Physical Communication最新文献

{"title":"Performance analysis of energy harvesting IoT network in THz communication system with randomly located nodes with directional antenna","authors":"Anupam Shome ,&nbsp;Chittotosh Ganguly ,&nbsp;Amit Kumar Dutta","doi":"10.1016/j.phycom.2025.102860","DOIUrl":"10.1016/j.phycom.2025.102860","url":null,"abstract":"<div><div>This work assesses the performance of a compact ad-hoc network in the terahertz (THz) frequency band. The network comprises of several energy-harvesting internet of things (IoT) secondary users (SUs) surrounding a relay node (RN) amidst randomly positioned primary users (PUs). The RN is connected to an access point (AP), which, in turn, has a direct connection to the internet cloud. We assume that every communication node is equipped with directional antenna. Operating under the cognitive underlay paradigm, the small area ad-hoc network allows SUs to harvest energy from various PUs, making it suitable for practical applications in small-scale commercial establishments. The primary focus of this study is the analytical evaluation of the coverage probability and average throughput of a SU considering the mentioned scenario which is missing in available literature so far. Extensive numerical simulations are conducted to establish the theoretical results.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"73 ","pages":"Article 102860"},"PeriodicalIF":2.2,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267458","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":"Pilot design for multiple domains channel estimation in special cases of affine frequency division multiplexing","authors":"Vincent Savaux","doi":"10.1016/j.phycom.2025.102863","DOIUrl":"10.1016/j.phycom.2025.102863","url":null,"abstract":"<div><div>This paper deals with pilot design for affine frequency division multiplexing (AFDM) modulation, enabling multipath channel estimation in both affine and frequency domains, as well as frequency offset (Doppler) estimation in the time domain. The basic principle is straightforward as it involves transmitting a unique chirp subcarrier, with its parameters properly chosen to produce equispaced, constant-modulus subcarriers in the frequency domain. As a result, standard channel estimation methods used in OFDM can also be applied in an AFDM system using a discrete Fourier transform (DFT)-based receiver. Nevertheless, the channel estimation can also be performed in affine domain if a conventional discrete affine Fourier transform (DAFT)-based AFDM receiver is employed. Additionally, leveraging the invariance property of chirps under time-domain translation, a sub-optimal maximum likelihood (ML) estimator of the frequency offset is proposed, based on the same pilot design. Simulation results show that the proposed method leads to similar bit error rate (BER) performance when channel estimation is performed in the frequency domain, instead of the affine domain as usual. Moreover, AFDM significantly outperforms orthogonal frequency division multiplexing (OFDM) by more than 3 dB.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"73 ","pages":"Article 102863"},"PeriodicalIF":2.2,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221023","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":"Perception-assisted superimposed CSI feedback for millimeter wave massive MIMO systems","authors":"Chaojin Qing,&nbsp;Yuqiao Yang,&nbsp;Zilong Wang,&nbsp;Haowen Jiang,&nbsp;Yu Sun","doi":"10.1016/j.phycom.2025.102864","DOIUrl":"10.1016/j.phycom.2025.102864","url":null,"abstract":"<div><div>The channel state information (CSI) feedback in frequency division duplex (FDD) mode-based millimeter wave (mmWave) massive multiple-input multiple-output (mMIMO) systems encounters significant feedback overhead due to the huge number of the base station (BS) antennas. To address this issue, we introduce the superimposed scheme into the CSI feedback. However, severe superimposed interference and channel estimation (CE) errors at the user equipment (UE) significantly degrade the recovery accuracy of downlink CSI. Inspired by perception-assisted communications, a perception-assisted superimposed CSI feedback method is proposed in this paper. In the proposed method, we employ the classic multiple signal classification (MUSIC) algorithm to extract the angle of arrival (AoA) of resolvable echo paths to form the perception information. With this perception information, a perception-assisted CSI recovery scheme is developed. This scheme comprises two phases, referred to as the preprocessing phase and the iterative recovery phase. By utilizing the perception information, the angular filtering matrices are constructed in the preprocessing phase to eliminate the angular components of non-path directions. In the iterative recovery phase, the perception information is further leveraged to iteratively suppress the superimposed interference and CE errors, thereby refining the recovery of the downlink CSI and uplink user data sequences (UL-US). Numerical simulation results demonstrate that the proposed method effectively improves the recovery accuracy of the downlink CSI and UL-US. Additionally, the proposed method exhibits its robustness against parameter variations.