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-Fall57202.2022.10012879
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-Fall57202.2022.10012727
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-Fall57202.2022.10013023
Yibin Zhang, Yang Peng, B. Adebisi, Guan Gui, H. Gačanin, H. Sari
The fast development of intelligent wireless communications enables many devices to access various networks. It often leads to the security risks of malicious access of illegal devices. To ensure a secure and reliable wireless access, it is necessary to identify illegal devices and prevent their attacks accurately. To improve the performance of specific emitter identification (SEI), this paper proposes a multi-scale convolution neural network (MSCNN) based on convolution layers of three branches with different convolution kernel sizes. MSCNN extracts radio frequency fingerprints (RFF) in three receptive fields through different convolution kernels. We verify the identification accuracy using the RF signals conforming to long term evolution (LTE) standard. The experimental results show that our proposed MSCNN-based SEI method can improve the absolute accuracy by 15% and the relative accuracy by 22% in perfect communication environment. In addition, we verify the robustness of proposed MSCNN by comparing identification performance in imperfect environment. Simulation results show that the proposed MSCNN can extract more hidden features through convolution kernels of different sizes, and thus achieves better SEI performance than existing methods.
{"title":"Specific Emitter Identification Based on Radio Frequency Fingerprint Using Multi-Scale Network","authors":"Yibin Zhang, Yang Peng, B. Adebisi, Guan Gui, H. Gačanin, H. Sari","doi":"10.1109/VTC2022-Fall57202.2022.10013023","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10013023","url":null,"abstract":"The fast development of intelligent wireless communications enables many devices to access various networks. It often leads to the security risks of malicious access of illegal devices. To ensure a secure and reliable wireless access, it is necessary to identify illegal devices and prevent their attacks accurately. To improve the performance of specific emitter identification (SEI), this paper proposes a multi-scale convolution neural network (MSCNN) based on convolution layers of three branches with different convolution kernel sizes. MSCNN extracts radio frequency fingerprints (RFF) in three receptive fields through different convolution kernels. We verify the identification accuracy using the RF signals conforming to long term evolution (LTE) standard. The experimental results show that our proposed MSCNN-based SEI method can improve the absolute accuracy by 15% and the relative accuracy by 22% in perfect communication environment. In addition, we verify the robustness of proposed MSCNN by comparing identification performance in imperfect environment. Simulation results show that the proposed MSCNN can extract more hidden features through convolution kernels of different sizes, and thus achieves better SEI performance than existing methods.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"35 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":"122178824","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-Fall57202.2022.10012699
Insup Kim, Gang-Lee Lee, Seyoung Lee, W. Choi
Autonomous vehicles (AVs) require large dataset to perceive surrounding accurately, and continuous connectivity to update software frequently. The more connection and data the vehicle has the more cybersecurity incidents could occur. To address the challenges of AVs development, new regulations and standards have been introduced from Event Data Recorder (EDR) and Data Storage System for Automated Driving (DSSAD) to automotive cybersecurity, and these new regulations and requirements demand AVs to equip large data storage to analyze accidents of AVs. New data storage for AVs could bring new cybersecurity risks. The main purpose of this paper is to derive data storage requirements for automated driving system (ADS) and to conduct systematic cybersecurity risk analysis for data storage. In this paper, the regulations and standards for AVs are reviewed and new requirements for data storage of automated driving system are derived based on that. Plus, cybersecurity risk of the future data storage is analyzed with threat analysis and risk analysis (TARA) method. Finally, cybersecurity validation and verification methods have been researched for data storage of AVs.
