Physical Communication最新文献

{"title":"High-accurate range acquisition for LFMCW radar with optimized maximum likelihood estimation towards Internet of Everything","authors":"Kun Zhang,&nbsp;Ming Li,&nbsp;Bo Zhang,&nbsp;Ping Chu,&nbsp;Guowei Che","doi":"10.1016/j.phycom.2025.102646","DOIUrl":"10.1016/j.phycom.2025.102646","url":null,"abstract":"<div><div>Linear frequency modulated continuous wave (LFMCW) radar, as an emerging key sensor, facilitates the realization of Internet of Everything (IoE) covering a tremendous range of areas such as healthcare, smart homes, and industrial automation. Among the applications towards IoE, the priority is high-accurate range acquisition of LFMCW radar. For the LFMCW radar, the beat frequency estimation directly determines the ranging accuracy. In this paper, we study the high-accurate beat frequency estimation using the optimized Maximum Likelihood Approach (MLA). The Nelder–Mead algorithm is employed during the optimization process. The numerical results reveal that the estimated beat frequency severely deviates from the theoretical value across various signal-to-noise ratio (SNR) levels without optimization. In contrast, our proposed method enables extremely accurate estimation of the beat frequency, with relative errors less than 1.6 Hz compared to the theoretical value. Accordingly, a high-accurate range with relative errors below 4.4 mm can be gained. Furthermore, it is concluded that both the estimated beat frequencies and ranges closely approach the Cramér–Rao bound across various SNR levels, which means that the optimized MLA has formidable noise immunity in practical applications, enabling highly accurate and robust range acquisition.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"70 ","pages":"Article 102646"},"PeriodicalIF":2.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534834","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":"Autoencoder-based physical layer authentication in a real indoor environment","authors":"Linda Senigagliesi,&nbsp;Gianluca Ciattaglia,&nbsp;Ennio Gambi","doi":"10.1016/j.phycom.2025.102626","DOIUrl":"10.1016/j.phycom.2025.102626","url":null,"abstract":"<div><div>Authentication of wireless nodes, as in fifth-generation (5G) and Internet of Things (IoT) networks, is an increasingly pressing issue, in order to limit the required computational effort and the necessary overhead. A simplification of the authentication process may therefore be of interest to achieve the satisfaction of stringent performance requirements, such as those envisaged for sixth-generation (6G) networks. This paper provides a study on the feasibility of physical layer authentication (PLA) in a real indoor environment, as an alternative solution to the traditional authentication schemes. To ensure the reliability of the proposed approach a simulated scenario is firstly tested. Subsequently, real-world data are collected through a laboratory setup using a Vectorial Signal Transceiver (VST) and two Universal Software Radio Peripherals (USRPs) to emulate the behavior of the receiver, the legitimate transmitter, and the potential adversary. A machine learning (ML) algorithm is then exploited to act as authenticator. This means that channel fingerprint is extracted from signals to create a dataset used to train a sparse autoencoder. To emulate a real authentication scenario, the autoencoder is trained only on the class of the legitimate user. Once a new message arrives, the autoencoder task is to discern authentic signals from those forged by the adversary. It is shown that a geometric mean of accuracy of more than 90%, with corresponding low levels of false alarm and missed detection, is achievable irrespective of the nodes location, underlining the robustness and versatility of the proposed ML-based PLA approach.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"70 ","pages":"Article 102626"},"PeriodicalIF":2.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the performance of NOMA-based two-way relay network","authors":"Abolqasem Hesam,&nbsp;Ali H. Bastami","doi":"10.1016/j.phycom.2025.102633","DOIUrl":"10.1016/j.phycom.2025.102633","url":null,"abstract":"<div><div>Non-orthogonal multiple access (NOMA) technique enhances the performance of the two-way relay network (TWRN) by using the rate-splitting strategy, i.e., by transmitting the low-power and high-power symbols simultaneously. Although the rate-splitting improves the spectral efficiency, the successive interference cancellation (SIC) receiver can cause the rate of the high-power symbol to decrease. In this paper, we propose a NOMA-based transmission scheme for a three-time-slot (3T) TWRN which improves the achievable sum-rate by (i) mitigating interference at the SIC receiver of each transceiver before decoding the high-power symbol, and (ii) decoding the low-power symbol based on the maximum-ratio combining (MRC) of the direct and relayed signals. We analyze the proposed scheme in terms of the achievable rate and its asymptotic behavior, and derive a highly accurate upper bound for the ergodic sum-rate. Additionally, we analytically demonstrate that the proposed scheme achieves the maximum rate of the 3T-TWRN. Simulation results validate our theoretical findings and show that the proposed scheme outperforms the conventional NOMA and orthogonal multiple access (OMA) schemes in TWRN.