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"73 ","pages":"Article 102864"},"PeriodicalIF":2.2,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221024","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":"NOMA communications with co-channel interference energy harvesting: Secrecy analysis","authors":"Toi Le-Thanh ,&nbsp;Cuong Tran-Minh ,&nbsp;Khuong Ho-Van","doi":"10.1016/j.phycom.2025.102858","DOIUrl":"10.1016/j.phycom.2025.102858","url":null,"abstract":"<div><div>The article examines security vulnerabilities in non-orthogonal multiple access (NOMA) communications powered by energy of co-channel interferences (CCIs) stemming from successive interference cancellation (SIC) decoding where any NOMA user can eavesdrop the others. We analyze the secrecy throughput and secrecy outage probability of this communication scheme under different practical factors such as Nakagami-<span><math><mi>m</mi></math></span> fading, imperfect SIC, non-linear energy harvesting. Derived analytical expressions are validated by Monte-Carlo simulations. Notably, under numerous operation settings, the results expose that orthogonal multiple access is more secure than NOMA in wireless networks powered by CCIs.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"73 ","pages":"Article 102858"},"PeriodicalIF":2.2,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221097","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":"Development of pruning methods for a lightweight yet efficient AE-based communication system","authors":"Thi Nhu Thuong Huynh ,&nbsp;Thi-Nga Dao ,&nbsp;Chi Hieu Ta","doi":"10.1016/j.phycom.2025.102844","DOIUrl":"10.1016/j.phycom.2025.102844","url":null,"abstract":"<div><div>Thanks to its superior bit error rate performance, the communication system based on autoencoder (AE) has recently attracted significant attention from both researchers and practitioners. In fact, communication functions, such as modulation and demodulation, are usually embedded in edge devices with constrained computing and memory resources. Consequently, the AE architecture within the communication system should be structurally optimized to reduce memory and computing requirements without sacrificing communication performance. Neural network pruning, a promising approach to complexity reduction, achieves this by removing redundant neurons or connections in neural networks. In this paper, we propose three novel pruning methods, such as weight pruning, neuron pruning, and hybrid pruning combining both, to reduce complexity in the AE-based communication system. In particular, the hybrid pruning method leverages the advantages of both weight and neuron pruning to further optimize the network architecture. Our evaluation results demonstrate the effectiveness of these pruning methods in model optimization, highlighting their potential applications in edge devices for modulation and demodulation tasks. Among the proposed pruning methods, the hybrid algorithm produces the best performance.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"73 ","pages":"Article 102844"},"PeriodicalIF":2.2,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing connectivity for emergency vehicles through UAV trajectory and resource allocation optimization","authors":"S. Fatemeh Bozorgi ,&nbsp;S. Mohammad Razavizadeh ,&nbsp;Mohsen Rezaee","doi":"10.1016/j.phycom.2025.102826","DOIUrl":"10.1016/j.phycom.2025.102826","url":null,"abstract":"<div><div>Effective communication for emergency vehicles – such as ambulances and fire trucks – is essential to support their operations in various traffic and environmental conditions. In this context, this paper investigates a vehicular communication system assisted by an Unmanned Aerial Vehicle (UAV), which adjusts its trajectory and resource allocation according to communication needs. The system classifies vehicles into two groups to address their varying service requirements: emergency vehicles, which require a minimum instantaneous data rate to access critical information timely, and