Resource Allocation Modes in C-V2X: From LTE-V2X to 5G-V2X

Khabaz, Sehla; Nguyen, Thi Mai Trang; Pujolle, Guy; Velloso, Pedro B.

doi:10.1109/jiot.2022.3159591

Cited by 104 publications

(39 citation statements)

References 68 publications

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“…Furthermore, SIC technique is performed in the PD-NOMA system to reduce interference among users [33], [34]. To develop this framework, we present two approaches [35].…”

Section: B Contributionmentioning

confidence: 99%

Distributed Learning-Based Resource Allocation for Self-Organizing C-V2X Communication in Cellular Networks

Banitalebi

Azmi

Mokari

et al. 2022

IEEE Open J. Commun. Soc.

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In this paper, we investigate a resource allocation problem for a Cellular Vehicle to Everything (C-V2X) network to improve energy efficiency of the system. To address this problem, self-organizing mechanisms are proposed for joint and disjoint subcarrier and power allocation procedures which are performed in a fully distributed manner. A multi-agent Q-learning algorithm is proposed for the joint power and subcarrier allocation. In addition, for the sake of simplicity, it is decoupled into two sub-problems: a subcarrier allocation sub-problem and a power allocation sub-problem. First, to allocate the subcarrier among users, a distributed Q-learning method is proposed. Then, given the optimal subcarriers, a dynamic power allocation mechanism is proposed where the problem is modeled as a non-cooperative game. To solve the problem, a no-regret learning algorithm is utilized. To evaluate the performance of the proposed approaches, other learning mechanisms are used which are presented in fig. 8. Simulation results show the multi-agent joint Q-learning algorithm yields significant performance gains of up to about 11% and 18% in terms of energy efficiency compared to proposed disjoint mechanism and the third disjoint Q-learning mechanism for allocating the power and subcarrier to each user; however, the multi-agent joint Q-learning algorithm uses more memory than disjoint methods.

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“…Furthermore, SIC technique is performed in the PD-NOMA system to reduce interference among users [33], [34]. To develop this framework, we present two approaches [35].…”

Section: B Contributionmentioning

confidence: 99%

Distributed Learning-Based Resource Allocation for Self-Organizing C-V2X Communication in Cellular Networks

Banitalebi

Azmi

Mokari

et al. 2022

IEEE Open J. Commun. Soc.

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“…We consider downlink transmissions in a cellular access network with a set B of interfering base stations (namely, "gNBs"), operated on the same frequency band by the same operator. Users are provided with multi-RAT connectivity, i.e., 3GPP cellular and IEEE 802.11ad/ax/ay physical interfaces which are considered for 3GPP 5G-NR extensions over unlicensed bands [20], for instance as sidelink technologies for V2X communications [21].…”

Section: A Novel Sidelink-assisted Icic Frameworkmentioning

confidence: 99%

Stochastic Muting with Short-range Relay Analysis

Vitale¹,

Sciancalepore

Mancuso

2022

2022 20th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

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Resource muting in the time-frequency domain is a tool for controlling co-channel interference in cellular networks. However, coordinated resource of multiple cells implies nontrivial complexity to be effective. Moreover, resource muting does not guarantee fair resource allocation to users. However, we analytically show that stochastic resource muting jointly with opportunistic short-range relay operated by neighbor mobile devices permits to abate the complexity of muting coordination and boosts throughput fairness among users. We therefore formulate a convex proportional fairness optimization problem and leverage our analysis to design VSM-PF, a practical optimization scheme that significantly outperforms legacy systems.

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“…The latest vehicle network modules support both direct V2V communication using Dedicated Short Range Communication (DSRC) [11] or C-V2X mode4 [12]; and cellular connectivity using LTE C-V2X [13]. The cellular-based connection can cover a wider range, but is expensive, so it is more economical to transmit over V2V connections as much as possible.…”

Section: Introductionmentioning

confidence: 99%

Vehicular Cloud Forming and Task Scheduling for Energy-Efficient Cooperative Computing

2023

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A vehicular cloud (VC) is a network of vehicles that perform cooperative computing through vehicle-to-vehicle (V2V) communication. Existing research on vehicular cloud computing (VCC) is mostly based on cloud servers or edge servers, not VCs. However, vehicles, by constructing a Vehicular Ad-Hoc Network (VANET), can perform applications requiring the large amount of computation cooperatively on their own without the help of edges or cloud servers. One of important issues for the VANET cooperative computing is how to handle the frequent topology change due to vehicle mobility. The unstable network topology limits the advantage of cooperative computing and even makes its operation stop sometimes. This paper proposes a cooperative computing method based on vehicle-to-vehicle (V2V) communication. For stable and energy-efficient cooperative computing, the proposed method considers the distance when selecting vehicles that it will cooperate with and delays task offloading back as far as possible. The proposed method outperforms previous static scheduling methods in terms of energy efficiency and network stability.INDEX TERMS computation offloading, cooperative computing, VANET, vehicle-to-vehicle communication, vehicular cloud.

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Resource Allocation Modes in C-V2X: From LTE-V2X to 5G-V2X

Cited by 104 publications

References 68 publications

Distributed Learning-Based Resource Allocation for Self-Organizing C-V2X Communication in Cellular Networks

Distributed Learning-Based Resource Allocation for Self-Organizing C-V2X Communication in Cellular Networks

Stochastic Muting with Short-range Relay Analysis

Vehicular Cloud Forming and Task Scheduling for Energy-Efficient Cooperative Computing

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