Pub Date : 2022-09-01DOI: 10.1109/VTC2022-Fall57202.2022.10012984
Shenmin Zhang, Yuan Ma, Xingjian Zhang, Jian Wang
The goal of the next-generation mobile communication system is higher data-rates, lower latency, and higher energy-efficient performance, which bring about the demands for fast beam tracking in time-varying mobile communication. With the development of large-scale antenna array technology, highly directional beams can be formed with limited radio frequency chains. However, traditional exhaustive searching scheme has unacceptable overhead that leads to great challenges for applying to mobile millimeter-wave environments. Fast beam tracking scheme therefore has been recognized as a key technology in millimeter wave communication. To address this issue, this paper proposes a data-driven multi-armed beam tracking scheme to select the beamforming/combining vectors that achieve the target quality of service based on the real-time measurement, rather than the prior knowledge such as channel and user mobility information in beamforming design. To further speed up the beam tracking process, multi-armed beam is created to sample multiple spatial directions simultaneously. Simulation results show that the proposed data-driven multi-armed beam tracking method could achieve fast beam tracking performance with high resolution and reduced training overhead.
{"title":"Data-Driven Multi-armed Beam Tracking for Mobile Millimeter-Wave Communication Systems","authors":"Shenmin Zhang, Yuan Ma, Xingjian Zhang, Jian Wang","doi":"10.1109/VTC2022-Fall57202.2022.10012984","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012984","url":null,"abstract":"The goal of the next-generation mobile communication system is higher data-rates, lower latency, and higher energy-efficient performance, which bring about the demands for fast beam tracking in time-varying mobile communication. With the development of large-scale antenna array technology, highly directional beams can be formed with limited radio frequency chains. However, traditional exhaustive searching scheme has unacceptable overhead that leads to great challenges for applying to mobile millimeter-wave environments. Fast beam tracking scheme therefore has been recognized as a key technology in millimeter wave communication. To address this issue, this paper proposes a data-driven multi-armed beam tracking scheme to select the beamforming/combining vectors that achieve the target quality of service based on the real-time measurement, rather than the prior knowledge such as channel and user mobility information in beamforming design. To further speed up the beam tracking process, multi-armed beam is created to sample multiple spatial directions simultaneously. Simulation results show that the proposed data-driven multi-armed beam tracking method could achieve fast beam tracking performance with high resolution and reduced training overhead.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"41 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":"122441230","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-Fall57202.2022.10012888
Tianyi Liao, Tianyi Zhai, Haotian Zhang, Ruijia Li, Jialing Huang, Yuxiao Li, Yinghua Wang, Jie Huang, Chenghai Wang
Industrial Internet of things (IIoT) is a typical application scenario in the sixth generation (6G) mobile networks. IIoT scenarios involve dense multipath components (MPCs) and nonnegligible scattering components caused by many moving objects. In this paper, image method (IM) is applied and extended to analyze the channel properties of IIoT. Directive model is modified to adapt to IM. The moving patterns of objects are defined and their snapshots are established along the time axis. Multiple-input multiple-output (MIMO) is supported as it is widely applied in IIoT. A smart warehouse scenario equipped with moving handcars is selected to analyze the channel of IIoT scenario. Parameters such as azimuth angle, elevation angle, angular spread, power, and delay spread of received rays are calculated and compared with those generated by quasi-deterministic (Q-D) model traditionally used in IM. Maximum and minimum Doppler shifts, received power, and delay spread are calculated along the time axis to analyze the influence of mobility to channel properties. The results show that directive model generates scattering components more realistically compared with Q-D model, and that the channel properties may experience sudden changes due to the line-of-sight (LoS) component being obstructed.