normal vehicles. To support both categories effectively, this paper proposes a joint optimization approach that coordinates UAV trajectory planning and Dynamic Bandwidth Allocation (DBA). The objective is to maximize the minimum average data rate for normal vehicles while ensuring that emergency vehicles maintain an instantaneous rate above a predefined threshold. This approach takes into account some system constraints, including UAV propulsion power consumption, mobility limitations, and backhaul capacity. To tackle the resulting non-convex problem, an iterative optimization method is employed, where the original problem is decomposed into two subproblems: bandwidth allocation and UAV trajectory design. In each iteration, the trajectory subproblem is solved using the Successive Convex Approximation (SCA) method. Numerical results confirm that the proposed solution achieves superior performance in meeting service requirements compared to baseline methods.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"73 ","pages":"Article 102826"},"PeriodicalIF":2.2,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy-efficient reporting model for cooperative spectrum sensing in TDMA-based cognitive radio networks","authors":"Prakash Chauhan,&nbsp;Sanjib K. Deka,&nbsp;Nityananda Sarma","doi":"10.1016/j.phycom.2025.102862","DOIUrl":"10.1016/j.phycom.2025.102862","url":null,"abstract":"<div><div>In cognitive radio networks (CRNs), cooperative spectrum sensing (CSS) incurs a considerable amount of cooperation overhead in terms of reporting energy because of the cooperation activities involved amongst secondary users (SUs). Reduction in energy overhead during reporting plays a vital role in improving the overall energy efficiency of CSS. This paper proposes an energy-efficient reporting model for CSS. While developing the reporting model, we first capture the dynamics of primary user (PU) activities on a channel using a two-state discrete-time Markov chain (TS-DTMC) model. After that, we present a mealy-machine-based reporting model, which enables SUs to reduce energy consumption by selectively reporting local sensing information. The energy overhead during the reporting phase of CSS is formulated by jointly considering prior PU occupancy probability and PU transitional probabilities on the channel. Simulation results demonstrate the proposed model’s performance improvement in reporting energy compared to the conventional reporting model and the exclusive ON/OFF reporting model from the literature.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"73 ","pages":"Article 102862"},"PeriodicalIF":2.2,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221022","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":"Deep convolutional fennec fox and sparse spatial self-nested graph neural networks for secure channel estimation and attack mitigation for MU-MIMO-OFDM","authors":"K. Vijaipriya ,&nbsp;M. Nesasudha ,&nbsp;Prawin Angel Michael","doi":"10.1016/j.phycom.2025.102866","DOIUrl":"10.1016/j.phycom.2025.102866","url":null,"abstract":"<div><div>In general, a Multi-User Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing system (MU-MIMO<img>OFDM) can support a multiplicity of users to access a given base station through the use of multiple antennas and OFDM modulation. Nevertheless, new problems such as power management and delay optimization come into play. 5 G, 6 G, and many upcoming technologies are in the pipeline, and getting the data transmission safe is always important, and Channel Estimation is an essential element in it. Based on the proposed theoretical model of MU-MIMO<img>OFDM, this paper designs a new Sparse Pyramid Fennec fox Fuzzy Framework (SPFFF) to enhance the comprehensive utilization of integration techniques of high-performance methods on channels; attack detection, power distribution, and delay-information optimized scheduling. This presents a Sparse Spatial Self-Nested Graph Neural Network (3SNGN) for channel estimation and attack detection since it deals with complex spatial relations and hierarchical dependencies for robust and accurate solutions. To assign power, a Deep Convolutional Pyramid-Dilated Neural Network (DCPDN) is designed to view multi-scale features and optimize the resource power. Make predictions on such data using two types of neural networks optimized by applying Fennec Fox Optimization (FFO) to improve prediction and computational performance. Newly, authors proposed a Fuzzy-based Alpine Skiing Priority (FASP) scheduling algorithm by integrating fuzzy logic with the Alpine Skiing Optimization algorithm to schedule users based on waiting time to avoid inter-user interference (IUI) and minimize the delay performance. In MATLAB, we have used the proposed method for BER (0.00012), MSE (0.00023), NMSE (0.001), and PSNR (45 dB) in the MU-MIMO<img>OFDM system, and it outperforms the present technique. The proposed approach has also attained higher spectral capacity, a higher fairness index, 17.45% improved energy efficiency, and 12.05% lower processing time than other conventional approaches. Simulation results have proved that the proposed framework remarkably enhances the spectral efficiency while reducing latency, enhancing its robustness against attacks.