A Latency and Reliability Guaranteed Resource Allocation Scheme for LTE V2V Communication Systems

By Jie Mei, Kan Zheng, Long Zhao, Yong Teng, Xianbin Wang. 2018

154 citations recorded by the source corpus.

By leveraging direct device-to-device interaction, LTE vehicle-to-vehicle (V2V) communication becomes a promising solution to meet the stringent requirements of vehicular communication. In this paper, we propose jointly optimizing the radio resource, power allocation, and modulation/coding schemes of the V2V communications, in order to guarantee the latency and reliability requirements of vehicular user equipments (VUEs) while maximizing the information rate of cellular user equipment (CUE). To ensure the solvability of this optimization problem, the packet latency constraint is first transformed into a data rate constraint based on random network analysis by adopting the Poisson distribution model for the packet arrival process of each VUE. Then, utilizing the Lagrange dual decomposition and binary search, a resource management algorithm is proposed to find the optimal solution of joint optimization problem with reasonable complexity. Simulation results show that the proposed radio resource management scheme can reduce the interference from V2V communication to CUEs and ensure the latency and reliability requirements of V2V communication.

Subfields: Electrical and Electronic Engineering

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Citations in the corpus

Federated Learning for Ultra-Reliable Low-Latency V2V Communications (2018, background): “Towards this end, the majority of existing works including [8], [9] and [10] focus on improving the average latency of vehicular networks while a handful such as [1] and [2] impose a probabilistic constraint to maintain small queue lengths.”
Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications (2018, background): “…existing RRM techniques have been proposed for enabling ultra-reliable low-latency vehicular communications while factoring in several challenges such as rate maximization, delay minimization, improving energy-efﬁciency, energy saving, and vehicle clustering/platooning [1], [4]–[6], [13]– [21].”
Challenges and Solutions for Cellular Based V2X Communications (2021, background): “Similar to [38] and [39], the proposed scheme leads to the poor spectrum utilization as a spectrum of one cellular user equipment can be shared with the only single device user equipment.”
Ultra-Reliable Low-Latency Vehicular Networks: Taming the Age of Information Tail (2018, background): “Remarkably, the majority of the existing V2V literature does not address latency and reliability, focusing only on average performance metrics [6]–[9], which is not sufficient to enable URLLC.”
Intelligent Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond: A Deep Reinforcement Learning Based Approach (2020, background): “In [9], the authors studied the joint optimization of radio resources, power control, and modulation schemes of the V2V communications while guarantying the latency and reliability requirements of vehicular users and maximizing the rate of cellular users.”