstrate that prediction-driven DRX mechanism significantly improves energy efficiency and latency performance in NR-U deployments.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103043"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397643","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":"Time-window-constrained computation offloading via cost-efficient multi-UAV scheduling in space-air-ground integrated networks","authors":"Zhe Han ,&nbsp;Guoqiang Zheng ,&nbsp;Chuanfeng Li ,&nbsp;Baofeng Ji","doi":"10.1016/j.phycom.2026.103042","DOIUrl":"10.1016/j.phycom.2026.103042","url":null,"abstract":"<div><div>In air-ground collaborative sensing scenarios such as periodic environmental monitoring, emergency rescue, and military reconnaissance in remote areas, computation tasks are often subject to strict time window constraints, urgently necessitating efficient computing mechanisms to guarantee their timeliness. We investigate the joint optimization problem of computation offloading and unmanned aerial vehicle (UAV) scheduling under time-window constraints within a collaborative computing architecture involving UAVs and high-altitude platform (HAP). To reduce the number of dispatched UAVs and their energy consumption, a mixed-integer nonlinear programming problem is formulated aiming at minimizing the execution cost of computation tasks. Addressing the core challenge posed by the non-convex strong coupling between discrete and continuous variables, we propose a novel bi-level collaborative optimization strategy (BCOS). The outer layer employs the covariance matrix adaptation evolution strategy (CMA-ES) to efficiently search for optimal task offloading ratios within the continuous domain, while the inner layer, given the offloading strategy, precisely solves the discrete UAV route planning subproblem based on the branch-and-price algorithm. Through the iterative interaction between the inner and outer layer algorithms, the joint optimization of discrete and continuous variables is achieved upon convergence. Simulation results demonstrate that BCOS can effectively coordinate the optimization of computational resources and UAVs scheduling strategies, significantly reducing the number of dispatched UAVs and energy consumption, thereby validating its effectiveness and superiority in complex task execution scenarios.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103042"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397646","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":"Signal classification based on multi-Scale time-Frequency transformer","authors":"Jiahuang Yang,&nbsp;Yuhao Wang,&nbsp;Meng Yang,&nbsp;Hua Meng","doi":"10.1016/j.phycom.2026.103022","DOIUrl":"10.1016/j.phycom.2026.103022","url":null,"abstract":"<div><div>Recognizing communication signals under non-cooperative remains challenging due to long waveform durations, noise corruption, and complex time-frequency structures. Existing convolutional neural network-, recurrent neural network-, and Transformer-based methods often rely on a single modeling paradigm and struggle to jointly capture local waveform patterns, long-range temporal dependencies, and complementary spectral information. To address these limitations, we propose MTF-Former, a Multi-scale time-frequency Transformer tailored for one-dimensional radio-frequency (RF) signals. MTF-Former combines signal-oriented data augmentation, a lightweight Time-Frequency Enhancement (TFE) block that injects frequency-aware modulation, and a hierarchical windowed Transformer encoder for efficient multi-scale temporal modeling. This unified design effectively integrates temporal and spectral cues while reducing computational cost compared with standard global self-attention Transformers for long sequences. Experiments on specific emitter identification (SEI) and automatic modulation recognition (AMR) benchmarks demonstrate that MTF-Former consistently outperforms many methods, achieving more notable performance gains at lower signal to noise ratio, with accuracy improvements of 1.30% on SEI (5 dB) and 2.42% on AMR (0 dB), and ablation studies further validate the contribution of each component.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103022"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397922","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 underreach protection relay using zero-mode band time-delay dynamics","authors":"Fusheng Li ,&nbsp;Fan Zhang ,&nbsp;Bin Qian ,&nbsp;Xiaodong Zhou ,&nbsp;Yi Luo ,&nbsp;Xiangyong Feng","doi":"10.1016/j.phycom.2026.102998","DOIUrl":"10.1016/j.phycom.2026.102998","url":null,"abstract":"<div><div>Traditional three-stage power system protection faces inherent conflicts among its four features. When protection settings avoid mal-operation during severe external faults without boundary elements, internal fault sensitivity is compromised. This limitation causes potential over-reach or under-reach mis-operation in conventional under-reach schemes. In this paper, a novel underreach local measurement-based protection scheme is proposed using zero-mode band time-delay dynamics. First, abrupt voltage changes detected in real-time by a Kalman filter initiate the proposed protection. Second, wavelet packet decomposition processes the zero-mode reverse traveling wave signal across multiple frequency bands. Two specific bands are extracted, Hilbert-transformed, and their first wave-head arrival time difference is calculated as the adaptive characteristic criterion. This value dynamically adjusts to fault locations. The theoretical travel time difference of arrival (TTDoA) serves as the dynamic action threshold. Simulations confirm accurate identification of abrupt changes and activation criteria for single-phase-to-ground faults at varying distances. The scheme reliably protects over 80% of the line length, precisely distinguishes internal from external faults, and operates correctly under high-resistance grounding faults.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 102998"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145915145","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":"Lightweight generative channel estimation with adaptive regularization in massive MIMO systems","authors":"Moonil Kim,&nbsp;Jaewoo So","doi":"10.1016/j.phycom.2026.103052","DOIUrl":"10.1016/j.phycom.2026.103052","url":null,"abstract":"<div><div>In massive multiple-input–multiple-output (MIMO) systems, as the number of transmit antennas increases, the number of transmit pilots increases for the channel estimation of user equipment. To reduce pilot overheads, generative-model-based channel estimation methods have been studied recently. In this paper, we propose a lightweight generative adversarial network (GAN)-based channel estimation scheme with adaptive regularization. Additionally, the proposed scheme groups users based on the channel collinearity matrix obtained using pre-acquired channel realizations for each user, and uses GANs trained on a per-user group basis. We design lightweight GAN architectures based on the locally centralized sparse characteristics of the beamspace channel. Here, we develop a novel objective function that adaptively determines the relative importance between the received pilots and the pre-trained generator. The simulation results show that the proposed scheme significantly improves performance in terms of the estimation accuracy and complexity when compared with conventional GAN-based channel estimation schemes. Moreover, the proposed scheme can achieve a flexible trade-off between the performance and the complexity of the training network.