Physical Communication最新文献

{"title":"Dynamic proactive–reactive scheduling for URLLC in 5G: Leveraging XGBoost and network virtualization","authors":"Saloua Hendaoui ,&nbsp;Fatma Hendaoui ,&nbsp;Nawel Zangar","doi":"10.1016/j.phycom.2024.102553","DOIUrl":"10.1016/j.phycom.2024.102553","url":null,"abstract":"<div><div>In this paper, we focus on service coexistence within the 5G network. Specifically, we aim to enhance the Quality of Service (QoS) for Ultra-Reliable Low Latency Communications (URLLC) within 5G networks. We propose a smart scheduler by integrating machine learning models. Real-time channel reports are used to make data-driven decisions based on current network conditions, thereby centralizing scheduling tasks to streamline operations and enhance adaptability.</div><div>The key contributions include the development of an adaptive scheduling strategy that dynamically switches between reactive and proactive approaches, ensuring optimal balance between low latency and ultra-reliability for URLLC services. Additionally, the Extreme Gradient Boosting (XGBoost) algorithm is applied to accurately predict URLLC traffic delays, assuring effective prioritization and timely delivery of time-sensitive services. Furthermore, the proposed solution combines network virtualization and machine learning techniques to address security risks and potential service interruptions linked to reactive scheduling policies. The proposed approach enhances existing methods by improving network virtualization features, which are integrated with machine learning models, specifically XGBoost to optimize the scheduling of ultra-reliable low-latency communication (URLLC). Unlike previous work, our proposal uses network virtualization to enable a dynamic combination of proactive and reactive scheduling strategies that can be adapted in real-time. Network virtualization features provide centralized control and resource management, resulting in reduced complexity and more efficient resource allocation. This helps the scheduler react quickly to changing network conditions, ensuring low latency and high reliability of URLLC services. In addition, network virtualization enhances the security level by isolating different types of traffic and mitigating risks such as Denial of Service (DoS) and impersonation attacks. Subsequently, the integration of advanced network virtualization functions with machine-learning predictions not only optimizes planning but also enhances security and resource management.</div><div>The proposed system is evaluated through simulations, demonstrating robust performance with low latency and high reliability for URLLC services. The XGBoost model achieves impressive results with low Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) on both validation and holdout datasets.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"68 ","pages":"Article 102553"},"PeriodicalIF":2.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158129","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 based on dual frequency domain Transformer in time–frequency doubly-selective fading underwater acoustic channels","authors":"Xuerong Cui ,&nbsp;Chuang Zhang ,&nbsp;Juan Li ,&nbsp;Bin Jiang ,&nbsp;Shibao Li ,&nbsp;Jianhang Liu","doi":"10.1016/j.phycom.2024.102585","DOIUrl":"10.1016/j.phycom.2024.102585","url":null,"abstract":"<div><div>In underwater acoustic (UWA) channels with time–frequency doubly-selective fading, the performance of traditional channel estimation algorithms is seriously degraded. To solve this problem, this paper proposes a dual frequency-domain Transformer Channel Estimation (DFTCE) based model for UWA-orthogonal frequency division multiplexing (UWA-OFDM) systems. The model uses two parallel CNNs to extract UWA channel features from the channel response at the pilot and the frequency-domain received signal, respectively, and then inputs the channel features into the Transformer for channel estimation. In the extraction of channel features from the channel response at the pilot, this study comprehensively accounts for the influence of both channel features and noise. Utilizing the carrier frequency interval of the pilot, a high-frequency feature extraction module is devised to extract high-frequency channel features while eliminating low-frequency noise components. In the extraction of channel features from the frequency-domain received signal, a global feature extraction module is developed, considering distinct subcarrier frequency ranges to capture nuanced features of the overall channel variations at different time instances. Furthermore, a multi-head attention mechanism is utilized to concentrate on variations among subcarriers. This helps alleviate the influence of channel noise and Inter-Carrier Interference (ICI), consequently enhancing the performance of channel estimation. Simulation experiments conducted using the UWA channel dataset WATERMARK reveal that the proposed method demonstrates a performance improvement of 2 dB-3 dB compared to the linear minimum mean square error (LMMSE) algorithm in an UWA environment with significant Doppler effect.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"68 ","pages":"Article 102585"},"PeriodicalIF":2.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158130","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 contextual bandits-based energy-efficient beamforming for integrated sensing and communication","authors":"Mohammad Amin Choukali,&nbsp;Sedighe Mirbolouk,&nbsp;Morteza Valizadeh,&nbsp;Mehdi Chehel Amirani","doi":"10.1016/j.phycom.2024.102576","DOIUrl":"10.1016/j.phycom.2024.102576","url":null,"abstract":"<div><div>In this paper, a transmit beamforming design is considered to jointly improve the communication and sensing functionalities in an integrated sensing and communication (ISAC) system, where a dual-functional base station (BS) serves a set of communication users (UEs) while sensing some potential targets (TGs). To this end, a multi-objective beamforming optimization problem is formulated to maximize the weighted linear combination of the communication energy efficiency (EE) and sensing beampattern gains. Using the normalized weighted sum method, the multi-objective problem is relaxed to a single-objective problem. For dealing with the non-convexity nature of the considered problem and the continuity of the beamforming space, a novel deep contextual bandits (DCB) scheme inspired by the soft actor–critic (S-DCB) method is proposed in which the channel state information of UEs is used as the context. Both the reward and policy functions are approximated to solve the problem. Simulation results indicate that the multi-objective function enables an adjustable trade-off between the communication and sensing functionalities. Besides, the effectiveness of the proposed S-DCB algorithm is verified through a comprehensive comparison with the state-of-the-art DCB-DDPG and the upper bound benchmarks.