2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)最新文献

Layer-1 Mobility in Distributed MIMO with Non-Coherent Joint Transmission 非相干联合传输分布式MIMO中的第一层移动性

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012990

Peng Lin, Omer Haliloglu

{"title":"Layer-1 Mobility in Distributed MIMO with Non-Coherent Joint Transmission","authors":"Peng Lin, Omer Haliloglu","doi":"10.1109/VTC2022-Fall57202.2022.10012990","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012990","url":null,"abstract":"The Distributed Multiple Input Multiple Output (D-MIMO) network comprises a very large number of distributed Radio Units (RUs), which simultaneously serve multiple User Equipments (UEs) over the same time/frequency resources based on directly measured channel characteristics. Existing research had shown that Coherent Joint Transmission (CJT) in D-MIMO networks could obtain better performance compared to the traditional small cell and cellular massive MIMO network through multiple RUs. Nonetheless, reliable access links become more important at high frequency bands and mobility scenarios that needs robust precoding schemes to utilize the full performance of a D-MIMO network. In this paper, Physical layer (L1) mobility is incorporated in D-MIMO network operating at mmWave. Then centralized and distributed precoding methods are considered to evaluate the spectral efficiencies of mobile UEs with different serving RU subset update periodicities. Moreover, Non-Coherent Joint Transmission (NCJT) among multiple RUs is explored. Through the simulation results, it is shown that serving RU subset (cluster) update and NCJT substantially impact the performance. During UE mobility, frequent serving subset update is necessary for CJT, however, not critical for NCJT.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"24 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":"124440383","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}

引用次数: 1

Federated Deep Reinforcement Learning for THz-Beam Search with Limited CSI 有限CSI下太赫兹波束搜索的联合深度强化学习

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012887

Po-chun Hsu, Li-Hsiang Shen, Chun-Hung Liu, Kai-Ten Feng

{"title":"Federated Deep Reinforcement Learning for THz-Beam Search with Limited CSI","authors":"Po-chun Hsu, Li-Hsiang Shen, Chun-Hung Liu, Kai-Ten Feng","doi":"10.1109/VTC2022-Fall57202.2022.10012887","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012887","url":null,"abstract":"Terahertz (THz) communication with ultra-wide available spectrum is a promising technique that can achieve the stringent requirement of high data rate in the next-generation wireless networks, yet its severe propagation attenuation significantly hinders its implementation in practice. Finding beam directions for a large-scale antenna array to effectively overcome severe propagation attenuation of THz signals is a pressing need. This paper proposes a novel approach of federated deep reinforcement learning (FDRL) to swiftly perform THz-beam search for multiple base stations (BSs) coordinated by an edge server in a cellular network. All the BSs conduct deep deterministic policy gradient (DDPG)-based DRL to obtain THz beamforming policy with limited channel state information (CSI). They update their DDPG models with hidden information in order to mitigate inter-cell interference. We demonstrate that the cell network can achieve higher throughput as more THz CSI and hidden neurons of DDPG are adopted. We also show that FDRL with partial model update is able to nearly achieve the same performance of FDRL with full model update, which indicates an effective means to reduce communication load between the edge server and the BSs by partial model uploading. Moreover, the proposed FDRL outperforms conventional non-learning-based and existing non-FDRL benchmark optimization methods.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"22 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":"125087967","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}

引用次数: 3

Learning based Delay-Doppler Channel Estimation with Interleaved Pilots in OTFS 基于学习的OTFS交错导频延时多普勒信道估计

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012974

Sandesh Rao Mattu, A. Chockalingam

{"title":"Learning based Delay-Doppler Channel Estimation with Interleaved Pilots in OTFS","authors":"Sandesh Rao Mattu, A. Chockalingam","doi":"10.1109/VTC2022-Fall57202.2022.10012974","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012974","url":null,"abstract":"Traditionally, channel estimation in orthogonal time frequency space (OTFS) is carried out in the delay-Doppler (DD) domain by placing pilot symbols surrounded by guard bins in the DD grid. This results in reduced spectral efficiency as the guard bins do not carry information. In the absence of guard bins, there is leakage from pilot symbols to data symbols and vice versa. Therefore, in this paper, we consider an interleaved pilot (IP) placement scheme with a lattice-type arrangement (which does not have guard bins) and propose a deep learning architecture using recurrent neural networks (referred to as IPNet) for efficient estimation of DD domain channel state information. The proposed IPNet is trained to overcome the effects of leakage from data symbols and provide channel estimates with good accuracy (e.g., the proposed scheme achieves a normalized mean square error of about 0.01 at a pilot SNR of 25 dB). Our simulation results also show that the proposed IPNet architecture achieves good bit error performance while being spectrally efficient. For example, the proposed scheme uses 12 overhead bins (12 pilot bins and no guard bins) for channel estimation in a considered frame while the embedded pilot scheme uses 25 overhead bins (1 pilot bin and 24 guard bins).","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"32 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":"117023494","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}

