Learning-Based Resource Allocation for Ultra-Reliable V2X Networks With Partial CSI

Chai, Guanhua; Wu, Weihua; Yang, Qinghai; Liu, Runzi; Yu, F. Richard

doi:10.1109/tcomm.2022.3199018

Cited by 13 publications

(2 citation statements)

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“…In ref. [64], the authors study the resource allocation in high mobility V2X networks with only slowly varying large‐scale channel parameters. The goal of this proposed scheme is to minimise the delay of V2I links whilst satisfying the V2V reliability constraint.…”

Section: Radio Resource Allocation Classification Based On the Mode R...mentioning

confidence: 99%

Resource allocation for Vehicle‐to‐Everything communications: A survey

2022

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The Deployment of new techniques to accommodate high data throughput, significant reliability, and quality of service (QoS) delivery prompts the use of 4G and 5G networks in several applications. In these networks, Vehicle‐to‐Everything (V2X) communications authorised by device‐to‐device cellular links (D2D) have gained momentum because of their potential to improve road safety, efficiency, and convenience. This improvement is to be provided by different types of V2X communications, including Vehicle‐to‐Pedestrian (V2P), Vehicle‐to‐Vehicle (V2V), Vehicle‐to‐Infrastructure (V2I), and Vehicle‐to‐Network (V2N) communications. However, considering several limiting factors such as mobility and increasing number of vehicles, efficiency of using wireless resources is a challenging task. Indeed, different research works on V2X radio resource allocation have been studied and evaluated with respect to several criteria. In this study, a survey of recent works but in a different way synthesising radio resource allocation methods is presented. Resource allocation classification into different categories is proposed. This classification is related to three main factors. The first takes into account the selection mode factor. It highlights the use of different approaches in each of the centralised, distributed, and mixed modes. The second one takes into account the considered performance criteria, including capacity, priority, delay etc. The last one focuses on the methods and scenarios used to evaluate a resource allocation approach.

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Section: Radio Resource Allocation Classification Based On the Mode R...mentioning

confidence: 99%

Resource allocation for Vehicle‐to‐Everything communications: A survey

2022

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“…The models show a performance enhancement of the V2V communications in 5G NR networks. In [10], the authors have proposed a V2X resource allocation scheme using machine learning. They presented a joint power, spectrum, and local computing ratio allocation problem with partial CSI and offered a solution that minimizes V2I link delay and satisfies the V2V reliability constraint.…”

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confidence: 99%

A Deep Reinforcement Learning-Based Two-Dimensional Resource Allocation Technique for V2I Communications

et al. 2023

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This paper proposes a two-dimensional resource allocation technique for vehicle-toinfrastructure (V2I) communications. Vehicular communications requires high data rates, low latency, and reliability simultaneously. The 3rd generation partnership project (3GPP) included various numerologies to support this, leading to diversification of transmit time interval (TTI). It enables the two-dimensional resource allocation that considers time and frequency simultaneously, which has yet to be studied much. To tackle this issue, we propose a reinforcement learning approach to solve the two-dimensional resource allocation problem for V2I communications. A reinforcement learning agent in a base station allocates a quality of service (QoS) guaranteed two-dimensional resource block to each vehicle to maximize the sum of achievable data quantity (ADQ). It exploits received power information and a resource occupancy status as input. It outputs vehicles' allocation information that consists of a time-frequency position, bandwidth, and TTI, which is a solution to the two-dimensional resource allocation. The simulation results show that the proposed method outperforms the fixed allocation method. Because of the ability to pursue ADQ maximization and QoS guarantee, the proposed method performs better than an optimization-based benchmark method if each vehicle has a QoS constraint. Also, we can see that the resource the agent selects according to the QoS constraint varies and maximizes the ADQ.INDEX TERMS Deep reinforcement learning, V2X communications, quality of service, resource allocation. I. INTRODUCTIONWith the advent of complex applications that combine high data rates, low latency, or high reliability, discussions on next-generation communication networks have been actively conducted to support them. The international telecommunication union (ITU) radiocommunication sector has defined three service types to meet the requirements of new applications: enhanced mobile broadband (eMBB) for applications requiring high data rates, massive machine-type communications (mMTC) for applications requiring high-The associate editor coordinating the review of this manuscript and approving it for publication was Hao Wang . density networks, and ultra-reliable low-latency communications (URLLC) for latency-sensitive applications. Vehicleto-everything (V2X) communications is a highly complex application requiring all three service types. It consists of several vehicle-related communications, such as vehicle-toinfrastructure (V2I) and vehicle-to-vehicle (V2V) communications. Vehicles utilize V2V communications for direct information exchange between themselves and V2I communications to convey information to the infrastructure such as base stations or roadside units (RSUs) and vice versa [1], [2], [3], [4].Early vehicular communications focused on collision avoidance to reduce car crashes. They need delay-sensitive

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