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103052"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397591","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":"Transformer-based outlier-tolerant adaptive beamforming algorithm","authors":"Zheng Xu ,&nbsp;Meng Wang ,&nbsp;Zihao Pan ,&nbsp;Ning Yang ,&nbsp;Daoxing Guo","doi":"10.1016/j.phycom.2026.103024","DOIUrl":"10.1016/j.phycom.2026.103024","url":null,"abstract":"<div><div>Adaptive beamforming technology plays a crucial role in modern communication systems, but existing deep learning methods lack robustness against data acquisition outliers. We propose an outlier-tolerant adaptive beamforming method based on the Transformer architecture for a 16-element linear antenna array with uniform inter-element spacing. First, an array signal model accounting for mutual coupling errors and data acquisition outliers is established, and an in-depth analysis is conducted of the performance degradation mechanism caused by data outliers arising from multiple factors, including computational precision, quantization errors, and system implementation. Second, a Transformer-based outlier signal classifier is designed that intelligently recognizes different types of outlier signals by fusing signal angle information and antenna weight features. On a large-scale dataset containing 56,373,835 samples, the classifier achieves an accuracy of 99.9990%. Finally, a two-stage Transformer adaptive beamforming method incorporating an outlier-aware mechanism is proposed. By combining the outlier-aware preprocessing module with the Transformer-based beamforming method, the system’s robustness to datasets with outliers is significantly enhanced. Experimental results demonstrate that under ideal conditions, the Transformer-based method achieves improvements in main lobe accuracy of approximately 15% and 16% compared to the traditional Null Steering Beamforming (NSB) and the Gated Recurrent Unit (GRU)-based adaptive beamforming methods, respectively, with approximately 10% improvement in the Signal to Interference plus Noise Ratio (SINR) performance. More importantly, under outlier levels of 10%-30%, the proposed outlier-tolerant method achieves significant performance improvement of approximately 75% in both main lobe accuracy and null accuracy compared to the conventional Transformer method, with main lobe accuracy stably maintained within 0.5° and null accuracy controlled within 0.1°, effectively addressing the data outlier robustness problem faced by deep learning beamforming algorithms in practical applications.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103024"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397925","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":"Multi – task reinforcement learning for UAV-enabled urban systems: Balancing trajectory planning and communication fairness","authors":"Xinqi He ,&nbsp;Yun Li ,&nbsp;Rongling Zhang","doi":"10.1016/j.phycom.2026.103046","DOIUrl":"10.1016/j.phycom.2026.103046","url":null,"abstract":"<div><div>Enhancing multi-task performance in unmanned aerial vehicle (UAV)-enabled urban communication systems remains challenging due to conflicting objectives, particularly the trade-off between trajectory-planning efficiency and communication fairness. This paper addresses this issue by jointly optimizing UAV path planning and hovering communication tasks. A UAV Network Fairness-Efficiency Model (UFEM) is proposed to quantify overall system performance by integrating an energy-efficiency metric for trajectory planning with a communication-fairness index for user scheduling, thereby capturing the inherent trade-off between these objectives. Building on this framework, a reinforcement learning (RL)-based urban downlink communication system is developed to track dynamic operating conditions through three-dimensional position observations and state-transition functions, while incorporating key environmental uncertainties such as a random wind model and a probabilistic line-of-sight (LoS) model. Task-specific reward functions are further designed to balance competing objectives and enable adaptive task switching. Based on these components, we introduce the Multi-Task Reinforcement Learning for UAV Maneuvers (MRLUM) algorithm, which jointly optimizes path planning and communication scheduling by fusing flight-state information and communication-channel data through an adaptive task-switching strategy. Simulation results demonstrate that MRLUM significantly improves both trajectory-planning efficiency and communication fairness under the UFEM metric, offering a promising solution for UAV-enabled urban communication systems facing multi-task conflicts and environmental uncertainties.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103046"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397948","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":"A novel beam training method for near-field wideband extremely large antenna arrays","authors":"Deyang Zhou,&nbsp;Ke Huang,&nbsp;Jingyi Guan,&nbsp;Yang Wang,&nbsp;Jianhe Du","doi":"10.1016/j.phycom.2026.103029","DOIUrl":"10.1016/j.phycom.2026.103029","url":null,"abstract":"<div><div>Extremely large antenna arrays (ELAA) is an important technology for future sixth-generation (6G) wireless networks and enables significantly high data transmission rates. However, existing beam training methods either require high overhead or perform poorly under limited training resources due to the joint estimation of angle and distance in the near-field. To address this problem, we design a novel three-stage beam training method for near-field wideband ELAA systems. Specifically, the base station (BS) first employs wide beams designed based on beam split analysis and sub-array partitioning for initial angle estimation to narrow the angle search space. Refined angle estimation of the user equipment (UE) is then achieved using symmetric narrow beams in the second stage. Finally, distance estimation is performed by leveraging the refined angle estimates to identify the optimal beam in the near-field. Simulation results show that our method achieves a near-optimal achievable rate with low training overhead. In addition, the proposed method exhibits smaller angle estimation errors compared with the existing beam training schemes.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103029"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397955","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}