Pub Date : 2022-09-01DOI: 10.1109/VTC2022-Fall57202.2022.10012963
Oscar L. Owen, Zhenni Pan, S. Shimamoto
This research investigates the use of a hybrid technique to locate vehicle positions on a 2D plane solely via other vehicles to further the future realization of Vehicle-to-Vehicle (V2V) communication. An approach in which trilateration and Time Difference Of Arrival (TDOA) are combined to estimate the Direction Of Arrival (DOA) of an incoming signal is considered. By using TDOA measurements of receivers on the Receiver Vehicle (RV), estimation regions are constructed to robustly obtain the Transmitter Vehicle (TV) position. This proposal not only creates a method for TDOA to be directly used in V2V communication but compared to other localization methods such as TOA (Time Of Arrival), the proposed technique does not need to consider time synchronization between the TV and RV, allowing for usage in a larger variety of on-road scenarios. A regression model is also implemented to further improve the accuracy of the estimation. Evaluation of the proposal is conducted for same side DOA and opposing side DOA. The DOA estimation was compared with a theoretically ideal scenario incorporating TOA. For further clarification of the methods utility and to mimic the transmission signal in road environments, the proposal is also tested in a ray tracing propagation model. The simulations show that the proposed solution accompanied with the regression model estimated the DOA in a 1 nanosecond (ns) time step environment to 1.92° accuracy and 0.08°accuracy in a 0.1ns time step environment.
{"title":"Vehicle Localization utilizing a Novel Hybrid TDOA-Based Estimation","authors":"Oscar L. Owen, Zhenni Pan, S. Shimamoto","doi":"10.1109/VTC2022-Fall57202.2022.10012963","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012963","url":null,"abstract":"This research investigates the use of a hybrid technique to locate vehicle positions on a 2D plane solely via other vehicles to further the future realization of Vehicle-to-Vehicle (V2V) communication. An approach in which trilateration and Time Difference Of Arrival (TDOA) are combined to estimate the Direction Of Arrival (DOA) of an incoming signal is considered. By using TDOA measurements of receivers on the Receiver Vehicle (RV), estimation regions are constructed to robustly obtain the Transmitter Vehicle (TV) position. This proposal not only creates a method for TDOA to be directly used in V2V communication but compared to other localization methods such as TOA (Time Of Arrival), the proposed technique does not need to consider time synchronization between the TV and RV, allowing for usage in a larger variety of on-road scenarios. A regression model is also implemented to further improve the accuracy of the estimation. Evaluation of the proposal is conducted for same side DOA and opposing side DOA. The DOA estimation was compared with a theoretically ideal scenario incorporating TOA. For further clarification of the methods utility and to mimic the transmission signal in road environments, the proposal is also tested in a ray tracing propagation model. The simulations show that the proposed solution accompanied with the regression model estimated the DOA in a 1 nanosecond (ns) time step environment to 1.92° accuracy and 0.08°accuracy in a 0.1ns time step environment.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115915538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-01DOI: 10.1109/VTC2022-Fall57202.2022.10012705
Kai Huang, Zezhou Luo, Le Liang, Shi Jin
In this paper, we investigate the resource allocation problem in a dynamic vehicular environment, where multiple vehicle-to-vehicle links attempt to reuse the spectrum of vehicle-to-infrastructure links. It is modeled as a deep reinforcement learning problem that is subject to proximal policy optimization. Training a well-performing policy usually requires a massive amount of interactions with the environment for a long time and thus is typically performed on a simulator. However, an agent well trained in a simulated environment may still fail when deployed in a live network, due to inevitable difference between the two environments, termed reality gap. We make preliminary efforts to address this issue by leveraging meta reinforcement learning that allows the learning agent to quickly adapt to a new environment with minimal interactions after being trained across a variety of similar tasks. We demonstrate that only a few episodes are required for the meta trained policy to adapt to a new environment and the proposed method is shown to achieve near-optimal performance and exhibit rapid convergence.
