Optimize Semi-Persistent Scheduling in NR-V2X: An Age-of-Information Perspective

Cao, Liu; Yin, Hao; Wei, Ran; Zhang, Lyutianyang

doi:10.1109/wcnc51071.2022.9771765

Cited by 11 publications

(5 citation statements)

References 11 publications

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“…The authors in [20] analyzed the AoI of Release 14 C-V2X Mode-4 and proposed a piggyback collaboration method to help decrease half duplex errors, thereby reducing the AoI. More recently, [35] looked at the effect of RRI spacing on AoI performance in the NR-V2X Mode-2 standard, and found optimal RRI values across vehicle densities. Note that although the Release 14 C-V2X Mode-4 SPS is similar to Release 16 NR-V2X Mode-2 SPS, there are a few key differences between the two standards.…”

Section: State-of-the-artmentioning

confidence: 99%

Adaptive RRI Selection Algorithms for Improved Cooperative Awareness in Decentralized NR-V2X

Dayal,

Shah,

Dhillon

et al. 2023

IEEE Access

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Decentralized vehicle-to-everything (V2X) networks (i.e., C-V2X Mode-4 and NR-V2X Mode-2) utilize sensing-based semi-persistent scheduling (SPS) where vehicles sense and reserve suitable radio resources for Basic Safety Message (BSM) transmissions at prespecified periodic intervals termed as Resource Reservation Interval (RRI). Vehicles rely on these received periodic BSMs to localize nearby (transmitting) vehicles and infrastructure, referred to as cooperative awareness. Cooperative awareness enables line of sight and non-line of sight localization, extending a vehicle's sensing and perception range. In this work, we first show that under high vehicle density scenarios, existing SPS (with prespecified RRIs) suffer from poor cooperative awareness, quantified as tracking error. Tracking error is defined as the difference between a vehicle's true and estimated location as measured by its neighbors. To address the issues of static RRI SPS and improve cooperative awareness, we propose two novel RRI selection algorithms -namely, Channel-aware RRI (Ch-RRI) selection and Age of Information (AoI)-aware RRI (AoI-RRI) selection. Ch-RRI dynamically selects an RRI based on channel resource availability depending upon the (sparse or dense) vehicle densities, whereas AoI-RRI utilizes a novel information freshness metric, called Age of Information (AoI) to select a suitable RRI. Both adaptive RRI algorithms use SPS for selecting transmission opportunities for timely BSM transmissions at the chosen RRI. System-level simulations demonstrate that both proposed schemes outperform the SPS with fixed RRI in terms of improved cooperative awareness. Furthermore, AoI-RRI SPS outperforms Ch-RRI SPS in high densities, whereas Ch-RRI SPS is slightly better than AoI-RRI SPS in low densities.

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Section: State-of-the-artmentioning

confidence: 99%

Adaptive RRI Selection Algorithms for Improved Cooperative Awareness in Decentralized NR-V2X

Dayal,

Shah,

Dhillon

et al. 2023

IEEE Access

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“…Following the standard IEEE 802.11p, we already see a number of studies that address the use of AoI metrics for time-critical applications in vehicular networks [6,7,8,9,10,11,12] and including test-bed demonstrators [18]. The AoI metric is reported to update the network freshness for the exchange of vehicles' speeds and positions.…”

Section: Age Of Information In Itsmentioning

confidence: 99%

“…Accounting for the AoI metrics, other works use analytic methods for the average AoI [9] and PAoI [12]. The average AoI metric is formulated mainly in two different approaches.…”

Section: Age Of Information In Itsmentioning

confidence: 99%

“…Besides, a variety of scenarios for the traffic of vehicles have been studied. Examples include four lane roads [6], highways [12,18], platooning [7,9], the more artificially Manhattan Grid [8,11,18], the TapasCologne scenario [8,11], and open environments [10]. Furthermore, the work in [18] recreates a traffic scenario integrating simulators with software defined radio (SDR) devices.…”

Section: Age Of Information In Itsmentioning

confidence: 99%

“…Recent research illustrates the use of AoI metrics in the vehicular context. The average AoI and the peak age of information (PAoI) are used to find the best strategy for the emission rate of beacon packets, see for instance [6,7,8,9,10,11,12] (further discussion in the next Section). However, these metrics do not consider information context [13]; not all packets necessarily carry the same information's importance, thereby not introducing the same level of freshness for the status update.…”

Section: Introductionmentioning

confidence: 99%

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A Spatial Model for Using the Age of Information in Cooperative Driving Applications

Heinovski

Gómez

Dressler

2022

Proceedings of the International Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems on International

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New technologies for sensing and communication act as enablers for cooperative driving applications. Sensors are able to detect objects in the surrounding environment and information such as their current location is exchanged among vehicles. In order to cope with the vehicles' mobility, such information is required to be as fresh as possible for proper operation of cooperative driving applications. The age of information (AoI) has been proposed as a metric for evaluating freshness of information; recently also within the context of intelligent transportation systems (ITS). We investigate mechanisms to reduce the AoI of data transported in form of beacon messages while controlling their emission rate. We aim to balance packet collision probability and beacon frequency using the average peak age of information (PAoI) as a metric. This metric, however, only accounts for the generation time of the data but not for application-specific aspects, such as the location of the transmitting vehicle. We thus propose a new way of interpreting the AoI by considering information context; thereby incorporating vehicles' locations. As an example, we characterize such importance using the orientation and the distance of the involved vehicles. In particular, we introduce a weighting coefficient used in combination with the PAoI to evaluate the information freshness; emphasizing on information from more important neighbors. We further design the beaconing approach in a way to meet a given AoI requirement, thus, saving resources on the wireless channel while keeping the AoI minimal. We illustrate the effectiveness of our approach in Manhattan-like urban scenarios; reaching pre-specified targets for the AoI of beacon messages.

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Understanding the impact of persistence and propagation on the Age of Information of broadcast traffic in 5G NR-V2X sidelink communications

Rolich,

Turcanu,

Vinel

et al. 2024

Computer Networks

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Optimize Semi-Persistent Scheduling in NR-V2X: An Age-of-Information Perspective

Cited by 11 publications

References 11 publications

Adaptive RRI Selection Algorithms for Improved Cooperative Awareness in Decentralized NR-V2X

Adaptive RRI Selection Algorithms for Improved Cooperative Awareness in Decentralized NR-V2X

A Spatial Model for Using the Age of Information in Cooperative Driving Applications

Understanding the impact of persistence and propagation on the Age of Information of broadcast traffic in 5G NR-V2X sidelink communications

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