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-Fall57202.2022.10012836
Mourad Raif, El Mehdi Ouafiq, Abdessamad El Rharras, A. Chehri, Rachid Saadane
Smart city applications are using extensively artificial intelligence for decision-making. Among the fields of application are facial recognition and intrusion detection. The subject is old, but processing techniques and hardware are constantly evolving. This paper will review the most widely known practices and apply them to a smart parking and intrusion detection system using the “JetsonNano” board. Nowadays, quality assurance for machine learning systems is becoming increasingly important. This article focuses on detecting bugs in implementing two classical face recognition algorithms: Eigenface (EF) and Local binary pattern histogram (LBPH). We tested the efficiency of our system using metamorphic testing depending on many factors: weather conditions, pixel noise, and distortion.
{"title":"Metamorphic Testing for Edge Real-Time Face Recognition and Intrusion Detection Solution","authors":"Mourad Raif, El Mehdi Ouafiq, Abdessamad El Rharras, A. Chehri, Rachid Saadane","doi":"10.1109/VTC2022-Fall57202.2022.10012836","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012836","url":null,"abstract":"Smart city applications are using extensively artificial intelligence for decision-making. Among the fields of application are facial recognition and intrusion detection. The subject is old, but processing techniques and hardware are constantly evolving. This paper will review the most widely known practices and apply them to a smart parking and intrusion detection system using the “JetsonNano” board. Nowadays, quality assurance for machine learning systems is becoming increasingly important. This article focuses on detecting bugs in implementing two classical face recognition algorithms: Eigenface (EF) and Local binary pattern histogram (LBPH). We tested the efficiency of our system using metamorphic testing depending on many factors: weather conditions, pixel noise, and distortion.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"88 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":"130901766","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-Fall57202.2022.10012897
Li Zhao, Jin-Ling Hu, Rui Zhao, Yan Shi
New Radio-Vehicle-to-Everything (NR-V2X) is motivated for supporting the advanced V2X applications, such as the autonomous driving, platooning, etc. The enhanced sidelink resource reservation mechanism of NR-V2X utilizes the characteristics of V2X applications, Hybrid Automatic Repeat reQuest (HARQ) scheme to improve the extreme high reliability and provide the flexible resource indications. Meanwhile, the sidelink signaling overhead for sidelink resource reservation and system performance gain should be balanced carefully. In this paper, the challenges and detailed design issues of the enhanced sidelink resource reservation mechanism of NR-V2X are depicted. The performances with typical configurations and scenarios are presented and analyzed.
{"title":"The Enhanced Sidelink Resource Reservation Mechanism of NR-V2X","authors":"Li Zhao, Jin-Ling Hu, Rui Zhao, Yan Shi","doi":"10.1109/VTC2022-Fall57202.2022.10012897","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012897","url":null,"abstract":"New Radio-Vehicle-to-Everything (NR-V2X) is motivated for supporting the advanced V2X applications, such as the autonomous driving, platooning, etc. The enhanced sidelink resource reservation mechanism of NR-V2X utilizes the characteristics of V2X applications, Hybrid Automatic Repeat reQuest (HARQ) scheme to improve the extreme high reliability and provide the flexible resource indications. Meanwhile, the sidelink signaling overhead for sidelink resource reservation and system performance gain should be balanced carefully. In this paper, the challenges and detailed design issues of the enhanced sidelink resource reservation mechanism of NR-V2X are depicted. The performances with typical configurations and scenarios are presented and analyzed.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"45 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":"126962004","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-Fall57202.2022.10013071
Weixuan Xiao, G. D. Sousa, N. Rachkidy, A. Guitton
LoRa (Long Range) is a physical layer designed for low-power wide area networks. It is widely used to provide long range connectivity to Internet of Things devices. In order to improve the limited throughput of LoRa, researchers have proposed several collision resolution algorithms. However, a common software framework to compare these algorithms is lacking. In this paper, we propose an open-source framework using GNU Radio, mainly designed to test and compare collision resolution algorithms, as well as physical layer algorithms. Our framework can help optimizing the parameters of algorithms according to channel conditions such as very low signal to noise ratio for instance. We also discuss technical implementation issues of existing collision resolution algorithms. Finally, we show how our framework can be used for either real experiments on USRPs, or for simulations with a large number of nodes.
{"title":"An Open-Source GNU Radio Framework for LoRa Physical Layer and Collision Resolution","authors":"Weixuan Xiao, G. D. Sousa, N. Rachkidy, A. Guitton","doi":"10.1109/VTC2022-Fall57202.2022.10013071","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10013071","url":null,"abstract":"LoRa (Long Range) is a physical layer designed for low-power wide area networks. It is widely used to provide long range connectivity to Internet of Things devices. In order to improve the limited throughput of LoRa, researchers have proposed several collision resolution algorithms. However, a common software framework to compare these algorithms is lacking. In this paper, we propose an open-source framework using GNU Radio, mainly designed to test and compare collision resolution algorithms, as well as physical layer algorithms. Our framework can help optimizing the parameters of algorithms according to channel conditions such as very low signal to noise ratio for instance. We also discuss technical implementation issues of existing collision resolution algorithms. Finally, we show how our framework can be used for either real experiments on USRPs, or for simulations with a large number of nodes.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"19 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":"116035960","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-Fall57202.2022.10012968
Takahiro Tomie, Satoshi Suyama, K. Kitao, Mitsuki Nakamura
This paper proposes a novel estimation method of radio propagation characteristics in outdoor urban environments based on color images. The proposed method considers only direct waves and single scattered waves. In order to detect single scattering walls, each wall of buildings in an evaluation area is assigned to a different RGB color. We create two types of color images that are viewed from a transmission point and reception points. By analyzing and comparing the colors of these images, the visible colors from both transmission and reception sides corresponding to the single scattering walls are detected all at once. The numbers of pixels on the scattering walls with the visible colors are used to calculate a received power of the single scattering waves. Comparison with measurement results of path loss shows high estimation accuracy of the proposed method with root mean squared error of 7.9 dB, and its calculation time is extremely short.