{"title":"Fast Spectrum Sharing in Vehicular Networks: A Meta Reinforcement Learning Approach","authors":"Kai Huang, Zezhou Luo, Le Liang, Shi Jin","doi":"10.1109/VTC2022-Fall57202.2022.10012705","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012705","url":null,"abstract":"In this paper, we investigate the resource allocation problem in a dynamic vehicular environment, where multiple vehicle-to-vehicle links attempt to reuse the spectrum of vehicle-to-infrastructure links. It is modeled as a deep reinforcement learning problem that is subject to proximal policy optimization. Training a well-performing policy usually requires a massive amount of interactions with the environment for a long time and thus is typically performed on a simulator. However, an agent well trained in a simulated environment may still fail when deployed in a live network, due to inevitable difference between the two environments, termed reality gap. We make preliminary efforts to address this issue by leveraging meta reinforcement learning that allows the learning agent to quickly adapt to a new environment with minimal interactions after being trained across a variety of similar tasks. We demonstrate that only a few episodes are required for the meta trained policy to adapt to a new environment and the proposed method is shown to achieve near-optimal performance and exhibit rapid convergence.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114694573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-01DOI: 10.1109/VTC2022-Fall57202.2022.10012935
Mustafa Ammous, S. Valaee
Due to their capability in creating a controllable wireless environment, extending coverage and improving localization accuracy, reconfigurable intelligent surfaces (RISs) are expected to be a main component of future 6G networks. In this paper, we present a novel cooperative positioning (CP) use-case of the RIS in mmWave frequencies. We show that two mobile stations (MSs) are able to estimate their positions through device-to-device (D2D) communications, and processing the signals reflected from the RIS. We start by building the system model based on the uniform linear array (ULA) architecture of the RIS elements. Then, we derive the Fisher information matrix (FIM) and the Cramér-Rao lower bound (CRLB) for calculating the MSs positioning error. After that, we optimize the RIS configuration to minimize the CRLB. Finally, simulation results compare the localization performance of random phases at the RIS with the optimal configuration.
{"title":"Cooperative Positioning with the Aid of Reconfigurable Intelligent Surfaces and Zero Access Points","authors":"Mustafa Ammous, S. Valaee","doi":"10.1109/VTC2022-Fall57202.2022.10012935","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012935","url":null,"abstract":"Due to their capability in creating a controllable wireless environment, extending coverage and improving localization accuracy, reconfigurable intelligent surfaces (RISs) are expected to be a main component of future 6G networks. In this paper, we present a novel cooperative positioning (CP) use-case of the RIS in mmWave frequencies. We show that two mobile stations (MSs) are able to estimate their positions through device-to-device (D2D) communications, and processing the signals reflected from the RIS. We start by building the system model based on the uniform linear array (ULA) architecture of the RIS elements. Then, we derive the Fisher information matrix (FIM) and the Cramér-Rao lower bound (CRLB) for calculating the MSs positioning error. After that, we optimize the RIS configuration to minimize the CRLB. Finally, simulation results compare the localization performance of random phases at the RIS with the optimal configuration.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115687737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-01DOI: 10.1109/VTC2022-Fall57202.2022.10012878
Chenxing Li, Yiping Duan, Qiyuan Du, Chengkang Pan, Guangyi Liu, Xiaoming Tao
Significant progress has been made on methods for generating images from structured semantic descriptions, but the generated images only retain semantic information, and the appearance of objects cannot be constrained and effectively represented. Therefore, we propose a scene graph structure image generation method assisted by object edge information. Our model uses two graph convolution neural networks(GCN) to process scene graphs and obtains object features as well as relation features which aggregate related information. The object bounding boxes are predicted by a method a decoupling the size and position. Where auxiliary models are added to coordinate with segmentation mask network training. Our experiments show that the introduction of object edges provides clearer object appearance information for image generation, which can constrain object shapes and improve image quality greatly. Finally, the cascaded refinement network is used to generate images. Additionally, compared with other appearance features, such as object slices, edge information occupies a smaller quantity of data, which greatly improves the image quality with less increase in the input information. This feature also benefits semantic communication systems. A large number of experiments show that our method is significantly superior to the latest Sg2im method when evaluated on Visual Genome datasets.