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"68 ","pages":"Article 102576"},"PeriodicalIF":2.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158140","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 Monte-Carlo based construction of polarization-adjusted convolutional (PAC) codes","authors":"Mohsen Moradi,&nbsp;Amir Mozammel","doi":"10.1016/j.phycom.2024.102578","DOIUrl":"10.1016/j.phycom.2024.102578","url":null,"abstract":"<div><div>This paper proposes a rate-profile construction method for polarization-adjusted convolutional (PAC) codes of any code length and rate, which is capable of preserving the trade-off between the error-correction performance and decoding complexity of PAC codes. The proposed method can improve the error-correction performance of PAC codes while guaranteeing a low mean sequential decoding complexity for signal-to-noise ratio (SNR) values beyond a target SNR value.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"68 ","pages":"Article 102578"},"PeriodicalIF":2.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158142","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":"Cognitive UAV-IRS planning for semantic-aware mobile edge computing networks","authors":"Xuefeng Chen,&nbsp;Rui Ma","doi":"10.1016/j.phycom.2024.102589","DOIUrl":"10.1016/j.phycom.2024.102589","url":null,"abstract":"<div><div>Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks offer a powerful solution for enhancing communication efficiency in resource-constrained environments and managing compute-intensive tasks. However, the inherent limitations of UAVs, such as restricted data storage, computation capability and battery capacity, hinder the maximum communication efficiency. For the first time, this paper investigates a semantic-aware mobile edge computing (SMEC) network, where task data is semantically compressed at the users and processed at edge computing servers. This approach aims to significantly reduce the transmission and storage overhead in UAV, and improve task performance in low signal-to-noise ratio (SNR). To further enhance transmission robustness and task performance, we incorporate a UAV-carried mobile intelligent reflecting surface (IRS). The objective is to minimize system costs while maintaining task performance, which requires the joint optimization of UAV trajectories, server pairings, user assignments, and IRS reflecting elements. This problem is NP-hard, posing significant computational challenges. To address the complexity of the formulated problem, we propose a novel cognitive UAV-IRS planning strategy based on deep reinforcement learning (DRL), where the UAV can infer the task intentions of the users. Simulation results demonstrate the effectiveness of our intelligent scheme, showing rapid convergence in solving the complex optimization problem. Comparative analysis with benchmark schemes reveals a substantial reduction in system costs and more robust task performance achieved by our proposed approach.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"68 ","pages":"Article 102589"},"PeriodicalIF":2.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143157049","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":"Noise removal techniques for underground communication systems based on matching pursuit","authors":"Yong Wang ,&nbsp;Bangwei Yu ,&nbsp;Ying Wang ,&nbsp;Liangang Qi ,&nbsp;Yang Liu","doi":"10.1016/j.phycom.2024.102579","DOIUrl":"10.1016/j.phycom.2024.102579","url":null,"abstract":"<div><div>In underground communication systems, continuous mud pulse signals are susceptible to pump noise during transmission, resulting in a high bit error rate (BER). In this paper, a Paradigm Inner Product Orthogonal Matching Pursuit (PIPOMP) algorithm is proposed for the transmission characteristics of continuous waves in the underground. First, the observation vectors of pump noise are obtained by signal cyclic prefix (CP) differencing, and the resulting observation vectors are more accurate than the traditional methods. Second, the columns of the sensing matrix that are most relevant to the observation vectors are selected as candidate support sets by computing the L2 paradigm. Then, the least squares method was used to solve for the estimated value of the pump noise at the previous moment. Finally, the pump noise is reconstructed by combining the correspondence between the time and frequency domains. This paper establishes a complete underground communication system. We simulate the denoising performance of pump noise under stable and unstable conditions and analyze the denoising performance of the PIPOMP algorithm in depth. Simulation results show that the algorithm significantly improves the interference immunity performance and reduces the system BER in the environment where pump noise interferes and the fading is more drastic.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"68 ","pages":"Article 102579"},"PeriodicalIF":2.