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"70 ","pages":"Article 102633"},"PeriodicalIF":2.0,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548428","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 capacity and energy efficiency in UAV full-duplex systems through transmit power control","authors":"Minh Tran ,&nbsp;Ba Cao Nguyen ,&nbsp;Taejoon Kim ,&nbsp;Bui Vu Minh ,&nbsp;Duc Thinh Vu ,&nbsp;Bui Trong Hoang ,&nbsp;Van Duan Nguyen","doi":"10.1016/j.phycom.2025.102636","DOIUrl":"10.1016/j.phycom.2025.102636","url":null,"abstract":"<div><div>This article proposes to utilize full-duplex (FD) communication at an unmanned aerial vehicle (UAV) device to improve the quality of service of the wireless communication systems. We analyze the considered UAV-FD systems’ performance by deriving the closed-form expressions of ergodic capacities (ECs) and energy efficiencies (EEs) in two scenarios, <em>e.g</em>., without and with source–destination (S–D) channel over a Nakagami-<span><math><mi>m</mi></math></span> distribution. Since the joint effect of high altitude and residual self-interference (RSI) at UAV-FD significantly affects the ECs and EEs of the proposed systems, we propose an algorithm for transmit power optimization at the UAV-FD. Numerical results indicate that the EC and EE with the S–D channel are dramatically higher than the EC and EE without the S–D channel. Moreover, the EC without the S–D channel reaches a saturated ceiling when the transmit power is high enough. To deal with this issue, transmitting power optimization is an effective method. More specifically, the impacts of RSI and negative parameters are greatly reduced with the optimal transmit power. In addition, the optimal transmit power is greatly lower than the conventional transmit power. Thus, the power consumption of the considered UAV-FD systems is dramatically reduced. Importantly, the ECs and EEs with the optimal transmit power are considerably higher than the ECs and EEs without that value. Besides the RSI, the positions of UAV-FD have a strong effect on the ECs and EEs of the UAV-FD systems. Thus, the UAV-FD should fly in appropriate positions to increase the ECs and EEs. On the other hand, we thoroughly examine the impact of key parameters such as frequency, bandwidth, RSI, transmit power, transmission rate, and the horizontal and vertical positions of UAV-FD to gain valuable insights into the behavior of UAV-FD systems. Based on these insights, we propose several recommendations to enhance the ECs and EEs of UAV-FD systems. Finally, Monte-Carlo simulations are conducted to validate the accuracy of the mathematical expressions.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"70 ","pages":"Article 102636"},"PeriodicalIF":2.0,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511484","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 learning-based efficient MIMO detection method","authors":"Burera,&nbsp;Saleem Ahmed,&nbsp;Sooyoung Kim","doi":"10.1016/j.phycom.2025.102637","DOIUrl":"10.1016/j.phycom.2025.102637","url":null,"abstract":"<div><div>Signal detection for multiple-input-multiple-output (MIMO) systems is a challenging problem due to its computational complexity. The conventional algorithms used in this problem often are either impractical or suffer from performance limitations. In this paper, we propose a machine learning-based MIMO detection method. The proposed method employs the encoder block of a transformer learning approach that has been tailored for MIMO detection. The input to the network of the proposed method is prepossessed using a simple linear decomposition method. Simulation results show that the proposed method achieves a significant enhancement in bit error rate (BER) performance and ultimately produces performance approaching that of the maximum likelihood (ML) detection method.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"70 ","pages":"Article 102637"},"PeriodicalIF":2.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509020","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 of energy consumption and latency for task offloading in cloud–edge collaboration system","authors":"Xue Jiang ,&nbsp;Haie Dou ,&nbsp;Lei Wang ,&nbsp;Zhijie Xia","doi":"10.1016/j.phycom.2025.102635","DOIUrl":"10.1016/j.phycom.2025.102635","url":null,"abstract":"<div><div>In a cloud–edge collaboration system, ensuring consistent wireless connectivity that meets the quality of service (QoS) for users with varying requirements is challenging. Therefore, the approach to task offloading for all mobile devices (MDs) should be carefully designed to enhance performance based on different requirements. This paper introduces a novel multi-objective genetic algorithm (MOGA) to address the task offloading problem, considering minimal energy consumption and latency simultaneously. Experimental results demonstrate that the proposed multi-objective genetic algorithm achieves rapid convergence and optimizes delay and energy consumption.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"70 ","pages":"Article 102635"},"PeriodicalIF":2.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520375","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":"Enhanced physical layer security for 5G and beyond communication: A hamming window inspired randomized frequency diverse array-based directional modulation scheme with artificial noise","authors":"Adeel Jadoon ,&nbsp;Abdul Basit ,&nbsp;Wasim Khan ,&nbsp;Umair Hafeez Khan ,&nbsp;Mohsin Khan ,&nbsp;Athar Waseem ,&nbsp;Zahid Ullah","doi":"10.1016/j.phycom.2025.102627","DOIUrl":"10.1016/j.phycom.2025.102627","url":null,"abstract":"<div><div>In this paper, we propose a Hamming window inspired random frequency diverse array based directional modulation with added artificial noise (RHFDA-DM-AN) scheme to enhance physical layer security for 5G and beyond wireless communication. By employing randomized Hamming window based frequency offsets for the transmit array of FDA, we achieve a significant improvement in range-angle uncoupled security through the generation of a pencil beam, surpassing the range-angle coupled security and angle only security offered by an FDA based DM and Phased Array (PA) based DM, respectively. Firstly, the frequency offsets are randomized considering the inherent thermal noise. Next, we derived an expression for the lower bound of the Ergodic Secrecy Capacity (ESC) in closed form for the proposed RHFDA-DM-AN scheme. Finally, the simulation results demonstrate that the proposed RHFDA-DM-AN scheme achieves improved performance and higher secrecy capacity compared to state of the art PA, FDA, and existing RFDA-based DM-AN schemes.