{"title":"Image Generation from Scene Graph with Object Edges","authors":"Chenxing Li, Yiping Duan, Qiyuan Du, Chengkang Pan, Guangyi Liu, Xiaoming Tao","doi":"10.1109/VTC2022-Fall57202.2022.10012878","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012878","url":null,"abstract":"Significant progress has been made on methods for generating images from structured semantic descriptions, but the generated images only retain semantic information, and the appearance of objects cannot be constrained and effectively represented. Therefore, we propose a scene graph structure image generation method assisted by object edge information. Our model uses two graph convolution neural networks(GCN) to process scene graphs and obtains object features as well as relation features which aggregate related information. The object bounding boxes are predicted by a method a decoupling the size and position. Where auxiliary models are added to coordinate with segmentation mask network training. Our experiments show that the introduction of object edges provides clearer object appearance information for image generation, which can constrain object shapes and improve image quality greatly. Finally, the cascaded refinement network is used to generate images. Additionally, compared with other appearance features, such as object slices, edge information occupies a smaller quantity of data, which greatly improves the image quality with less increase in the input information. This feature also benefits semantic communication systems. A large number of experiments show that our method is significantly superior to the latest Sg2im method when evaluated on Visual Genome datasets.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124637908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-01DOI: 10.1109/VTC2022-Fall57202.2022.10012727
Zhaoyang Su, Liu Liu, Shiyuan Cai, Lei Suo, Feng Bao
Urban rail transit has become an important way for people to travel. The traditional urban rail transit system has fixed infrastructure, relies on base stations for communication, and has poor network robustness. The ad-hoc network has developed rapidly in recent years due to its high stability. And it can be used in urban rail to improve the performance of communication networks. In this paper, a clustering algorithm based on urban rail in-vehicle ad-hoc networks is proposed. The algorithm includes cluster head selected strategy and low-delay queuing strategy. We introduce the network architecture and algorithm theory in detail, and verify the algorithm performance in terms of end-to-end delay and packet loss rate through simulation. As the result, the algorithm can effectively improve communication efficiency and reliability.
{"title":"A Clustering Algorithm Based on Node Cost and Service Priority for Urban Rail In-Vehicle Ad-Hoc Network","authors":"Zhaoyang Su, Liu Liu, Shiyuan Cai, Lei Suo, Feng Bao","doi":"10.1109/VTC2022-Fall57202.2022.10012727","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012727","url":null,"abstract":"Urban rail transit has become an important way for people to travel. The traditional urban rail transit system has fixed infrastructure, relies on base stations for communication, and has poor network robustness. The ad-hoc network has developed rapidly in recent years due to its high stability. And it can be used in urban rail to improve the performance of communication networks. In this paper, a clustering algorithm based on urban rail in-vehicle ad-hoc networks is proposed. The algorithm includes cluster head selected strategy and low-delay queuing strategy. We introduce the network architecture and algorithm theory in detail, and verify the algorithm performance in terms of end-to-end delay and packet loss rate through simulation. As the result, the algorithm can effectively improve communication efficiency and reliability.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121646618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-01DOI: 10.1109/VTC2022-Fall57202.2022.10012879
Lin He, Rong Chai, Ruijin Sun
Unmanned aerial vehicles (UAVs) have emerged as a promising solution to provide wireless data access for ground users (GUs) in various applications. In this paper, we study UAV deployment problem in an integrated access and backhaul network, where a number of UAVs are deployed as aerial base stations (ABSs) or aerial relays (ARs) to forward GUs’ data packets to the remote gateway via multi-hop transmissions. Aiming at minimizing the system cost, which is defined as the weighted sum of UAV deployment cost and the energy consumption required for data transmission, a constrained system cost minimization problem is formulated, where UAV deployment, GU association and route selection problem are optimized. To solve the formulated non-convex problem, we propose a two-stage heuristic algorithm. In the first stage, we focus on the optimal design of the access links and propose a joint ABS deployment and resource allocation algorithm. Specifically, a modified K-means based clustering scheme is proposed to determine ABS deployment and GU association strategy. Given the obtained ABS deployment strategy, in the second stage, we then design a joint AR deployment, route selection scheme for the backhaul links and propose a minimum circle algorithm-based AR deployment and route selection strategy. Numerical results verify the effectiveness of the proposed algorithm.