{"title":"Cybersecurity and Capacity Requirement for Data Storage of Autonomous Driving System","authors":"Insup Kim, Gang-Lee Lee, Seyoung Lee, W. Choi","doi":"10.1109/VTC2022-Fall57202.2022.10012699","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012699","url":null,"abstract":"Autonomous vehicles (AVs) require large dataset to perceive surrounding accurately, and continuous connectivity to update software frequently. The more connection and data the vehicle has the more cybersecurity incidents could occur. To address the challenges of AVs development, new regulations and standards have been introduced from Event Data Recorder (EDR) and Data Storage System for Automated Driving (DSSAD) to automotive cybersecurity, and these new regulations and requirements demand AVs to equip large data storage to analyze accidents of AVs. New data storage for AVs could bring new cybersecurity risks. The main purpose of this paper is to derive data storage requirements for automated driving system (ADS) and to conduct systematic cybersecurity risk analysis for data storage. In this paper, the regulations and standards for AVs are reviewed and new requirements for data storage of automated driving system are derived based on that. Plus, cybersecurity risk of the future data storage is analyzed with threat analysis and risk analysis (TARA) method. Finally, cybersecurity validation and verification methods have been researched for data storage of AVs.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"16 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":"124214556","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-Fall57202.2022.10012719
Tianyi Zhou, C. Liang, Qianbin Chen
With the development of satellite communications, satellites have been equipped with edge computing capability and edge caching capability, and these advancements can further drive the development of video transmission mechanisms. In this paper, we propose to utilize in-network caching and computing of software-defined space-air-ground integrated networks to improve the quality of video experience for users. The optimization problem can be viewed as a coupling of three parts, namely, the video resolution adaptation problem, the computing resource scheduling problem, and the bandwidth provision problem. To achieve the solution of the problem effectively in practice, we deploy the alternating direction method of multipliers to decouple the three sets of variables. Numerical results demonstrate the effectiveness of the proposed scheme.
{"title":"Joint Caching and Computing of Software-Defined Space-Air-Ground Integrated Networks for Video Streaming Service Improvement","authors":"Tianyi Zhou, C. Liang, Qianbin Chen","doi":"10.1109/VTC2022-Fall57202.2022.10012719","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012719","url":null,"abstract":"With the development of satellite communications, satellites have been equipped with edge computing capability and edge caching capability, and these advancements can further drive the development of video transmission mechanisms. In this paper, we propose to utilize in-network caching and computing of software-defined space-air-ground integrated networks to improve the quality of video experience for users. The optimization problem can be viewed as a coupling of three parts, namely, the video resolution adaptation problem, the computing resource scheduling problem, and the bandwidth provision problem. To achieve the solution of the problem effectively in practice, we deploy the alternating direction method of multipliers to decouple the three sets of variables. Numerical results demonstrate the effectiveness of the proposed scheme.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"29 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":"126171683","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-Fall57202.2022.10012876
Yu Yao, Junhui Zhao, Zeqing Li, Xu Cheng, Lenan Wu, Xuan Li
Vehicle-to-vehicle (V2V) communication applications face significant challenges to security and privacy since all types of possible breaches are common in connected and autonomous vehicles (CAVs) networks. As an inheritance from conventional wireless services, illegal eavesdropping is one of the main threats to Vehicle-to-vehicle (V2V) communications. In our work, the anti-eavesdropping scheme in CAVs networks is developed through the use of cognitive risk control (CRC)-based vehicular joint radar-communication (JRC) system. In particular, the supplement of off-board measurements acquired using V2V links to the perceptual information has presented the potential to enhance the traffic target positioning precision. Then, transmission power control is performed utilizing reinforcement learning, the result of which is determined by a task switcher. Based on the threat evaluation, a multi-armed bandit (MAB) problem is designed to implement the secret key selection procedure when it is needed. Numerical experiments have presented that the developed approach has anticipated performance in terms of some risk assessment indicators.
