A Mixed Traffic Sharing and Resource Allocation for V2X Communication

Masmoudi, Ahlem; Feki, S.; Mnif, Kais; Zarai, Faouzi

doi:10.1007/978-3-030-34866-3_11

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…Therefore, power control is essential for optimizing the performance of vehicular communication networks. Many researchers have committed to solving spectrum allocation [4,5] or power control [6] problems in vehicular communications, but these schemes ignore that they both will have an impact on network performance at the same time. Therefore, joint spectrum allocation and power control is essentail to improve network performance in vehicular communications.…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Joint Spectrum Allocation and Power Control in Vehicular Communications Based on Dueling Double DQN

Ren

Chai

2022

Preprint

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Vehicular communications is one of the important applications in the low-latency and high-reliability scenarios of 5G communication systems. To meet the quality of service (QoS) requirements of different vehicle links in a dynamic environment and improve the transmission effectiveness of the communication system, a distributed Vehicle-to-Vehicle (V2V) spectrum allocation and power control scheme based on the Deep Reinforcement Learning (DRL) framework is proposed. First, the latency constraint of the V2V link is formulated as a reward function, and a positive reward is given when the constraint is not violated; then, each V2V link is regarded as an agent to realize distributed spectrum and transmission power allocation to ensure the minimum transmission overhead. We use Double Deep Q-learning (DDQN) with dueling architecture to find the mapping between the observed environment and the optimal resource allocation scheme. Compared with other algorithms, the proposed algorithm can learn to meet the strict latency constraints on the V2V link, and can effectively improve the network capacity of the Vehicle to Infrastructure (V2I) link in vehicular communications.

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Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Joint Spectrum Allocation and Power Control in Vehicular Communications Based on Dueling Double DQN

Ren

Chai

2022

Preprint

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“… Resource utilization ratio is the ratio of allocated RBs to the total RBs. It is calculated as follows:

Resource utilization ratio goodbreak= \frac{number of allocated RBs}{Total number of RBs}

The performance of our solution is compared to approaches already proposed in Masmoudi et al 5 and Zeki et al 16 in terms of network sum rate, PRR, resource utilization ratio, time complexity, and execution time.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…The performance of our solution is compared to approaches already proposed in Masmoudi et al 5 and Zeki et al 16 in terms of network sum rate, PRR, resource utilization ratio, time complexity, and execution time.…”

Section: Performance Metrics and Compared Schemesmentioning

confidence: 99%

Multi‐layer neural network algorithm for vehicle‐to‐everything communication in 5G networks

Feki

Hamdi

Belghith

et al. 2022

Int J Communication

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Internet of Vehicles (IoVs), the emerging trend of Internet of Things (IoTs), has undoubtedly become a promising trend to improve communication among vehicles on the roads. Vehicle-to-everything (V2X) communication that is based on 5G technology enables vehicle users to communicate and collaborate with each other to enhance road traffic efficiency and safety. Owing to the increased traffic load and restricted resources of existing network substructure, a channel that responds to the latency and reliability needs of V2X communication must be designed. Thereby, several intelligent spectrum allocation techniques have been proposed to improve the system's overall effectiveness. In this paper, we discuss the spectrum sharing issue of V2X communication in Device-to-Device (D2D)-based cellular networks. We propose a new multilayer neural network (MLNN)-based Resource Allocation and sharing approach (MNNRA) for D2D-based V2X communications. According to the main advantages of MLNN, the proposed algorithm takes several profits by improving system performance while reducing computational complexity. Numerical analysis is presented to approve the effectiveness of our proposed solution in terms of network sum rate, packet reception ratio, resource utilization ratio, and time complexity.

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“…From the perspective of resource optimization, some studies on cellular V2X communications also exist where the authors considered a unicast communication paradigm for guaranteeing the sum-rate for V2X communication [16]. Similarly, the authors of [17] proposed a resource allocation solution to maximize the service rate of the network users while Masmoudi et al reduces the latency of V2X communication to support driver safety applications [18].…”

Section: A Related Workmentioning

confidence: 99%

Efficient Mining Cluster Selection for Blockchain-Based Cellular V2X Communications

Jameel

Javed

Zeadally

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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Cellular vehicle-to-everything (V2X) communication is expected to herald the age of autonomous vehicles in the coming years. With the integration of blockchain in such networks, information of all granularity levels, from complete blocks to individual transactions, would be accessible to vehicles at any time. Specifically, the blockchain technology is expected to improve the security, immutability, and decentralization of cellular V2X communication through smart contract and distributed ledgers. Although blockchain-based cellular V2X networks hold promise, many challenges need to be addressed to enable the future interoperability and accessibility of such large-scale platforms. One such challenge is the offloading of mining tasks in cellular V2X networks. While transportation authorities may try to balance the network mining load, the vehicles may select the nearest mining clusters to offload a task. This may cause congestion and disproportionate use of vehicular network resources. To address this issue, we propose a game-theoretic approach for balancing the load at mining clusters while maintaining fairness among offloading vehicles. Keeping in mind the low-latency requirements of vehicles, we consider a finite channel blocklength transmission which is more practical compared to the use of infinite blocklength codes. The simulation results obtained with our proposed offloading framework show improved performance over the conventional nearest mining cluster selection technique.

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A Mixed Traffic Sharing and Resource Allocation for V2X Communication

Cited by 7 publications

References 15 publications

WITHDRAWN: Joint Spectrum Allocation and Power Control in Vehicular Communications Based on Dueling Double DQN

WITHDRAWN: Joint Spectrum Allocation and Power Control in Vehicular Communications Based on Dueling Double DQN

Multi‐layer neural network algorithm for vehicle‐to‐everything communication in 5G networks

Efficient Mining Cluster Selection for Blockchain-Based Cellular V2X Communications

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