Pub Date : 2025-02-01DOI: 10.23919/JCN.2025.000003
Lili Guo;Shibing Zhang;Xuan Zhu;Xiaodong Ji
This paper investigates optimal trajectory design for an unmanned aerial vehicle (UAV) enabled two-way relaying, where a fixed-wing UAV employs an amplify-and-forward protocol to assist data exchange between two ground users. With the aim of maximizing the system energy efficiency (EE), an optimization problem corresponding to the UAV's trajectory design is formulated, where the UAV's initial/final speed and location constraints in addition to the acceleration constraint of the UAV are considered. The initial optimization problem is intractable due to its non-concave objective function and nonconvex constraints. To this end, slack variables are introduced, and then the successive convex approximation (SCA) method and the Dinkelbach's algorithm are applied to transform it into a convex optimization problem, which is solved by a proposed iterative algorithm. Simulation results show that the proposed iterative algorithm converges quite quickly, and with the trajectory design, the two-way UAV relaying is much more superior than the compared benchmark schemes in terms of EE.
{"title":"Energy efficient trajectory design for fixed-wing UAV enabled two-way amplify-and-forward relaying","authors":"Lili Guo;Shibing Zhang;Xuan Zhu;Xiaodong Ji","doi":"10.23919/JCN.2025.000003","DOIUrl":"https://doi.org/10.23919/JCN.2025.000003","url":null,"abstract":"This paper investigates optimal trajectory design for an unmanned aerial vehicle (UAV) enabled two-way relaying, where a fixed-wing UAV employs an amplify-and-forward protocol to assist data exchange between two ground users. With the aim of maximizing the system energy efficiency (EE), an optimization problem corresponding to the UAV's trajectory design is formulated, where the UAV's initial/final speed and location constraints in addition to the acceleration constraint of the UAV are considered. The initial optimization problem is intractable due to its non-concave objective function and nonconvex constraints. To this end, slack variables are introduced, and then the successive convex approximation (SCA) method and the Dinkelbach's algorithm are applied to transform it into a convex optimization problem, which is solved by a proposed iterative algorithm. Simulation results show that the proposed iterative algorithm converges quite quickly, and with the trajectory design, the two-way UAV relaying is much more superior than the compared benchmark schemes in terms of EE.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 1","pages":"32-39"},"PeriodicalIF":2.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10923674","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
Pub Date : 2025-02-01DOI: 10.23919/JCN.2024.000071
Jihyeon Min;Youngil Park
Automotive Ethernet has emerged as a communication protocol to meet the escalating demands in applications such as advanced driver assistance systems (ADAS), autonomous driving, and connected vehicles. This technology surpasses the capabilities of the traditional controller area network (CAN) by providing higher bandwidth and supporting bidirectional communication, which is essential for real-time applications and the easy expansion of networks through Ethernet switches. Furthermore, automotive Ethernet guarantees packet transmission within the bounded latency, achieving low packet latency and jitter, and low packet loss through time-sensitive networking (TSn), a set of standards within the IEEE 802 network, which ensures the operation of time-critical applications. TSN also helps for automotive Ethernet, which requires reliability to deliver messages from time-critical applications without errors or loss. Among TSN standards, frame replication and elimination for reliability (FRER) stands out as a technology that provides redundancy. Although FRER is useful in detecting data loss, it consumes significant bandwidth and works only in a mesh topology. Therefore, it is difficult to be used in the bus type automotive Ethernet such as 10BASE-T1S. The bus topology presents a problem wherein if a segment of the cable becomes damaged, communication throughout the entire network becomes impossible. In this paper, we propose a fault-tolerant redundancy protocol for within the 10BASE-T1S bus topology. Our method involves adapting the physical layer collision avoidance (PLCA) protocol, originally utilized in the 10BASE-T1S bus topology, for operation in a ring-type connection. This approach offers the advantage of maintaining compatibility with the existing PLCA protocol in use. We verify the effectiveness of our proposed method through simulations and hardware emulation, confirming its ability to restore network functionality in the event of hard faults, such as cable disconnections and node failures.