{"title":"Cognitive Risk Control for Anti-Eavesdropping in Connected and Autonomous Vehicles Network","authors":"Yu Yao, Junhui Zhao, Zeqing Li, Xu Cheng, Lenan Wu, Xuan Li","doi":"10.1109/VTC2022-Fall57202.2022.10012876","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012876","url":null,"abstract":"Vehicle-to-vehicle (V2V) communication applications face significant challenges to security and privacy since all types of possible breaches are common in connected and autonomous vehicles (CAVs) networks. As an inheritance from conventional wireless services, illegal eavesdropping is one of the main threats to Vehicle-to-vehicle (V2V) communications. In our work, the anti-eavesdropping scheme in CAVs networks is developed through the use of cognitive risk control (CRC)-based vehicular joint radar-communication (JRC) system. In particular, the supplement of off-board measurements acquired using V2V links to the perceptual information has presented the potential to enhance the traffic target positioning precision. Then, transmission power control is performed utilizing reinforcement learning, the result of which is determined by a task switcher. Based on the threat evaluation, a multi-armed bandit (MAB) problem is designed to implement the secret key selection procedure when it is needed. Numerical experiments have presented that the developed approach has anticipated performance in terms of some risk assessment indicators.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"13 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":"126042758","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-Fall57202.2022.10013072
Hongchao Chen, Simeng Xu, Jiajia Wang, Meifang Jing, Yuhan Hu, Yi Zhao, Xiaohui Yang
This paper investigates the optimization of phase shifts at intelligent reflecting surface (IRS)-assisted multiple input single output (MISO) systems with imperfect channel state information (CSI). By utilizing the channel statistical expressions, a closed-form ergodic achievable rate expression is derived by considering channel estimation errors of the base station (BS)-user channel, BS-IRS channel and IRS-user channel for semi-passive IRS systems in which only a portion of all IRS elements has been equipped with active sensors. We further propose to apply the genetic algorithm to tackle the system ergodic achievable rate maximization problem. Simulation results show the effectiveness of our proposed scheme.
{"title":"Imperfect CSI Based Design for Intelligent Reflecting Surface Assisted MISO Systems","authors":"Hongchao Chen, Simeng Xu, Jiajia Wang, Meifang Jing, Yuhan Hu, Yi Zhao, Xiaohui Yang","doi":"10.1109/VTC2022-Fall57202.2022.10013072","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10013072","url":null,"abstract":"This paper investigates the optimization of phase shifts at intelligent reflecting surface (IRS)-assisted multiple input single output (MISO) systems with imperfect channel state information (CSI). By utilizing the channel statistical expressions, a closed-form ergodic achievable rate expression is derived by considering channel estimation errors of the base station (BS)-user channel, BS-IRS channel and IRS-user channel for semi-passive IRS systems in which only a portion of all IRS elements has been equipped with active sensors. We further propose to apply the genetic algorithm to tackle the system ergodic achievable rate maximization problem. Simulation results show the effectiveness of our proposed scheme.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"39 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":"129383858","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-Fall57202.2022.10012988
Kaizhen Liu, Xiangning Li, Haiyang Zhao, Guoping Fan
We study the question of phase retrieval aided Millimeter wave (mmWave) channel estimation and propose a sparse phase retrieval-aided mmWave channel estimation technique which can estimate the sparse mmWave channel parameters from quadratic measurements. The proposed scheme has low-cost hardware implementation compared with traditional compressed sensing-based methods and robust to carrier frequency offset caused by high-frequency hardware imperfections. Based on the proposed sparse phase retrieval-aided model, we introduce a two-stage algorithm to estimate the mmWave channel parameters (up to a global phase) and then compute the exact solution via the anchor measurements. Simulation results are provided to illustrate the effectiveness of the proposed method.
{"title":"Structured Phase Retrieval-aided Channel Estimation for Millimeter-Wave/Sub-Terahertz MIMO Systems","authors":"Kaizhen Liu, Xiangning Li, Haiyang Zhao, Guoping Fan","doi":"10.1109/VTC2022-Fall57202.2022.10012988","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012988","url":null,"abstract":"We study the question of phase retrieval aided Millimeter wave (mmWave) channel estimation and propose a sparse phase retrieval-aided mmWave channel estimation technique which can estimate the sparse mmWave channel parameters from quadratic measurements. The proposed scheme has low-cost hardware implementation compared with traditional compressed sensing-based methods and robust to carrier frequency offset caused by high-frequency hardware imperfections. Based on the proposed sparse phase retrieval-aided model, we introduce a two-stage algorithm to estimate the mmWave channel parameters (up to a global phase) and then compute the exact solution via the anchor measurements. Simulation results are provided to illustrate the effectiveness of the proposed method.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"39 1-4 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":"129676010","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}
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