PHY layer enhancements for next generation V2X communication

Triwinarko, Andy; Dayoub, Iyad; Cherkaoui, Soumaya

doi:10.1016/j.vehcom.2021.100385

Cited by 21 publications

(17 citation statements)

References 3 publications

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“…Forth, an important novelty of the IEEE 802.11bd standard, which will provide higher throughput for bandwidthhungry applications such as road map update applications and infotainment applications, is the Single-User Multiple Input Multiple Output (SU-MIMO) technology [37]. This technology allows the transmission of 2 spatial streams in the unicast mode.…”

Section: Main Novelties Of Ieee 80211bdmentioning

confidence: 99%

Section: Related Papersmentioning

confidence: 99%

“…Various aspects of IEEE 802.11bd networks are studied in many papers [28], [37], [43]- [51]. Paper [43] The authors of [37], [46]- [48] compare the performance of IEEE 802.11p and IEEE 802.11bd. The results show that adaptive blind retransmissions of broadcast frames and the MCS with DCM can highly enlarge the maximum transmission range of NGV STAs and improve their PLR compared with non-NGV STAs.…”

Section: Related Papersmentioning

confidence: 99%

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A Study of Channel Bonding in IEEE 802.11bd Networks

2022

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In recent years, Vehicle-To-Everything (V2X) applications have been actively developing, and the Quality of Service (QoS) requirements are becoming more stringent. To support V2X applications, IEEE developed the IEEE 802.11p standard in 2010. However, systems based on this standard fail to fulfill requirements on very low frame transmission delay and packet loss ratio imposed by modern V2X applications, such as applications for autonomous driving and platooning. To satisfy new requirements, IEEE has launched a new IEEE 802.11bd project to design the next generation of IEEE 802.11p. An important feature of IEEE 802.11bd is the channel bonding technique, which allows transmitting data in two adjacent channels simultaneously. Thus, it increases data rates and may reduce delays and packet loss ratio. The current version of IEEE 802.11bd specifies two channel bonding techniques, which differ from that used in modern Wi-Fi networks. This work evaluates the performance of the three aforementioned techniques from the IEEE 802.11 family of standards considering frame transmission delays and packet loss ratio. With rigorous simulations, it is shown that, in most cases, the channel bonding techniques highly decrease the percentage of both IEEE 802.11bd and legacy stations with unsatisfied QoS requirements on delays and packet loss ratio. Unfortunately, sometimes, the IEEE 802.11bd channel bonding techniques significantly worsen performance. Moreover, the work highlights that the choice of the primary channel for channel bonding techniques significantly affects the network performance. As a result, the paper provides a recommendation for selecting both the most suitable channel access method and primary channel.

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Section: Main Novelties Of Ieee 80211bdmentioning

confidence: 99%

Section: Related Papersmentioning

confidence: 99%

Section: Related Papersmentioning

confidence: 99%

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A Study of Channel Bonding in IEEE 802.11bd Networks

2022

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“…The Internet of vehicles (IoV) is an emerging concept that enhances the existing capabilities of vehicular communication by integrating with the Internet of things (IoT). IoV is a key use-case in the upcoming beyond fifth generation (5G) wireless networks [1,2]. IoV creates diverse new applications with extremely diverse service requirements including ultrahigh reliable and delay-sensitive, bandwidth-hungry as well as compute-intensive applications [3].…”

Section: Imentioning

confidence: 99%

Network Slicing with MEC and Deep Reinforcement Learning for the Internet of Vehicles

Mlika

Cherkaoui

2021

IEEE Network

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The interconnection of vehicles in the future fifth generation (5G) wireless ecosystem forms the so-called Internet of vehicles (IoV). IoV offers new kinds of applications requiring delay-sensitive, compute-intensive and bandwidth-hungry services. Mobile edge computing (MEC) and network slicing (NS) are two of the key enabler technologies in 5G networks that can be used to optimize the allocation of the network resources and guarantee the diverse requirements of IoV applications. As traditional model-based optimization techniques generally end up with NP-hard and strongly non-convex and non-linear mathematical programming formulations, in this paper, we introduce a model-free approach based on deep reinforcement learning (DRL) to solve the resource allocation problem in MECenabled IoV network based on network slicing. Furthermore, the solution uses non-orthogonal multiple access (NOMA) to enable a better exploitation of the scarce channel resources.The considered problem addresses jointly the channel and power allocation, the slice selection and the vehicles selection (vehicles grouping). We model the problem as a single-agent Markov decision process. Then, we solve it using DRL using the wellknown DQL algorithm. We show that our approach is robust and effective under different network conditions compared to benchmark solutions.

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“…Vehicle-to-everything (V2X) communication is an integral part of future intelligent transportation systems (ITS) and has become a key communication paradigm in future wireless networks [44]. Indeed, V2X communication is one of the key verticals in fifth generation (5G) networks [17,38].…”

Section: Introductionmentioning

confidence: 99%

Network slicing for vehicular communications: a multi-agent deep reinforcement learning approach

Mlika,

Cherkaoui

2022

Preprint

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This paper studies the multi-agent resource allocation problem in vehicular networks using non-orthogonal multiple access (NOMA) and network slicing. Vehicles want to broadcast multiple packets with heterogeneous quality-of-service (QoS) requirements, such as safety-related packets (e.g., accident reports) that require very low latency communication, while raw sensor data sharing (e.g., high-definition map sharing) requires high-speed communication. To ensure heterogeneous service requirements for different packets, we propose a network slicing architecture. We focus on a non-cellular network scenario where vehicles communicate by the broadcast approach via the direct device-to-device interface (i.e., sidelink communication). In such a vehicular network, resource allocation among vehicles is very difficult, mainly due to (i) the rapid variation of wireless channels among highly mobile vehicles and (ii) the lack of a central coordination point. Thus, the possibility of acquiring instantaneous channel state information to perform centralized resource allocation is precluded. The resource allocation problem considered is therefore very complex. It includes not only the usual spectrum and power allocation, but also coverage selection (which target vehicles to broadcast to) and packet selection (which network slice to use). This problem must be solved jointly since selected packets can be overlaid using NOMA and therefore spectrum and power must be carefully allocated for better vehicle coverage. To do so, we first provide a mathematical programming formulation and a thorough NP-hardness analysis of the problem. Then, we model it as a multi-agent Markov decision process. Finally, to solve it efficiently, we use a deep reinforcement learning (DRL) approach and specifically propose a deep Q learning

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PHY layer enhancements for next generation V2X communication

Cited by 21 publications

References 3 publications

A Study of Channel Bonding in IEEE 802.11bd Networks

A Study of Channel Bonding in IEEE 802.11bd Networks

Network Slicing with MEC and Deep Reinforcement Learning for the Internet of Vehicles

Network slicing for vehicular communications: a multi-agent deep reinforcement learning approach

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