{"title":"Fault recovery of 10BASE-T1S automotive ethernet with bus/ring hybrid topology","authors":"Jihyeon Min;Youngil Park","doi":"10.23919/JCN.2024.000071","DOIUrl":"https://doi.org/10.23919/JCN.2024.000071","url":null,"abstract":"Automotive Ethernet has emerged as a communication protocol to meet the escalating demands in applications such as advanced driver assistance systems (ADAS), autonomous driving, and connected vehicles. This technology surpasses the capabilities of the traditional controller area network (CAN) by providing higher bandwidth and supporting bidirectional communication, which is essential for real-time applications and the easy expansion of networks through Ethernet switches. Furthermore, automotive Ethernet guarantees packet transmission within the bounded latency, achieving low packet latency and jitter, and low packet loss through time-sensitive networking (TSn), a set of standards within the IEEE 802 network, which ensures the operation of time-critical applications. TSN also helps for automotive Ethernet, which requires reliability to deliver messages from time-critical applications without errors or loss. Among TSN standards, frame replication and elimination for reliability (FRER) stands out as a technology that provides redundancy. Although FRER is useful in detecting data loss, it consumes significant bandwidth and works only in a mesh topology. Therefore, it is difficult to be used in the bus type automotive Ethernet such as 10BASE-T1S. The bus topology presents a problem wherein if a segment of the cable becomes damaged, communication throughout the entire network becomes impossible. In this paper, we propose a fault-tolerant redundancy protocol for within the 10BASE-T1S bus topology. Our method involves adapting the physical layer collision avoidance (PLCA) protocol, originally utilized in the 10BASE-T1S bus topology, for operation in a ring-type connection. This approach offers the advantage of maintaining compatibility with the existing PLCA protocol in use. We verify the effectiveness of our proposed method through simulations and hardware emulation, confirming its ability to restore network functionality in the event of hard faults, such as cable disconnections and node failures.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 1","pages":"1-9"},"PeriodicalIF":2.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10923679","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
Pub Date : 2025-02-01DOI: 10.23919/JCN.2025.000001
Hyunsoo Lee;Soyi Jung;Soohyun Park
With the rapid development of autonomous mobility technologies, drones are now widely used in many applications, including military domain. Particularly in battlefield conditions, designing a deep reinforcement learning (DRL)-based autonomous control algorithm presents significant challenges due to the need for real-time and adjustable nonlinear trajectory planning. Therefore, this paper introduces a novel situation-aware DRL-based autonomous nonlinear drone mobility control algorithm tailored for cyber-physical loitering munition applications. The proposed DRL-based drone mobility control algorithm is crafted with a focus on real-time situation-aware operations, enabling it to navigate through many obstacles encountered on the battlefield efficiently. For efficient observation and intuitive fast understanding of time-varying real-time situations, this paper presents an algorithm that works on a cyber-physical virtual battlefield environment using Unity. In detail, our proposed DRL-based nonlinear drone mobility control algorithm utilizes situation-aware sensing components that are implemented with a Raycast function in Unity virtual scenarios. Based on the gathered situation-aware information, the drone can autonomously and nonlinearly adjust its trajectory during flight. Thus, this approach is obviously beneficial for avoiding obstacles in complex and unpredictable battlefields. Our visualization- based performance evaluation shows that the proposed algorithm outperforms other mobility control algorithms, with an average performance nearly twice as high when the obstacle density is 50%. This superiority is further evidenced by the detailed trajectory planning presented.