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"73 ","pages":"Article 102866"},"PeriodicalIF":2.2,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267473","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 survey of domain generalization in AI-enabled semantic communication: Architecture, challenges and future opportunities","authors":"Muhammad Furqan Zia ,&nbsp;Messaoud Ahmed Ouameur ,&nbsp;Miloud Bagaa ,&nbsp;Daniel Massicotte ,&nbsp;Adlen Ksentini","doi":"10.1016/j.phycom.2025.102857","DOIUrl":"10.1016/j.phycom.2025.102857","url":null,"abstract":"<div><div>The growing integration of artificial intelligence (AI) into wireless communication systems is driving a shift toward semantic communication, an emerging paradigm that prioritizes the exchange of meaning over raw data. However, semantic communication systems face major challenges when deployed across diverse and unseen domains due to variations in language, context, and channel conditions. This survey provides a comprehensive overview of Domain Generalization (DG) as a key enabler for improving the robustness and adaptability of AI-enabled semantic communication. We explore the types of domain shifts and review the latest DG techniques applicable to semantic communication. Additionally, the paper discusses architectural considerations and real world applications across varied wireless scenarios. Unlike prior works, this survey brings together DG strategies specifically within the context of semantic communication, identifying open challenges and future research directions such as scalable adaptation, resource efficient deployment, and resilience in dynamic environments. It aims to serve as a timely resource for researchers and practitioners working to develop reliable, generalizable communication systems for next generation networks.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"73 ","pages":"Article 102857"},"PeriodicalIF":2.2,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint subcarrier two-stage compressed sensing with off-grid localization for 6G XL-MIMO systems","authors":"Xinyu Yuan ,&nbsp;Ruoyu Zhang ,&nbsp;Yue Ma ,&nbsp;Jingqi Wang ,&nbsp;Chen Miao","doi":"10.1016/j.phycom.2025.102855","DOIUrl":"10.1016/j.phycom.2025.102855","url":null,"abstract":"<div><div>6G systems aim to support ultra-high data rates, low latency, and centimeter-level positioning to meet the demands of future intelligent networks. As a key enabler, XL-MIMO holds great promise due to its ability to enhance spectral efficiency and spatial resolution. However, in near-field scenarios, conventional far-field assumptions fail due to spherical wavefronts, leading to angular sparsity degradation and parameter coupling, which pose challenges to accurate channel estimation and localization. To tackle these issues, we propose a joint subcarrier two-stage compressed sensing algorithm with off-grid localization for 6G XL-MIMO systems. By exploiting the inherent polar-domain channel characteristics of XL-MIMO, we construct a polar-domain dictionary for initial sparse channel estimation, and then construct a joint subcarriers model. By analyzing the cross-subcarrier phase rotation induced by the delay taps, the proposed method overcomes the limitations of distance sampling sparsity, enabling high-precision parameter estimation of multipath propagation delays. Based on this, a gradient descent-based off-grid optimization algorithm is proposed, jointly optimizing the path gain, angle and distance parameters, significantly reducing the grid error and achieving precise positioning. Simulation results demonstrate that the proposed method significantly outperforms existing algorithms in both channel estimation accuracy and localization performance, achieving sub-meter-level distance precision in near-field scenarios.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"73 ","pages":"Article 102855"},"PeriodicalIF":2.2,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159526","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}