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"70 ","pages":"Article 102627"},"PeriodicalIF":2.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Serverless federated learning: Decentralized spectrum sensing in heterogeneous networks","authors":"Ferhat Ozgur Catak ,&nbsp;Murat Kuzlu ,&nbsp;Yaser Dalveren ,&nbsp;Gokcen Ozdemir","doi":"10.1016/j.phycom.2025.102634","DOIUrl":"10.1016/j.phycom.2025.102634","url":null,"abstract":"<div><div>Federated learning (FL) has gained more popularity due to the increasing demand for robust and efficient mechanisms to ensure data privacy and security during collaborative model training in the concept of artificial intelligence/machine learning (AI/ML). This study proposes an advanced version of FL without the central server, called a serverless or decentralized federated learning framework, to address the challenge of cooperative spectrum sensing in non-independent and identically distributed (non-IID) environments. The framework leverages local model aggregation at neighboring nodes to improve robustness, privacy, and generalizability. The system incorporates weighted aggregation based on distributional similarity between local datasets using Wasserstein distance. The results demonstrate that the proposed serverless federated learning framework offers a satisfactory performance in terms of accuracy and resilience.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"70 ","pages":"Article 102634"},"PeriodicalIF":2.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143478939","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":"Dual-mode Index Modulation based on Affine Frequency Division Multiplexing","authors":"Anoop A. ,&nbsp;Christo Kurisummoottil Thomas ,&nbsp;Kala S. ,&nbsp;J.V. Bibal Benifa ,&nbsp;Walid Saad","doi":"10.1016/j.phycom.2025.102628","DOIUrl":"10.1016/j.phycom.2025.102628","url":null,"abstract":"<div><div>Affine Frequency Division Multiplexing (AFDM) has been recognized for its superior bit error rate (BER) performance compared to Orthogonal Frequency Division Multiplexing (OFDM), especially under conditions of high mobility. This paper introduces a novel transmission method termed AFDM with Dual-Mode Index Modulation (AFDM-DM-IM), aiming to achieve an optimal balance between transmission reliability and spectral efficiency. To facilitate effective demodulation, two baseline detectors based on minimum mean squared error (MMSE) maximum likelihood (MMSE-ML) and MMSE log-likelihood ratio (MMSE-LLR) algorithms are developed. Furthermore, a deep learning-based demodulator (DeepADM) is proposed to account for the channel estimation error compared to baseline detectors such as MMSE-ML and MMSE-LLR. A theoretical analysis of the BER performance of AFDM-DM-IM is provided, along with simulation results. These results demonstrate superior performance of AFDM-DM-IM over both traditional AFDM and other existing index modulation system based on AFDM (AFDM-IM). This novel approach provides a promising pathway for the development of resilient and efficient communication systems in future high-mobility scenarios.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"70 ","pages":"Article 102628"},"PeriodicalIF":2.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143478938","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 vehicle trajectory prediction for V2V communication using a hybrid RNN approach","authors":"Rathnakannan Kailasam,&nbsp;Vinitha Jaini Xavier Arul Raj,&nbsp;Palani Rajan Balasubramanian","doi":"10.1016/j.phycom.2025.102623","DOIUrl":"10.1016/j.phycom.2025.102623","url":null,"abstract":"<div><div>This study presents a hybrid recurrent neural network (RNN) for predicting vehicle trajectories in terms of latitude and longitude positions, enabling V2 V communication. The hybrid network, which integrates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells, is trained on the NGSIM dataset to address the regression problem of forecasting future vehicle positions. The proposed model achieves a root mean square error (RMSE) of &lt;0.003, demonstrating a 33 % improvement compared to a network composed solely of LSTM cells. Furthermore, evaluation against recent approaches highlights the effectiveness of the proposed method in predicting vehicle trajectories. The impact of different dropout types and probabilities is also analyzed, with an input dropout probability of 0.6 delivering performance comparable to that of the model without dropout. These results indicate that the hybrid RNN effectively predicts future vehicle trajectories, laying a foundation for enhanced V2 V communication and contributing to advancements in autonomous vehicular systems.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"71 ","pages":"Article 102623"},"PeriodicalIF":2.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143576858","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}