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  1. Article ; Online: Deep Reinforcement Learning-Based Resource Allocation for Cellular Vehicular Network Mode 3 with Underlay Approach.

    Fu, Jinjuan / Qin, Xizhong / Huang, Yan / Tang, Li / Liu, Yan

    Sensors (Basel, Switzerland)

    2022  Volume 22, Issue 5

    Abstract: Vehicle-to-vehicle (V2V) communication has attracted increasing attention since it can improve road safety and traffic efficiency. In the underlay approach of mode 3, the V2V links need to reuse the spectrum resources preoccupied with vehicle-to- ... ...

    Abstract Vehicle-to-vehicle (V2V) communication has attracted increasing attention since it can improve road safety and traffic efficiency. In the underlay approach of mode 3, the V2V links need to reuse the spectrum resources preoccupied with vehicle-to-infrastructure (V2I) links, which will interfere with the V2I links. Therefore, how to allocate wireless resources flexibly and improve the throughput of the V2I links while meeting the low latency requirements of the V2V links needs to be determined. This paper proposes a V2V resource allocation framework based on deep reinforcement learning. The base station (BS) uses a double deep Q network to allocate resources intelligently. In particular, to reduce the signaling overhead for the BS to acquire channel state information (CSI) in mode 3, the BS optimizes the resource allocation strategy based on partial CSI in the framework of this article. The simulation results indicate that the proposed scheme can meet the low latency requirements of V2V links while increasing the capacity of the V2I links compared with the other methods. In addition, the proposed partial CSI design has comparable performance to complete CSI.
    MeSH term(s) Resource Allocation
    Language English
    Publishing date 2022-02-27
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s22051874
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: STSGAN: Spatial-Temporal Global Semantic Graph Attention Convolution Networks for Urban Flow Prediction

    Zhou, Junwei / Qin, Xizhong / Yu, Kun / Jia, Zhenhong / Du, Yan

    ISPRS international journal of geo-information. 2022 July 08, v. 11, no. 7

    2022  

    Abstract: Accurate urban traffic flow prediction plays a vital role in Intelligent Transportation System (ITS). The complex long-term and long-range spatiotemporal correlations of traffic flow pose a significant challenge to the prediction task. Most current ... ...

    Abstract Accurate urban traffic flow prediction plays a vital role in Intelligent Transportation System (ITS). The complex long-term and long-range spatiotemporal correlations of traffic flow pose a significant challenge to the prediction task. Most current research methods focus only on spatial correlations in local areas, ignoring global geographic contextual information. It is challenging to capture spatial information from distant nodes using shallow graph neural networks (GNNs) to model long-range spatial correlations. To handle this problem, we design a novel spatiotemporal global semantic graph-attentive convolutional network model (STSGAN), which is a deep-level network to achieve the simultaneous modelling of spatiotemporal correlations. First, we propose a graph-attentive convolutional network (GACN) to extract the importance of different spatial features and learn the spatial correlation of local regions and the global spatial semantic information. The temporal causal convolution structure (TCN) is utilized to capture the causal relationships between long-short times, thus enabling an integrated consideration of local and overall spatiotemporal correlations. Several experiments are conducted on two real-world traffic flow datasets, and the results show that our approach outperforms several state-of-the-art baselines.
    Keywords data collection ; prediction ; spatial data ; traffic
    Language English
    Dates of publication 2022-0708
    Publishing place Multidisciplinary Digital Publishing Institute
    Document type Article
    ZDB-ID 2655790-3
    ISSN 2220-9964
    ISSN 2220-9964
    DOI 10.3390/ijgi11070381
    Database NAL-Catalogue (AGRICOLA)

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  3. Article ; Online: Cross-Attention Fusion Based Spatial-Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction.

    Yu, Kun / Qin, Xizhong / Jia, Zhenhong / Du, Yan / Lin, Mengmeng

    Sensors (Basel, Switzerland)

    2021  Volume 21, Issue 24

    Abstract: Accurate traffic flow prediction is essential to building a smart transportation city. Existing research mainly uses a given single-graph structure as a model, only considers local and static spatial dependencies, and ignores the impact of dynamic spatio- ...

    Abstract Accurate traffic flow prediction is essential to building a smart transportation city. Existing research mainly uses a given single-graph structure as a model, only considers local and static spatial dependencies, and ignores the impact of dynamic spatio-temporal data diversity. To fully capture the characteristics of spatio-temporal data diversity, this paper proposes a cross-Attention Fusion Based Spatial-Temporal Multi-Graph Convolutional Network (CAFMGCN) model for traffic flow prediction. First, introduce GCN to model the historical traffic data's three-time attributes (current, daily, and weekly) to extract time features. Second, consider the relationship between distance and traffic flow, constructing adjacency, connectivity, and regional similarity graphs to capture dynamic spatial topology information. To make full use of global information, a cross-attention mechanism is introduced to fuse temporal and spatial features separately to reduce prediction errors. Finally, the CAFMGCN model is evaluated, and the experimental results show that the prediction of this model is more accurate and effective than the baseline of other models.
    Language English
    Publishing date 2021-12-18
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s21248468
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

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