Distance-Based Resource Allocation for Vehicle-to-Pedestrian Safety Communication

Cellular Vehicle to Everything (V2X) has redefined the vehicular communication architecture as something that needs an ultra-reliable link, high capacity, and fast message delivery in vehicular networks. The V2X scenarios are broadly categorized as Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), Vehicle to Pedestrians (V2P), and Vehicle to Network (V2N). Vulnerable pedestrians belong to the V2P category and hence require an ultra-reliable link and a fast message delivery in case the moving vehicle is in the close proximity of the pedestrian. However, congestion in the network calls for an optimized resource allocation that would allow a fast and secure connection between a vehicle and the pedestrian. In this paper, we have proposed a distance-based resource allocation that classifies the pedestrians in different categories, performs a one-to-many weighted bipartite matching, and finally a reinforcement learning based power allocation.

[1]  Zhi Ding,et al.  Graph-Based Resource Sharing in Vehicular Communication , 2018, IEEE Transactions on Wireless Communications.

[2]  Xiang Cheng,et al.  Interference Hypergraph-Based 3D Matching Resource Allocation Protocol for NOMA-V2X Networks , 2019, IEEE Access.

[3]  Jochen Seitz,et al.  Vehicle-to-Pedestrian Communication for Vulnerable Road Users: Survey, Design Considerations, and Challenges , 2019, Sensors.

[4]  Geoffrey Ye Li,et al.  Deep Reinforcement Learning Based Resource Allocation for V2V Communications , 2018, IEEE Transactions on Vehicular Technology.

[5]  Lajos Hanzo,et al.  Twin-Timescale Radio Resource Management for Ultra-Reliable and Low-Latency Vehicular Networks , 2019, IEEE Transactions on Vehicular Technology.

[6]  Geoffrey Ye Li,et al.  Machine Learning for Vehicular Networks: Recent Advances and Application Examples , 2018, IEEE Vehicular Technology Magazine.

[7]  Pingzhi Fan,et al.  A Novel Low-Latency V2V Resource Allocation Scheme Based on Cellular V2X Communications , 2019, IEEE Transactions on Intelligent Transportation Systems.

[8]  Usman Ali Khan,et al.  Three-Dimensional Resource Allocation in D2D-Based V2V Communication , 2019, Electronics.

[9]  Kais Mnif,et al.  A Survey on Radio Resource Allocation for V2X Communication , 2019, Wirel. Commun. Mob. Comput..

[10]  Xujie Li,et al.  A Resource Allocation Scheme Based on Immune Algorithm for D2D-Based Vehicular Communication Networks , 2019, IEEE Access.

[11]  Hend Koubaa,et al.  Radio resource management for vehicular communication via cellular device to device links: review and challenges , 2020, Telecommun. Syst..

[12]  Sukumar Nandi,et al.  A tutorial survey on vehicular communication state of the art, and future research directions , 2019, Veh. Commun..

[13]  Geoffrey Ye Li,et al.  Resource Allocation for High-Reliability Low-Latency Vehicular Communications With Packet Retransmission , 2019, IEEE Transactions on Vehicular Technology.

[14]  Gang Wu,et al.  Joint Resource Allocation and Trajectory Control for UAV-Enabled Vehicular Communications , 2019, IEEE Access.

[15]  Long D. Nguyen,et al.  Distributed Deep Deterministic Policy Gradient for Power Allocation Control in D2D-Based V2V Communications , 2019, IEEE Access.

[16]  Yan Shi,et al.  A Vision of C-V2X: Technologies, Field Testing, and Challenges With Chinese Development , 2020, IEEE Internet of Things Journal.

[17]  Walid Saad,et al.  Matching theory for future wireless networks: fundamentals and applications , 2014, IEEE Communications Magazine.

[18]  Marko Höyhtyä,et al.  Review of Latest Advances in 3GPP Standardization: D2D Communication in 5G Systems and Its Energy Consumption Models , 2018, Future Internet.

[19]  Cheng Zhang,et al.  Resource allocation for virtual reality content sharing based on 5G D2D multicast communication , 2020, EURASIP J. Wirel. Commun. Netw..

[20]  Anthony T. Chronopoulos,et al.  Power Allocation With Energy Efficiency Optimization in Cellular D2D-Based V2X Communication Network , 2020, IEEE Transactions on Intelligent Transportation Systems.

[21]  Tomás de Jesús Mateo Sanguino,et al.  Review on V2X, I2X, and P2X Communications and Their Applications: A Comprehensive Analysis over Time , 2019, Sensors.

[22]  Geoffrey Ye Li,et al.  Resource Allocation for D2D-Enabled Vehicular Communications , 2017, IEEE Transactions on Communications.

[23]  Zheng Li,et al.  Multi-Agent Deep Reinforcement Learning Based Spectrum Allocation for D2D Underlay Communications , 2019, IEEE Transactions on Vehicular Technology.