{"title":"Image Method Based 6G Channel Modeling for IIoT and Mobility Scenarios","authors":"Tianyi Liao, Tianyi Zhai, Haotian Zhang, Ruijia Li, Jialing Huang, Yuxiao Li, Yinghua Wang, Jie Huang, Chenghai Wang","doi":"10.1109/VTC2022-Fall57202.2022.10012888","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012888","url":null,"abstract":"Industrial Internet of things (IIoT) is a typical application scenario in the sixth generation (6G) mobile networks. IIoT scenarios involve dense multipath components (MPCs) and nonnegligible scattering components caused by many moving objects. In this paper, image method (IM) is applied and extended to analyze the channel properties of IIoT. Directive model is modified to adapt to IM. The moving patterns of objects are defined and their snapshots are established along the time axis. Multiple-input multiple-output (MIMO) is supported as it is widely applied in IIoT. A smart warehouse scenario equipped with moving handcars is selected to analyze the channel of IIoT scenario. Parameters such as azimuth angle, elevation angle, angular spread, power, and delay spread of received rays are calculated and compared with those generated by quasi-deterministic (Q-D) model traditionally used in IM. Maximum and minimum Doppler shifts, received power, and delay spread are calculated along the time axis to analyze the influence of mobility to channel properties. The results show that directive model generates scattering components more realistically compared with Q-D model, and that the channel properties may experience sudden changes due to the line-of-sight (LoS) component being obstructed.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"46 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":"128069566","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-Fall57202.2022.10012753
W. Piper, Hongjian Sun, Jing Jiang
Digital twins is an increasingly valuable technology for realising smart cities worldwide. Visualising this technology using mixed reality creates unprecedented opportunities to easily access relevant data and information. In this paper, a digital twins-based system is designed to visualise information from a city’s street lighting system. Data is obtained in two ways: from measured parameters of a miniature model street light in real-time, and from real Durham street lighting. Machine learning is used to maximise the efficiency of purchasing electricity from the grid, and to forecast appropriate adaptive street light brightness levels based on city’s traffic flow and solar irradiance. An application designed in Unity Pro is deployed on a Microsoft HoloLens 2, and it allows the user to view the processed data and control the model street light. It was found that the application performed as desired, displaying information such as voltage, current, carbon emission, electricity price, battery state of charge and LED mode, while enabling control over the model street light. Moreover, the Deep Q-Network machine learning algorithm successfully scheduled to buy electricity at times of low price and low carbon intensity, while the Long Short-Term Memory algorithm accurately forecasted traffic flow with mean Root-Mean-Square Error and Mean Absolute Percentage Error values of 12.0% and 20.0% respectively.
{"title":"Digital Twins for Smart Cities: Case Study and Visualisation via Mixed Reality","authors":"W. Piper, Hongjian Sun, Jing Jiang","doi":"10.1109/VTC2022-Fall57202.2022.10012753","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012753","url":null,"abstract":"Digital twins is an increasingly valuable technology for realising smart cities worldwide. Visualising this technology using mixed reality creates unprecedented opportunities to easily access relevant data and information. In this paper, a digital twins-based system is designed to visualise information from a city’s street lighting system. Data is obtained in two ways: from measured parameters of a miniature model street light in real-time, and from real Durham street lighting. Machine learning is used to maximise the efficiency of purchasing electricity from the grid, and to forecast appropriate adaptive street light brightness levels based on city’s traffic flow and solar irradiance. An application designed in Unity Pro is deployed on a Microsoft HoloLens 2, and it allows the user to view the processed data and control the model street light. It was found that the application performed as desired, displaying information such as voltage, current, carbon emission, electricity price, battery state of charge and LED mode, while enabling control over the model street light. Moreover, the Deep Q-Network machine learning algorithm successfully scheduled to buy electricity at times of low price and low carbon intensity, while the Long Short-Term Memory algorithm accurately forecasted traffic flow with mean Root-Mean-Square Error and Mean Absolute Percentage Error values of 12.0% and 20.0% respectively.","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":"132560159","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-Fall57202.2022.10012769
Eldar Gabdulsattarov, Khaled Maaiuf Rabie, Xingwang Li, G. Nauryzbayev
Quantum Annealing (QA) uses quantum fluctuations to search for a global minimum of an optimization-type problem faster than classical computer. To meet the demand for future internet traffic and mitigate the spectrum scarcity, this work presents the QA-aided maximum likelihood (ML) decoder for multi-user non-orthogonal multiple access (NOMA) networks as an alternative to the successive interference cancellation (SIC) method. The practical system parameters such as channel randomness and possible transmit power levels are taken into account for all individual signals of all involved users. The brute force (BF) and SIC signal detection methods are taken as benchmarks in the analysis. The QA-assisted ML decoder results in the same BER performance as the BF method outperforming the SIC technique, but the execution of QA takes more time than BF and SIC. The parallelization technique can be a potential aid to fasten the execution process. This will pave the way to fully realize the potential of QA decoders in NOMA systems.
