An Efficient Mode Selection for Improving Resource Utilization in Sidelink V2X Cellular Networks

Albonda, Haider Daami R.; Perez-Romero, J.

doi:10.1109/camad.2018.8514958

Cited by 12 publications

(8 citation statements)

References 11 publications

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“…We assume that, when sidelink transmissions are utilized, every member vehicle can multicast the V2V messages directly to multiple member vehicles of the same cluster 1 ≤ i ≤ V (j) using one-to-many technology. The decision on when to use cellular or sidelink mode is done based on [29].…”

Section: System Model and Problem Formulation A System Modelmentioning

confidence: 99%

An Efficient RAN Slicing Strategy for a Heterogeneous Network With eMBB and V2X Services

Albonda

Perez-Romero

2019

IEEE Access

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Emerging 5G wireless technology will support services and use cases with vastly heterogeneous requirements. Network slicing, which allows composing multiple dedicated logical networks with specific functionality running on top of a common infrastructure, is introduced as a solution to cope with this heterogeneity. At the radio access network (RAN), the use of network slicing involves the assignment of radio resources to each slice in accordance with its expected requirements and functionalities. Therefore, RAN slicing will provide the required design flexibility and will be necessary for any network slicing solution. This paper investigates the RAN slicing problem for providing two generic services of 5G, namely enhanced mobile broadband (eMBB) and vehicle-to-everything (V2X). In this respect, we propose an efficient RAN slicing scheme based on an off-line reinforcement learning followed by a low-complexity heuristic algorithm, which allocates radio resources to different slices with the target of maximizing the resource utilization while ensuring the availability of resources to fulfill the requirements of the traffic of each RAN slice. A simulation-based analysis is presented to assess the performance of the proposed solution. The simulation results have shown that the proposed algorithm improves the network performance in terms of resource utilization, the latency of V2X services, achievable data rate, and outage probability. INDEX TERMS Vehicle-to-everything (V2X), reinforcement learning, network slicing, RAN slicing.

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Section: System Model and Problem Formulation A System Modelmentioning

confidence: 99%

An Efficient RAN Slicing Strategy for a Heterogeneous Network With eMBB and V2X Services

Albonda

Perez-Romero

2019

IEEE Access

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“…Although the above works have proposed different approaches for mode selection, none of them has considered jointly the mode selection between sidelink and cellular modes taking into account the quality of the links between V2V users in sidelink mode and between the base station and the vehicles in cellular mode, and the available resources. In this respect, in our previous work [21], we proposed a novel algorithm for mode selection that takes into account the quality of these links and the network traffic load situation.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed mode selection strategy takes into account the quality of the links between V2V users in sidelink mode and between the base station and the vehicles in cellular mode, and the available resources. Different from [21], the proposed mode selection decides the amount of resources to be allocated to V2V links in each mode and allows reusing RBs between different SL users, provided that the interference constraints are met. Our proposed mode selection strategy targets to achieve further improvements in the network performance (i.e., in terms of resource utilization, V2X service latency, achievable data rate, and outage probability).…”

Section: Related Workmentioning

confidence: 99%

“…The presented evaluation results intend to assess and illustrate the performance of the proposed solution in terms of latency, packet success rate and RB utilization. As a reference for comparison, we assume a mode selection strategy denoted as "Mode Selection Signal-Based (MSSB)", which takes into account only the quality of the links and is inspired from the work in [21]. Fig.…”

Section: Performance Evaluationmentioning

confidence: 99%

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A New Mode Selection and Resource Reuse Strategy for V2X in Future Cellular Networks

Albonda

Perez-Romero

2020

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)

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Recently, vehicle-to-vehicle (V2V) communications for future cellular systems have attracted considerable attention due to their potential benefits such as improved system capacity, spectral efficiency or delay reduction, and they enable new services related to intelligent transportation systems. V2V communications can be performed using two communication modes, namely cellular mode, based on uplink/downlink communications, and sidelink mode, which enables vehicles to communicate directly with other vehicles. However, selecting the appropriate operating mode according to the requirements of V2V services and allocating the radio resources to V2V communications becomes a challenging issue. In this respect, we propose a novel mode selection and resource reuse strategy to select the appropriate mode of operation and decide the amount of resource blocks (RBs) for V2V in cellular and sidelink modes, where multiple V2V links may share the same radio resources. A simulation-based analysis is presented to assess the performance of the proposed solution. The simulation results have shown that the proposed strategy improves network performance in terms of achievable throughput, outage probability, latency, and resource utilization.

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“…To deal with the wireless channel impairments, sidelink (SL) [7] resource allocation, which is a primary concern in the design and operation of NR-V2X, presents unique challenges. One of these challenges results from the fact that the traffic volume and transmission rate of communication increases greatly [8].…”

Section: Introductionmentioning

confidence: 99%

Maximum-Throughput Sidelink Resource Allocation for NR-V2X Networks With the Energy-Efficient CSI Transmission

et al. 2020

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New radio vehicle-to-everything (NR-V2X) is an emerging technology based on the 5G cellular mobile communication networks. In this paper, the sidelink (SL) resource allocation for NR-V2X in Mode 1 is considered, where 5G base station (gNB) schedules SL resources to be used by V2X users (VUs) based on the periodically reported channel state information (CSI). To reduce the overhead, an energy efficient power allocation scheme of CSI transmission is proposed using hybrid spectrum access technology. With the knowledge of CSI, SL resource allocation is modeled as a mixed binary integer nonlinear programming (MBINP). It is designed to maximize the sum throughput of NR-V2X networks among different subcarriers subject to the total available power and the minimal transmission rate. To achieve that, the original MBINP is decomposed into two sub-problems, namely, subcarrier assignment and power allocation. Firstly, the appropriate subcarriers are quickly obtained by averaging the power allocation. Secondly, an alternative optimization mechanism is proposed to the power allocation. Simulation results show that the proposed power allocation scheme of CSI transmission can obviously save the energy consumption, and the proposed suboptimal SL resource allocation algorithm achieves better performance than the compared methods with relatively low complexity. INDEX TERMS New radio vehicle-to-everything (NR-V2X), channel state information (CSI), subcarrier assignment, power allocation.

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An Efficient Mode Selection for Improving Resource Utilization in Sidelink V2X Cellular Networks

Cited by 12 publications

References 11 publications

An Efficient RAN Slicing Strategy for a Heterogeneous Network With eMBB and V2X Services

An Efficient RAN Slicing Strategy for a Heterogeneous Network With eMBB and V2X Services

A New Mode Selection and Resource Reuse Strategy for V2X in Future Cellular Networks

Maximum-Throughput Sidelink Resource Allocation for NR-V2X Networks With the Energy-Efficient CSI Transmission

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