{"title":"Cost Efficient UAV Deployment and Resource Allocation for UAV-Assisted Networks","authors":"Lin He, Rong Chai, Ruijin Sun","doi":"10.1109/VTC2022-Fall57202.2022.10012879","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012879","url":null,"abstract":"Unmanned aerial vehicles (UAVs) have emerged as a promising solution to provide wireless data access for ground users (GUs) in various applications. In this paper, we study UAV deployment problem in an integrated access and backhaul network, where a number of UAVs are deployed as aerial base stations (ABSs) or aerial relays (ARs) to forward GUs’ data packets to the remote gateway via multi-hop transmissions. Aiming at minimizing the system cost, which is defined as the weighted sum of UAV deployment cost and the energy consumption required for data transmission, a constrained system cost minimization problem is formulated, where UAV deployment, GU association and route selection problem are optimized. To solve the formulated non-convex problem, we propose a two-stage heuristic algorithm. In the first stage, we focus on the optimal design of the access links and propose a joint ABS deployment and resource allocation algorithm. Specifically, a modified K-means based clustering scheme is proposed to determine ABS deployment and GU association strategy. Given the obtained ABS deployment strategy, in the second stage, we then design a joint AR deployment, route selection scheme for the backhaul links and propose a minimum circle algorithm-based AR deployment and route selection strategy. Numerical results verify the effectiveness of the proposed algorithm.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123399192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-01DOI: 10.1109/VTC2022-Fall57202.2022.10012697
Xinmin Li, Jiahui Li, B. Yin, Jiaxin Yan, Yuan Fang
In this work, we investigate an uplink unmanned aerial vehicles (UAVs)-enabled intelligent transportation system to collect data from traveling vehicles on a specific highway road. To ensure the freshness of information delivered from the traveling vehicles to UAV base stations, we use the new age of information (AoI) metric to characterize the information freshness and formulate the AoI minimization problem by optimizing the UAVs’ trajectories and the communication time of vehicles jointly. In order to handle the mixed-integer nonlinear problem, a multi-agent deep reinforcement learning scheme is proposed by applying independent flight direction and time slot action spaces, in which each UAV working as an independent agent adjusts to the dynamic environment quickly based on stored experience. The AoI-related reward function is proposed to select the beneficial action space to guarantee the information freshness. Numerical simulation results show the proposed scheme outperforms the benchmark schemes.
{"title":"Age of Information Optimization in UAV-enabled Intelligent Transportation System via Deep Reinforcement Learning","authors":"Xinmin Li, Jiahui Li, B. Yin, Jiaxin Yan, Yuan Fang","doi":"10.1109/VTC2022-Fall57202.2022.10012697","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012697","url":null,"abstract":"In this work, we investigate an uplink unmanned aerial vehicles (UAVs)-enabled intelligent transportation system to collect data from traveling vehicles on a specific highway road. To ensure the freshness of information delivered from the traveling vehicles to UAV base stations, we use the new age of information (AoI) metric to characterize the information freshness and formulate the AoI minimization problem by optimizing the UAVs’ trajectories and the communication time of vehicles jointly. In order to handle the mixed-integer nonlinear problem, a multi-agent deep reinforcement learning scheme is proposed by applying independent flight direction and time slot action spaces, in which each UAV working as an independent agent adjusts to the dynamic environment quickly based on stored experience. The AoI-related reward function is proposed to select the beneficial action space to guarantee the information freshness. Numerical simulation results show the proposed scheme outperforms the benchmark schemes.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123762536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-01DOI: 10.1109/VTC2022-Fall57202.2022.10013079
Chao Chen, Ziye Li, S. Baek, Rui Yin, Xiaohan Yu, Chuanhuang Li
We consider the minimum-delay multicast scheduling problem for switched beamforming systems. A salient characteristic of mmWave links, reflection, is considered, which enables opportunistic reduction of data dissemination delay. We formulate the problem as a mixed integer nonlinear programming, which is difficult to solve directly. Instead, we decompose the problem into a set of subproblems, by allocating a fixed path to each receiver for data reception. The optimal solution to each subproblem has a contiguous structure, and hence can be computed using a dynamic programming-based approach. We propose an optimal algorithm for the original problem based on the solutions to the subproblems. By simulation we show the outperformance of our algorithm over an optimal multicast scheduling policy without leveraging reflections and a broadcast baseline scheme.