{"title":"A Novel Estimation Method of Radio Propagation Characteristics Based on Color Images","authors":"Takahiro Tomie, Satoshi Suyama, K. Kitao, Mitsuki Nakamura","doi":"10.1109/VTC2022-Fall57202.2022.10012968","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012968","url":null,"abstract":"This paper proposes a novel estimation method of radio propagation characteristics in outdoor urban environments based on color images. The proposed method considers only direct waves and single scattered waves. In order to detect single scattering walls, each wall of buildings in an evaluation area is assigned to a different RGB color. We create two types of color images that are viewed from a transmission point and reception points. By analyzing and comparing the colors of these images, the visible colors from both transmission and reception sides corresponding to the single scattering walls are detected all at once. The numbers of pixels on the scattering walls with the visible colors are used to calculate a received power of the single scattering waves. Comparison with measurement results of path loss shows high estimation accuracy of the proposed method with root mean squared error of 7.9 dB, and its calculation time is extremely short.","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":"126696576","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-Fall57202.2022.10013059
Liang Guo, Jie Jia, Jian Chen, An Du, Xingwei Wang
In this paper, the joint task offloading and resource allocation are investigated for the semi-grant-free (SGF) non-orthogonal multiple access (NOMA) assisted mobile edge computing (MEC) system. Moreover, simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) are deployed to improve the quality of wireless communications under the mode switching protocol. Each MU can partially or fully offload its task to the base station (BS) based on its differentiated channel conditions and computing capacity in the proposed MEC system. We formulate the joint task offloading, channel assignment, power allocation, and the RIS coefficients design problem to save energy consumption. The formulated problem is modeled from a long-term optimization perspective as a multi-agent Markov game (MG). Then, a multi-agent deep reinforcement learning (MADRL) based joint task offloading and resource allocation (JTORA) algorithm is proposed to solve the problem. The simulation results confirm that the applied SGF-NOMA scheme can significantly reduce energy consumption under a stringent latency constraint. Moreover, the effectiveness of the STAR-RIS and the proposed algorithm are confirmed.
{"title":"Joint Task Offloading and Resource Allocation in STAR-RIS assisted NOMA System","authors":"Liang Guo, Jie Jia, Jian Chen, An Du, Xingwei Wang","doi":"10.1109/VTC2022-Fall57202.2022.10013059","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10013059","url":null,"abstract":"In this paper, the joint task offloading and resource allocation are investigated for the semi-grant-free (SGF) non-orthogonal multiple access (NOMA) assisted mobile edge computing (MEC) system. Moreover, simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) are deployed to improve the quality of wireless communications under the mode switching protocol. Each MU can partially or fully offload its task to the base station (BS) based on its differentiated channel conditions and computing capacity in the proposed MEC system. We formulate the joint task offloading, channel assignment, power allocation, and the RIS coefficients design problem to save energy consumption. The formulated problem is modeled from a long-term optimization perspective as a multi-agent Markov game (MG). Then, a multi-agent deep reinforcement learning (MADRL) based joint task offloading and resource allocation (JTORA) algorithm is proposed to solve the problem. The simulation results confirm that the applied SGF-NOMA scheme can significantly reduce energy consumption under a stringent latency constraint. Moreover, the effectiveness of the STAR-RIS and the proposed algorithm are confirmed.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"138 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":"126014970","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-Fall57202.2022.10012928
Yang Li, Shuyi Chen, W. Meng
Massive multiple-input-multiple-output (MIMO) and millimeter-wave (mmWave) have been adopted as the enabling technologies for the 5G and beyond 5G (B5G) systems. However, due to the large number of antennas, it is hard to obtain the channel state information (CSI) which is essential for obtaining desirable beamforming gains. Off-grid error is one of the main limiting factors of the channel estimation (CE) performance, which presents when the true angle does not lie on the discretized angle grid of mmWave channel. To address this problem, we propose a joint algorithm named off-grid approximate message passing (OG-AMP) to achieve both angular domain CE and off-grid errors eliminationin in this paper. Specially, we formulate CE issue as a Bayesian inference problem to compute the posterior of the channel coefficients and adopt the Gaussian approximation to simplify the sum-product algorithm. Our simulation results show that the proposed algorithm shows the superiority over a state-of-art benchmark method.
{"title":"Bilinear Approximate Message Passing Based Off-grid Channel Estimation for Multi-user Millimeter-Wave MIMO System","authors":"Yang Li, Shuyi Chen, W. Meng","doi":"10.1109/VTC2022-Fall57202.2022.10012928","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012928","url":null,"abstract":"Massive multiple-input-multiple-output (MIMO) and millimeter-wave (mmWave) have been adopted as the enabling technologies for the 5G and beyond 5G (B5G) systems. However, due to the large number of antennas, it is hard to obtain the channel state information (CSI) which is essential for obtaining desirable beamforming gains. Off-grid error is one of the main limiting factors of the channel estimation (CE) performance, which presents when the true angle does not lie on the discretized angle grid of mmWave channel. To address this problem, we propose a joint algorithm named off-grid approximate message passing (OG-AMP) to achieve both angular domain CE and off-grid errors eliminationin in this paper. Specially, we formulate CE issue as a Bayesian inference problem to compute the posterior of the channel coefficients and adopt the Gaussian approximation to simplify the sum-product algorithm. Our simulation results show that the proposed algorithm shows the superiority over a state-of-art benchmark method.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"10 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":"126469731","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}
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}
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