{"title":"Towards Quantum Annealing for Multi-user NOMA-based Networks","authors":"Eldar Gabdulsattarov, Khaled Maaiuf Rabie, Xingwang Li, G. Nauryzbayev","doi":"10.1109/VTC2022-Fall57202.2022.10012769","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012769","url":null,"abstract":"Quantum Annealing (QA) uses quantum fluctuations to search for a global minimum of an optimization-type problem faster than classical computer. To meet the demand for future internet traffic and mitigate the spectrum scarcity, this work presents the QA-aided maximum likelihood (ML) decoder for multi-user non-orthogonal multiple access (NOMA) networks as an alternative to the successive interference cancellation (SIC) method. The practical system parameters such as channel randomness and possible transmit power levels are taken into account for all individual signals of all involved users. The brute force (BF) and SIC signal detection methods are taken as benchmarks in the analysis. The QA-assisted ML decoder results in the same BER performance as the BF method outperforming the SIC technique, but the execution of QA takes more time than BF and SIC. The parallelization technique can be a potential aid to fasten the execution process. This will pave the way to fully realize the potential of QA decoders in NOMA systems.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"48 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":"131806466","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-Fall57202.2022.10012864
Hyunsoo Kim, Y. Byun, B. Shim
With the rapid development of intelligent transportation systems (ITS), a growing number of vehicular applications have emerged to provide an entirely new experience for our daily life. To provide low-latency and high reliable services for these applications, there has been growing interest in reconfigurable intelligent surface (RIS)-aided vehicle-to-everything (V2X) systems. In this paper, we propose an entirely different deep learning (DL)-based phase shift control scheme for fast time-varying V2X channel. The proposed scheme, henceforth referred to as LSTM-based phase shift control for V2X (L-PSCV), learns temporal variation of channels from past pilot sequence and then uses them to find out the optimal phase shift for instantaneous channel. From the numerical experiments on the V2X system, we demonstrate that the proposed L-PSCV scheme outperforms the conventional schemes in terms of sum-rate.
{"title":"LSTM-based RIS Phase Shift Control for V2X Communication Systems","authors":"Hyunsoo Kim, Y. Byun, B. Shim","doi":"10.1109/VTC2022-Fall57202.2022.10012864","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012864","url":null,"abstract":"With the rapid development of intelligent transportation systems (ITS), a growing number of vehicular applications have emerged to provide an entirely new experience for our daily life. To provide low-latency and high reliable services for these applications, there has been growing interest in reconfigurable intelligent surface (RIS)-aided vehicle-to-everything (V2X) systems. In this paper, we propose an entirely different deep learning (DL)-based phase shift control scheme for fast time-varying V2X channel. The proposed scheme, henceforth referred to as LSTM-based phase shift control for V2X (L-PSCV), learns temporal variation of channels from past pilot sequence and then uses them to find out the optimal phase shift for instantaneous channel. From the numerical experiments on the V2X system, we demonstrate that the proposed L-PSCV scheme outperforms the conventional schemes in terms of sum-rate.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"135 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":"132213448","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}
The mobile crowdsensing (MCS) is an emerging sensing paradigm based on the mobile device. For location-dependent sensing tasks (LDSTs), when tasks are farther with low payment from workers, they can be difficult to complete. The completion rate of this unpopular task has always been an issue. Most existing researches mainly focus on how to increase payment for unpopular tasks, but the platform may suffer from it, because an incorrect increase results in an inability to raise the number of completed tasks. In this paper, we present a task bundling reorganized mechanism (TBRM) to improve the platform utility of MCS system. In the proposed mechanism, the unpopular and popular tasks are properly bundled to improve the platform utility. To decrease searching time for suitable bundles, two sub-policies are respectively utilized to design TBRM based on reinforcement learning: the area selection policy and the rule selection policy. Experimental results demonstrate that TBRM outperforms the three benchmark mechanisms, which reveals that TBRM can effectively bundle unpopular tasks and improve platform utility.
{"title":"Location-Dependent Task Bundling for Mobile Crowdsensing","authors":"Yan Zhen, Yunfei Wang, Peng He, Yaping Cui, Ruyang Wang, Dapeng Wu","doi":"10.1109/VTC2022-Fall57202.2022.10013041","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10013041","url":null,"abstract":"The mobile crowdsensing (MCS) is an emerging sensing paradigm based on the mobile device. For location-dependent sensing tasks (LDSTs), when tasks are farther with low payment from workers, they can be difficult to complete. The completion rate of this unpopular task has always been an issue. Most existing researches mainly focus on how to increase payment for unpopular tasks, but the platform may suffer from it, because an incorrect increase results in an inability to raise the number of completed tasks. In this paper, we present a task bundling reorganized mechanism (TBRM) to improve the platform utility of MCS system. In the proposed mechanism, the unpopular and popular tasks are properly bundled to improve the platform utility. To decrease searching time for suitable bundles, two sub-policies are respectively utilized to design TBRM based on reinforcement learning: the area selection policy and the rule selection policy. Experimental results demonstrate that TBRM outperforms the three benchmark mechanisms, which reveals that TBRM can effectively bundle unpopular tasks and improve platform utility.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"48 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":"134379502","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-Fall57202.2022.10012705
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-Fall57202.2022.10013073
Haoran Peng, Cheng-Yuan Ho, Yen-Ting Lin, Li-Chun Wang
This paper investigates the symbiotic radio (SR) system supported by reconfigurable intelligent surfaces (RIS) to provide shared spectrum. SR Stakeholders share the same infrastructure and spectrum resources, but with different quality of service (QoS) requirements. The objective of this study is to develop a low complexity and global optimization algorithm to maximize the energy efficiency (EE) of the secondary receiver (SRx) and under a required signal-to-interference-plus-noise ratio (SINR) constraint for the primary receiver (PRx). Specifically, we formulate the joint optimization of phase shift, transmission power control, and reflection element scheduling of the RIS-assisted SR system as a nonconvex mixed-integer nonlinear program (MINLP) problem. Then, we relax the nonconvex MINLP problem into an equivalent convex MINLP problem. To this end, we propose an efficient and effective method based on the accelerated generalized Benders decomposition (GBD) algorithm to solve the global-optimal and fast convergence goals. Simulation results show that the proposed GBDbased approach efficiently improves the EE by 41.94% compared to the successive convex approximation (SCA).