引用次数: 1

Rate Loss due to Beam Cusping in Grid of Beams 梁栅中波束凹点引起的速率损失

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012805

K. K. Tiwari, G. Caire

{"title":"Rate Loss due to Beam Cusping in Grid of Beams","authors":"K. K. Tiwari, G. Caire","doi":"10.1109/VTC2022-Fall57202.2022.10012805","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012805","url":null,"abstract":"We present mean communication rate loss (MCRL) values due to the beam cusping phenomenon inherent to grid of beams based wireless systems, which are widely used/proposed in millimeter-wave and sub-THz bands. We consider array antenna elements with non-zero aperture and axisymmetric radiation pattern with a certain directivity, unlike the ideal isotropic antenna elements widely considered in theoretical papers on array processing and RF beamforming. The array antenna elements have a half power beam width of ninety degrees in this work which is typical of commonly used microstrip patch antenna elements. We perform Monte Carlo numerical experiments to obtain MCRL values for different spatial dimensions of multiple input multiple output (MIMO) systems, different angular fields of view, and different beamforming codebook sizes. We show that increasing the beam grid density beyond a certain threshold does not help and therefore a certain cusping loss is unavoidable even for continuous beam steering with directive antenna elements. Further, we also show the quantitative impact on the MCRL values as the axisymmetric radiation pattern directivity of the modelled antenna element is increased.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"69 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":"129565023","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}

引用次数: 1

Deep Reinforcement Learning-Based Routing for Space-Terrestrial Networks 基于深度强化学习的地空网络路由

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10013028

Kai-Chu Tsai, Ting-Jui Yao, Pingmu Huang, Cheng-Sen Huang, Zhu Han, Li-Chun Wang

{"title":"Deep Reinforcement Learning-Based Routing for Space-Terrestrial Networks","authors":"Kai-Chu Tsai, Ting-Jui Yao, Pingmu Huang, Cheng-Sen Huang, Zhu Han, Li-Chun Wang","doi":"10.1109/VTC2022-Fall57202.2022.10013028","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10013028","url":null,"abstract":"Satellite communication is one primary structure of the future wireless systems. Many corporations and academic institutions are committed to this research, e.g., StarLink. Their aim is to construct a constellation of satellite networks covering the whole world. Thanks to this design, our mobile devices can connect to the Internet anywhere in the world without being restricted by the coverage area of base stations. However, given that the constellation of satellites around the Earth is a system that varies in time, algorithms must adapt to the dynamic topology. To solve the problem, in this paper we investigate how to send data from the base station to the target satellite with minimal delay. We systematically formulate the transmission limitations, uplink, and downlink transmission delay. With well defined limits and delays on space-terrestrial transmissions, we can set rewards and punishments by mathematical analysis to simulate the agent in deep reinforcement learning (DRL), which can explore all choices in the system and optimize the routing algorithm. Finally, the simulation results corroborate a fully functional space-terrestrial network constellation to simulate the actual situation.","PeriodicalId":326047,"journal":{"name":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","volume":"174 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":"128351670","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}

引用次数: 2

Learning-Based Path Loss Estimation Using Multiple Spatial Data and System Parameters 基于多空间数据和系统参数的学习路径损失估计

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012870

Kazuya Inoue, Keita Imaizumi, K. Ichige, Tatsuya Nagao, Takahiro Hayashi

引用次数: 1

A Two-Factor Authentication Scheme for Moving Connected Vehicles 移动互联车辆的双因素认证方案

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012773

Dajiang Suo, S. Sarma

{"title":"A Two-Factor Authentication Scheme for Moving Connected Vehicles","authors":"Dajiang Suo, S. Sarma","doi":"10.1109/VTC2022-Fall57202.2022.10012773","DOIUrl":"https://doi.org/10.1109/VTC2022-Fall57202.2022.10012773","url":null,"abstract":"A roadside adversary who holds compromised vehicle-to-everything (V2X) credentials can easily spoof vehicle identities and broadcast fabricated messages that jeopardize the maneuvers of surrounding vehicles. Previous work on the security of ad hoc networks suggests the use of a side channel for two parties to exchange digital certificates to prevent impersonation and man-in-the-middle attacks on the main wireless channel. This paper presents a two-factor authentication scheme by leveraging line-of-sight (LOS) communication as the side channel to impede roadside adversaries who try to impersonate legitimate moving vehicles in the non-line-of-sight (NLOS) channel. To gain the trust of other traffic participants, a vehicle that has received a challenge message broadcast by infrastructure through the main (NLOS) wireless channel must send back its response through the LOS channel to demonstrate it is indeed a vehicle in traffic. The directional property and visual confirmation of the LOS channel and the fact that vehicle movement is ascertained based on physics make it extremely difficult for the roadside adversary to finish the response-challenge process without being detected. Experimental results demonstrate the feasibility of using the proposed scheme for authenticating low-speed vehicles. However, for authenticating vehicles traveling at high speed, transmitting the response message containing certificates through the LOS channel can create a communication bottleneck for the authentication process, although implicit certificates can be adopted to reduce the total authentication time. Future work will explore the alternative format of the challenge-response protocol and the potential technologies for realizing LOS communication to reduce the communication bottleneck.","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":"128733803","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}

引用次数: 1

Research, Implementation and Practice of Congestion Control Mechanism in LTE-V2X LTE-V2X中拥塞控制机制的研究、实现与实践

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012979

Jin-Ling Hu, Li Zhao, Yuan Feng, Yinghao Liu, Mingjun Gao

引用次数: 0

A Novel Radio Frequency Fingerprint Identification Method Using Incremental Learning 一种基于增量学习的射频指纹识别方法

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012703

Jie Zhou, Yang Peng, Guan Gui, Yun Lin, B. Adebisi, H. Gačanin, H. Sari

引用次数: 1

An Effective Radio Frequency Signal Classification Method Based on Multi-Task Learning Mechanism 一种基于多任务学习机制的射频信号分类方法

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Pub Date : 2022-09-01 DOI: 10.1109/VTC2022-Fall57202.2022.10012794

Hongzhi Liu, Chengyao Hao, Yang Peng, Yu Wang, T. Ohtsuki, Guan Gui

引用次数: 1