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143157538","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":"Beamforming design via machine learning in intelligent reflecting surface-aided wireless communication","authors":"Asma Ahmadinejad,&nbsp;Siamak Talebi","doi":"10.1016/j.phycom.2024.102586","DOIUrl":"10.1016/j.phycom.2024.102586","url":null,"abstract":"<div><div>Beamforming design is a pivotal issue in intelligent reflecting surface (IRS) assisted wireless communication. The capacity of the classic regular IRS-based schemes with a few numbers of elements is not convincing. In order to deal with this issue and gain spatial degrees of freedom, we offer an irregular IRS architecture and investigate a weighted sum rate (WSR) maximization problem so as to enhance the system capacity. WSR maximization subject to the transmit power is a nonconvex problem and confronting with this issue is arduous. Despite some existing approaches exhibit proper results, several defects such as computational complexity, acquiring local optimal solutions and so on are still controversial. In this paper, unlike these conventional techniques, a machine learning (ML) inspired beamforming design is presented. In the offered method, the goal is to employ a deep learning (DL) model which, via utilizing only omni or quasi-omni beam patterns, learns how to predict the precoding vectors. In order to improve the support of this system, instead of hiring position information, uplink received signal are used for beamforming prediction. In addition, a joint optimization method was considered in order to iteratively handle the optimization problem. Moreover, other fruitful advantages such as negligible training overhead and no need for training before deployment are attained. Simulation results, based on accurate ray tracing, affirm that the offered method access premiere performance compared with conventional beamforming approaches.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"68 ","pages":"Article 102586"},"PeriodicalIF":2.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158138","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":"Polar-coded perturbation sphere decoding algorithm over Rayleigh fading channel","authors":"Haiqiang Chen ,&nbsp;Yan Chen ,&nbsp;Yuanbo Liu ,&nbsp;Rui Wang ,&nbsp;Xiangcheng Li ,&nbsp;Youming Sun ,&nbsp;Qingnian Li","doi":"10.1016/j.phycom.2024.102566","DOIUrl":"10.1016/j.phycom.2024.102566","url":null,"abstract":"<div><div>A perturbation sphere decoding algorithm for control channels with small payload size is proposed in this paper. When a codeword fails the cyclic redundancy check (CRC), the algorithm performs a perturbation operation by adding noise to the codeword and achieves a new received sequence. Subsequently, this perturbed sequence is sent to the decoder for another decoding attempt. Moreover, a partial perturbation algorithm is proposed to further reduce the resource consumption, which only applies perturbation for those rows having relative low weights, resulting in improved performance and reduced complexity. Simulation results show that, the two proposed algorithms exhibit excellent performance over Rayleigh fading channel and the performance gain increases with the maximum perturbation number. For (64,22) polar codes, the two algorithms can achieve about 0.58 dB and 0.71 dB performance gain, respectively, with maximum perturbation number = 4 and FER = <span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup></mrow></math></span>, compared to the conventional sphere decoding algorithm. Meanwhile, the complexity of partial perturbation algorithm is reduced to about 77.66% compared to the perturbation algorithm at the SNR = 5 dB.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"68 ","pages":"Article 102566"},"PeriodicalIF":2.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143157539","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 joint source-channel coding empowered two-way relay networks for wireless image transmission","authors":"Xingyu Wang,&nbsp;Xiangdong Zheng,&nbsp;Lianhong Zhang,&nbsp;Chao Li","doi":"10.1016/j.phycom.2024.102568","DOIUrl":"10.1016/j.phycom.2024.102568","url":null,"abstract":"<div><div>Compared with the traditional uni-directional relaying, two-way relay networks provide important enhancements and optimizations to modern communication systems. However, with the increasing requirements of artificial intelligence applications for image data transmission, relay-assisted communication technologies are reaching the theoretical limit in terms of bandwidth, which hinders the further development of AI applications. To address this issue, we propose a deep joint source-channel coding empowered two-way relay network (DeepJSCC-TWRN) to help image transmission. Specifically, in the DeepJSCC-TWRN, a DeepJSCC is employed to improve image transmission quality of the TWRN from the perspective of visual semantic information, and each source can achieve optimal performance by being trained in a uniform deep learning framework. For measuring the performance of the proposed DeepJSCC-TWRN, we employ the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) as performance metrics. Simulation results show that DeepJSCC-TWRN outperforms the baseline method, demonstrating the ability to preserve visual semantic information.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"68 ","pages":"Article 102568"},"PeriodicalIF":2.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158110","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 optimization trajectory and resource allocation for UAV-assisted semantic communications","authors":"Xiangyang Xu ,&nbsp;Chunlong He ,&nbsp;Xingquan Li ,&nbsp;Jiaming Xu","doi":"10.1016/j.phycom.2024.102555","DOIUrl":"10.1016/j.phycom.2024.102555","url":null,"abstract":"<div><div>Semantic communication can effectively save bandwidth, and enhance communication capabilities by transmitting semantic features. However, semantic communication has certain limitations, such as limited application scenarios and inflexible deployment. To this end, we investigate an unmanned aerial vehicle (UAV)-assisted semantic communication system in this paper. An UAV serves as a mobile base station to service users in designated area. Each user has different requirements for transmission delay and performance, and the UAV has a limited maximum flight time. We need to achieve the communication goals of all users in the shortest possible time, that is, to ensure that the information received by each user meets latency and quality requirements. This is a non-convex optimization problem, which is very complicated to solve using traditional methods. In order to solve this problem, we propose a deep reinforcement learning algorithm based on Proximal Policy Optimization 2. The simulation results confirm the effectiveness of our proposed algorithm.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"68 ","pages":"Article 102555"},"PeriodicalIF":2.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158139","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}