{"title":"Situation-aware deep reinforcement learning for autonomous nonlinear mobility control in cyber-physical loitering munition systems","authors":"Hyunsoo Lee;Soyi Jung;Soohyun Park","doi":"10.23919/JCN.2025.000001","DOIUrl":"https://doi.org/10.23919/JCN.2025.000001","url":null,"abstract":"With the rapid development of autonomous mobility technologies, drones are now widely used in many applications, including military domain. Particularly in battlefield conditions, designing a deep reinforcement learning (DRL)-based autonomous control algorithm presents significant challenges due to the need for real-time and adjustable nonlinear trajectory planning. Therefore, this paper introduces a novel situation-aware DRL-based autonomous nonlinear drone mobility control algorithm tailored for cyber-physical loitering munition applications. The proposed DRL-based drone mobility control algorithm is crafted with a focus on real-time situation-aware operations, enabling it to navigate through many obstacles encountered on the battlefield efficiently. For efficient observation and intuitive fast understanding of time-varying real-time situations, this paper presents an algorithm that works on a cyber-physical virtual battlefield environment using Unity. In detail, our proposed DRL-based nonlinear drone mobility control algorithm utilizes situation-aware sensing components that are implemented with a Raycast function in Unity virtual scenarios. Based on the gathered situation-aware information, the drone can autonomously and nonlinearly adjust its trajectory during flight. Thus, this approach is obviously beneficial for avoiding obstacles in complex and unpredictable battlefields. Our visualization- based performance evaluation shows that the proposed algorithm outperforms other mobility control algorithms, with an average performance nearly twice as high when the obstacle density is 50%. This superiority is further evidenced by the detailed trajectory planning presented.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 1","pages":"10-22"},"PeriodicalIF":2.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10923673","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
Pub Date : 2025-02-01DOI: 10.23919/JCN.2025.000011
{"title":"Open access publishing agreement","authors":"","doi":"10.23919/JCN.2025.000011","DOIUrl":"https://doi.org/10.23919/JCN.2025.000011","url":null,"abstract":"","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 1","pages":"56-58"},"PeriodicalIF":2.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10923681","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
Pub Date : 2025-02-01DOI: 10.23919/JCN.2025.000009
{"title":"Information for authors","authors":"","doi":"10.23919/JCN.2025.000009","DOIUrl":"https://doi.org/10.23919/JCN.2025.000009","url":null,"abstract":"","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 1","pages":"51-55"},"PeriodicalIF":2.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10923683","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
Pub Date : 2024-12-01DOI: 10.23919/JCN.2024.000066
Xianli Sun;Linghua Zhang;Qiqing Zhai;Peng Zheng
How to comprehend the relationship between information spreading and individual behavior adoption is an essential problem in complex networks. To this end, a novel two-layer model to depict the diffusion of green behavior under the impact of positive and negative information is proposed. Positive information motivates people to adopt green behavior, while negative information reduces the adoption of green behavior. In the model, the physical contact layer describes the green behavior diffusion, and the information layer describes the positive and negative information spreading. Moreover, the social interactions of individuals in two layers change with time and are illustrated by an activity-driven model. Then, we develop the probability transition equations and derive the green behavior threshold. Next, experiments are carried out to confirm the preciseness and theoretical predictions of the new model. It reveals that the prevalence of green behavior can be promoted by restraining the negative information transmission rate and recovery rate of the green nodes while facilitating the positive information transmission rate and green behavior transmission rate. Additionally, reducing the positive information recovery rate and the recovery rate of the green nodes, and increasing the rates of forgetting negative information are beneficial for encouraging the outbreak of green behavior. Furthermore, in the physical contact layer, higher contact capacity and greater activity heterogeneity significantly facilitate green behavior spreading. In the information layer, smaller contact capacity and weaker activity heterogeneity promote diffusion when negative information dominates, whereas larger contact capacity and stronger activity heterogeneity are beneficial when positive information prevails.
{"title":"Green behavior diffusion with positive and negative information in time-varying multiplex networks","authors":"Xianli Sun;Linghua Zhang;Qiqing Zhai;Peng Zheng","doi":"10.23919/JCN.2024.000066","DOIUrl":"https://doi.org/10.23919/JCN.2024.000066","url":null,"abstract":"How to comprehend the relationship between information spreading and individual behavior adoption is an essential problem in complex networks. To this end, a novel two-layer model to depict the diffusion of green behavior under the impact of positive and negative information is proposed. Positive information motivates people to adopt green behavior, while negative information reduces the adoption of green behavior. In the model, the physical contact layer describes the green behavior diffusion, and the information layer describes the positive and negative information spreading. Moreover, the social interactions of individuals in two layers change with time and are illustrated by an activity-driven model. Then, we develop the probability transition equations and derive the green behavior threshold. Next, experiments are carried out to confirm the preciseness and theoretical predictions of the new model. It reveals that the prevalence of green behavior can be promoted by restraining the negative information transmission rate and recovery rate of the green nodes while facilitating the positive information transmission rate and green behavior transmission rate. Additionally, reducing the positive information recovery rate and the recovery rate of the green nodes, and increasing the rates of forgetting negative information are beneficial for encouraging the outbreak of green behavior. Furthermore, in the physical contact layer, higher contact capacity and greater activity heterogeneity significantly facilitate green behavior spreading. In the information layer, smaller contact capacity and weaker activity heterogeneity promote diffusion when negative information dominates, whereas larger contact capacity and stronger activity heterogeneity are beneficial when positive information prevails.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"26 6","pages":"650-665"},"PeriodicalIF":2.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10834484","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142937881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