{"title":"Optimal Multicast Scheduling for Switched Beamforming Systems Leveraging Reflections","authors":"Chao Chen, Ziye Li, S. Baek, Rui Yin, Xiaohan Yu, Chuanhuang Li","doi":"10.1109/VTC2022-Fall57202.2022.10013079","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10013079","url":null,"abstract":"We consider the minimum-delay multicast scheduling problem for switched beamforming systems. A salient characteristic of mmWave links, reflection, is considered, which enables opportunistic reduction of data dissemination delay. We formulate the problem as a mixed integer nonlinear programming, which is difficult to solve directly. Instead, we decompose the problem into a set of subproblems, by allocating a fixed path to each receiver for data reception. The optimal solution to each subproblem has a contiguous structure, and hence can be computed using a dynamic programming-based approach. We propose an optimal algorithm for the original problem based on the solutions to the subproblems. By simulation we show the outperformance of our algorithm over an optimal multicast scheduling policy without leveraging reflections and a broadcast baseline scheme.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128653583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-01DOI: 10.1109/VTC2022-Fall57202.2022.10012720
Ahmed Aladi, E. Alsusa
In recent years, orthogonal frequency division multiplexing has been considered a potential technology for serving as a source of randomness for the physical layer security designs due to the high dimensionality per single transmission represented by the modulated subcarriers. In this paper, we first proposed an algorithm with a pre-shared key to rotate the constellation mapping of the modulated M-ary phase shift keying M-PSK symbols. Following this, we exploited the independent channel fading of the subcarriers by inducing a phase error per symbol based on the channel state information to make the cryptographic attacks more challenging. The bit mismatch was then minimised through error-correcting codes. The security’s efficacy can be evaluated by the symbol error rate as a false cipher detection rate. The simulation results indicated that the attacker’s receiver suffers higher detection errors of the cipher even when the received signal-to-noise ratio is the same as that of the legitimate users.
{"title":"Physical-Layer-Security-based OFDM Transmission with Phase Error Insertion","authors":"Ahmed Aladi, E. Alsusa","doi":"10.1109/VTC2022-Fall57202.2022.10012720","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012720","url":null,"abstract":"In recent years, orthogonal frequency division multiplexing has been considered a potential technology for serving as a source of randomness for the physical layer security designs due to the high dimensionality per single transmission represented by the modulated subcarriers. In this paper, we first proposed an algorithm with a pre-shared key to rotate the constellation mapping of the modulated M-ary phase shift keying M-PSK symbols. Following this, we exploited the independent channel fading of the subcarriers by inducing a phase error per symbol based on the channel state information to make the cryptographic attacks more challenging. The bit mismatch was then minimised through error-correcting codes. The security’s efficacy can be evaluated by the symbol error rate as a false cipher detection rate. The simulation results indicated that the attacker’s receiver suffers higher detection errors of the cipher even when the received signal-to-noise ratio is the same as that of the legitimate users.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128773675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-01DOI: 10.1109/VTC2022-Fall57202.2022.10013081
Go Takita, Takanori Hara, Y. Yuda, K. Higuchi
This paper proposes a repetition-based low-latency non-orthogonal multiple access (NOMA)-hybrid automatic repeat request (HARQ) method with adaptive termination for ultra-reliable low latency communications (URLLC). To reduce transmission delay, the proposed method retransmits packets in short transmission time intervals in advance and terminates packet retransmissions adaptively according to acknowledgement (ACK) feedback from the receiver. In addition, to reduce the throughput loss due to unnecessary retransmitted packets sent in the time until ACK feedback, retransmitted packets of the URLLC user are non-orthogonally multiplexed in the same channel with packets of other users based on NOMA. Two non-orthogonal multiplexing methods are compared: superposition coding (SPC) and joint modulation (JM). As for the receiver structure, a successive interference canceller (SIC) and complexity reduced maximum likelihood detection (R-ML) are investigated. We confirm that the proposed method using JM and R-ML provides the best improvement in the transmission delay time versus throughput trade-off based on computer simulations.
{"title":"Repetition-Based NOMA-HARQ with Adaptive Termination for URLLC","authors":"Go Takita, Takanori Hara, Y. Yuda, K. Higuchi","doi":"10.1109/VTC2022-Fall57202.2022.10013081","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10013081","url":null,"abstract":"This paper proposes a repetition-based low-latency non-orthogonal multiple access (NOMA)-hybrid automatic repeat request (HARQ) method with adaptive termination for ultra-reliable low latency communications (URLLC). To reduce transmission delay, the proposed method retransmits packets in short transmission time intervals in advance and terminates packet retransmissions adaptively according to acknowledgement (ACK) feedback from the receiver. In addition, to reduce the throughput loss due to unnecessary retransmitted packets sent in the time until ACK feedback, retransmitted packets of the URLLC user are non-orthogonally multiplexed in the same channel with packets of other users based on NOMA. Two non-orthogonal multiplexing methods are compared: superposition coding (SPC) and joint modulation (JM). As for the receiver structure, a successive interference canceller (SIC) and complexity reduced maximum likelihood detection (R-ML) are investigated. We confirm that the proposed method using JM and R-ML provides the best improvement in the transmission delay time versus throughput trade-off based on computer simulations.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128771582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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