{"title":"Energy-Efficient Symbiotic Radio Using Generalized Benders Decomposition","authors":"Haoran Peng, Cheng-Yuan Ho, Yen-Ting Lin, Li-Chun Wang","doi":"10.1109/VTC2022-Fall57202.2022.10013073","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10013073","url":null,"abstract":"This paper investigates the symbiotic radio (SR) system supported by reconfigurable intelligent surfaces (RIS) to provide shared spectrum. SR Stakeholders share the same infrastructure and spectrum resources, but with different quality of service (QoS) requirements. The objective of this study is to develop a low complexity and global optimization algorithm to maximize the energy efficiency (EE) of the secondary receiver (SRx) and under a required signal-to-interference-plus-noise ratio (SINR) constraint for the primary receiver (PRx). Specifically, we formulate the joint optimization of phase shift, transmission power control, and reflection element scheduling of the RIS-assisted SR system as a nonconvex mixed-integer nonlinear program (MINLP) problem. Then, we relax the nonconvex MINLP problem into an equivalent convex MINLP problem. To this end, we propose an efficient and effective method based on the accelerated generalized Benders decomposition (GBD) algorithm to solve the global-optimal and fast convergence goals. Simulation results show that the proposed GBDbased approach efficiently improves the EE by 41.94% compared to the successive convex approximation (SCA).","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"49 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":"114471903","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-Fall57202.2022.10012863
Y. Shi, Xixi Zhang, Zhengran He, Jie Yang
The application of deep learning (DL) in the field of network intrusion detection (NID) has yielded remarkable results in recent years. As for malicious traffic classification tasks, numerous DL methods have proved robust and effective with self-designed model architecture. However, the design of model architecture requires substantial professional knowledge and effort of human experts. Neural architecture search (NAS) can automatically search the architecture of the model under the premise of a given optimization goal, which is a subdomain of automatic machine learning (AutoML). After that, Differentiable Architecture Search (DARTS) has been proposed by formulating architecture search in a differentiable manner, which greatly improves the search efficiency. In this paper, we introduce a model which performs DARTS in the field of malicious traffic classification and search for optimal architecture based on network traffic datasets. In addition, we compare the DARTS method with several common models, including convolutional neural network (CNN), full connect neural network (FC), support vector machine (SVM), and multi-layer Perception (MLP). Simulation results show that the proposed method can achieve the optimal classification accuracy at lower parameters without manual architecture engineering.
{"title":"A Novel Malware Traffic Classification Method Based on Differentiable Architecture Search","authors":"Y. Shi, Xixi Zhang, Zhengran He, Jie Yang","doi":"10.1109/VTC2022-Fall57202.2022.10012863","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012863","url":null,"abstract":"The application of deep learning (DL) in the field of network intrusion detection (NID) has yielded remarkable results in recent years. As for malicious traffic classification tasks, numerous DL methods have proved robust and effective with self-designed model architecture. However, the design of model architecture requires substantial professional knowledge and effort of human experts. Neural architecture search (NAS) can automatically search the architecture of the model under the premise of a given optimization goal, which is a subdomain of automatic machine learning (AutoML). After that, Differentiable Architecture Search (DARTS) has been proposed by formulating architecture search in a differentiable manner, which greatly improves the search efficiency. In this paper, we introduce a model which performs DARTS in the field of malicious traffic classification and search for optimal architecture based on network traffic datasets. In addition, we compare the DARTS method with several common models, including convolutional neural network (CNN), full connect neural network (FC), support vector machine (SVM), and multi-layer Perception (MLP). Simulation results show that the proposed method can achieve the optimal classification accuracy at lower parameters without manual architecture engineering.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"58 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":"117253262","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：