Pub Date : 2024-12-01DOI: 10.23919/JCN.2024.000076
{"title":"Open access publishing agreement","authors":"","doi":"10.23919/JCN.2024.000076","DOIUrl":"https://doi.org/10.23919/JCN.2024.000076","url":null,"abstract":"","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"26 6","pages":"716-718"},"PeriodicalIF":2.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10834495","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142937982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
Pub Date : 2024-12-01DOI: 10.23919/JCN.2024.000053
Shixun Wu;Miao Zhang;Kanapathippillai Cumanan;Kai Xu;Zhangli Lan
In this paper, mobile terminal (MT) tracking based on time of arrival (TOA), angle of departure (AOD), and angle of arrival (AOA) measurements with one base station is investigated. The main challenge is the unknown propagation environment, such as line-of-sight (LOS), non-line-of-sight (NLOS) modeled as one-bounce scattering or mixed LOS/NLOS propagations, which may result in heterogeneous measurements. For LOS scenario, a linear Kalman filter (LKF) algorithm is adopted through analyzing and deriving the estimated error of MT. For NLOS scenario, as the position of scatterer is unknown, a nonlinear range equation is formulated to measure the actual AOD/AOA measurements and the position of scatterer, and three different algorithms: The extended Kalman filter (EKF), unscented Kalman filter (UKF) and an approximated LKF are developed. For mixed LOS/NLOS scenario, the modified interacting multiple model LKF (M-IMM-LKF) and the identified LKF algorithms (I-LKF) are utilized to address the issue of the frequent transition between LOS and NLOS propagations. In comparison with EKF and UKF algorithms, the simulation results and running time comparisons show the superiority and effectiveness of the LKF algorithm in LOS and NLOS scenarios. Both M-IMM-LKF and I-LKF algorithms are capable to significantly reduce the localization errors, and better than three existing algorithms.
{"title":"Single base station tracking approaches with hybrid TOA/AOD/AOA measurements in different propagation environments","authors":"Shixun Wu;Miao Zhang;Kanapathippillai Cumanan;Kai Xu;Zhangli Lan","doi":"10.23919/JCN.2024.000053","DOIUrl":"https://doi.org/10.23919/JCN.2024.000053","url":null,"abstract":"In this paper, mobile terminal (MT) tracking based on time of arrival (TOA), angle of departure (AOD), and angle of arrival (AOA) measurements with one base station is investigated. The main challenge is the unknown propagation environment, such as line-of-sight (LOS), non-line-of-sight (NLOS) modeled as one-bounce scattering or mixed LOS/NLOS propagations, which may result in heterogeneous measurements. For LOS scenario, a linear Kalman filter (LKF) algorithm is adopted through analyzing and deriving the estimated error of MT. For NLOS scenario, as the position of scatterer is unknown, a nonlinear range equation is formulated to measure the actual AOD/AOA measurements and the position of scatterer, and three different algorithms: The extended Kalman filter (EKF), unscented Kalman filter (UKF) and an approximated LKF are developed. For mixed LOS/NLOS scenario, the modified interacting multiple model LKF (M-IMM-LKF) and the identified LKF algorithms (I-LKF) are utilized to address the issue of the frequent transition between LOS and NLOS propagations. In comparison with EKF and UKF algorithms, the simulation results and running time comparisons show the superiority and effectiveness of the LKF algorithm in LOS and NLOS scenarios. Both M-IMM-LKF and I-LKF algorithms are capable to significantly reduce the localization errors, and better than three existing algorithms.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"26 6","pages":"617-631"},"PeriodicalIF":2.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10834482","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
Pub Date : 2024-12-01DOI: 10.23919/JCN.2024.000067
Li Zhang;Gang Zhou;Gangyin Sun;Chaopeng Wu
More and more deep learning methods have been applied to wireless communication systems. However, the collection of authentic signal data poses challenges. Moreover, due to the vulnerability of neural networks, adversarial attacks seriously threaten the security of communication systems based on deep learning models. Traditional signal augmentation methods expand the dataset through transformations such as rotation and flip, but these methods improve the adversarial robustness of the model little. However, common methods to improve adversarial robustness such as adversarial training not only have a high computational overhead but also potentially lead to a decrease in accuracy on clean samples. In this work, we propose a signal augmentation method called adversarial and mixed-based signal augmentation (AMSA). The method can improve the adversarial robustness of the model while expanding the dataset and does not compromise the generalization ability. It combines adversarial training with data mixing and then interpolates selected pairs of samples to form new samples in an expanded dataset consisting of original and adversarial samples thus generating more diverse data. We conduct experiments on the RML2016.10a and RML2018.01a datasets using automatic modulation recognition (AMR) models based on convolutional neural networks (CNN), long short-term memory (LSTM), convolutional long short-term deep neural networks (CLDNN), and transformer. And compare the performance in scenarios with different numbers of samples. The results show that AMSA allows the model to achieve comparable or even better adversarial robustness than using adversarial training, and reduces the degradation of the model's generalization performance on clean data.
{"title":"Signal augmentation method based on mixing and adversarial training for better robustness and generalization","authors":"Li Zhang;Gang Zhou;Gangyin Sun;Chaopeng Wu","doi":"10.23919/JCN.2024.000067","DOIUrl":"https://doi.org/10.23919/JCN.2024.000067","url":null,"abstract":"More and more deep learning methods have been applied to wireless communication systems. However, the collection of authentic signal data poses challenges. Moreover, due to the vulnerability of neural networks, adversarial attacks seriously threaten the security of communication systems based on deep learning models. Traditional signal augmentation methods expand the dataset through transformations such as rotation and flip, but these methods improve the adversarial robustness of the model little. However, common methods to improve adversarial robustness such as adversarial training not only have a high computational overhead but also potentially lead to a decrease in accuracy on clean samples. In this work, we propose a signal augmentation method called adversarial and mixed-based signal augmentation (AMSA). The method can improve the adversarial robustness of the model while expanding the dataset and does not compromise the generalization ability. It combines adversarial training with data mixing and then interpolates selected pairs of samples to form new samples in an expanded dataset consisting of original and adversarial samples thus generating more diverse data. We conduct experiments on the RML2016.10a and RML2018.01a datasets using automatic modulation recognition (AMR) models based on convolutional neural networks (CNN), long short-term memory (LSTM), convolutional long short-term deep neural networks (CLDNN), and transformer. And compare the performance in scenarios with different numbers of samples. The results show that AMSA allows the model to achieve comparable or even better adversarial robustness than using adversarial training, and reduces the degradation of the model's generalization performance on clean data.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"26 6","pages":"679-688"},"PeriodicalIF":2.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10834486","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
Pub Date : 2024-12-01DOI: 10.23919/JCN.2024.000069
Pengxue Liu;Yitong Li;Dalong Zhang
This paper presents an extensive analysis of the IEEE 802.11ax uplink orthogonal frequency-division multiple access (OFDMA)-based random access (UORA) mechanism, addressing inherent inefficiencies in channel access under varying network loads. Specifically, a mathematical model is developed to analyze the system performance of the 802.11ax UORA protocol, enabling the characterization of steady-state operating points under both saturated and unsaturated conditions. Key performance metrics, including system efficiency and mean access delay, are derived as functions of the steady-state operating points. Optimization of these performance metrics through the appropriate selection of backoff parameters is explored, with the analysis validated by simulation results. Additionally, the effects of access parameter heterogeneity, multi-link operation (MLO) and multiple resource unit (MRU) operation capabilities on the performance of IEEE 802.11ax UORA mechanism are further discussed.
{"title":"Performance optimization of IEEE 802.11ax UL OFDMA random access","authors":"Pengxue Liu;Yitong Li;Dalong Zhang","doi":"10.23919/JCN.2024.000069","DOIUrl":"https://doi.org/10.23919/JCN.2024.000069","url":null,"abstract":"This paper presents an extensive analysis of the IEEE 802.11ax uplink orthogonal frequency-division multiple access (OFDMA)-based random access (UORA) mechanism, addressing inherent inefficiencies in channel access under varying network loads. Specifically, a mathematical model is developed to analyze the system performance of the 802.11ax UORA protocol, enabling the characterization of steady-state operating points under both saturated and unsaturated conditions. Key performance metrics, including system efficiency and mean access delay, are derived as functions of the steady-state operating points. Optimization of these performance metrics through the appropriate selection of backoff parameters is explored, with the analysis validated by simulation results. Additionally, the effects of access parameter heterogeneity, multi-link operation (MLO) and multiple resource unit (MRU) operation capabilities on the performance of IEEE 802.11ax UORA mechanism are further discussed.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"26 6","pages":"580-592"},"PeriodicalIF":2.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10834483","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
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