A Systematic Literature Review in Distributed Resource Allocation for C-V2X

A Systematic Literature Review in Distributed Resource Allocation for C-V2X

Ali Nihad Al-Najjar Mohd Fadlee A Rasid* Fazirulhisyam Hashim Faisul Arif Ahmad Abbas Jamalipour

Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia

Wireless and Photonics Networks Research Centre (WiPNET), Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia

Studies Planning and Follow-up Directorate, Ministry of Higher Education and Scientific Research, Baghdad 10065, Iraq

School of Electrical and Information Engineering, University of Sydney, Camperdown NSW 2006, Australia

Corresponding Author Email: 
21 February 2023
7 May 2024
2 June 2024
Available online: 
20 June 2024
| Citation

© 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).



Vehicular networks are the key paradigm of the Internet of Vehicles (IoV) as the extension of the Internet of Things (IoT) notion in Intelligent Transportation Systems (ITS) which can assist in the development of autonomous driving in smart cities. This technology can provide a wide variety of onboard data services, such as road safety, and increase traffic efficiency by connecting vehicles with road infrastructure and pedestrians. However, it is a challenging task to provide a satisfactory quality of service (QoS) to this network due to a number of limiting factors such as resource collision, resource interference, and congested channels because of the network topology and rapid changes produced by the high mobility as well as hardware imperfections and the anticipated growth of vehicular network devices. As a result, it will be essential to ensure that the resources of the available cellular network are allocated and used in the most efficient possible way. To achieve these goals, 3GPP has standardized the cellular vehicle to everything (C-V2X) with two versions, the long-term evolution-V2X (LTE-V2X) in Release 14 and the new radio-V2X (NR-V2X) in Release 16, as prominent technologies to improve resource allocation for vehicular networks. In order to capture the continuous effort for improving resource allocation, we present a systematic literature review (SLR) on distributed resource allocation (DRA) schemes for the two cellular-based vehicular network technologies. First, we discuss the technical configuration of resource allocation in the light of LTE-V2X and NR-V2X technologies and classify the state-of-the-art for each technology. Afterward, we explain the impact of machine learning (ML) and congestion control (CC) on the DRA. Then, we point out the primary performance metrics and simulation tools that were used in the related work. Ultimately, we highlight the challenges, open issues, and opportunities for DRA in C-V2X and outline several promising future research directions.


distributed resource allocation, long-term evolution-V2X, new radio-V2X, cellular vehicle to everything, machine learning, congestion control

1. Introduction

Road traffic injuries pose a substantial burden on public health in terms of morbidity, mortality, and disability. Annually, over 1.2 million people perish and an additional 50 million suffer injuries as a result of road accidents worldwide. Accidents involving motor vehicles are the leading cause of death for individuals aged 15 to 29. Over 90% of all deaths are estimated to occur in low-and middle-income countries, which account for roughly half of the world's registered vehicles. Road traffic accidents are projected to be the seventh-leading cause of death by 2030 if the current trend continues and no proper action is taken [1]. The growing quantity of vehicles on the roadways, as a result of the growing human population, strains the world's transportation networks and causes a variety of problems, including parking difficulties, accidents, long commute times, traffic congestion, and increased pollution. Therefore, a reliable and secure transportation system is of utmost importance. Vehicular networks play a vital role in the Internet of Things (IoT), giving rise to the Internet of Vehicles (IoV) [1], a sophisticated extension of IoT in Intelligent Transportation Systems (ITS). IoT is regarded as a fundamental enabling technology for smart cities, where intelligent devices can interact with one another [2]. IoV is primarily intended to facilitate a real-time network between vehicles, pedestrians, and roadside infrastructure.

Despite the enormous potential of smart cities, vehicular networks are currently confronted with a multitude of technological obstacles in terms of their performance and security. In light of this, vehicular cellular technology, also referred to as cellular-V2X (C-V2X) [3], which operates in both the 5.9-GHz range and the licensed spectrum allocated for cellular networks, has emerged to enable vehicular networks and overcome these obstacles. Third Generation Partnership Project (3GPP) Release 14 outlined the initial C-V2X technology, also known as the long-term evolution-V2X (LTE-V2X). Release 16 of the 3GPP has introduced NR-V2X as the most recent version of C-V2X technology. C-V2X provides exceptional performance in coverage range, reliability, throughput, and latency. Although C-V2X holds promise for improving communication between vehicles, effectively using this technology requires overcoming hurdles in allocating resources.

Consequently, the C-V2X resource allocation has attracted the researcher’s interest over the past few years. Radio frequencies, being valuable and scarce resources, require careful management to minimize interference and collisions. In addition, resources allocation in C-V2X networks encounters unique challenges as a result of the dynamic and diverse characteristics of vehicular environment. These challenges arise from factors such as different vehicle densities, unpredictable mobility patterns, and diverse quality-of-service (QoS) requirements. To tackle such challenges, it is necessary to employ advanced resource allocation strategies that can dynamically adjust to changing network environments while simultaneously catering to the varying requirements of different applications. A key requirement in C-V2X networks is to guarantee reliable connectivity for safety-critical applications, such as cooperative awareness and collision avoidance. These applications require fast and reliable communication, which means that communication resources need to be allocated efficiently to give priority to significant messages. Moreover, these kinds of networks are capable of accommodating a diverse array of applications that have various requirements in terms of bandwidth and latency. These applications include traffic management, infotainment, and autonomous driving. Optimizing network utilization and meeting different quality of service (QoS) requirements of applications requires efficient resource allocation. This allocation must ensure fairness and maximize total system throughput. Therefore, resource allocation in C-V2X is crucial for enhancing the performance of IoV applications.

As there is always an absence of base station (BS) services in some places, two types of resource allocation have been proposed by C-V2X, which are the centralized mode and the decentralized mode. In the context of LTE-V2X, these modes are referred to as modes 3 and 4, whereas in NR-V2X, they are described as modes 1 and 2. For the centralized mode, resources are scheduled and assigned to vehicles by either the evolved Node B (eNB) in LTE or the generation Node B (gNB) in NR. In addition, in dynamic vehicle environments, traditional centralized resource allocation systems may encounter scalability and latency issues. Centralized algorithms necessitate a centralized controller to gather and analyze global network data, which can result in increased communication overhead and delay, especially in large-scale networks with significant mobility. On the other hand, DRA methods provide a more scalable and responsive solution for C-V2X networks in the decentralized mode, vehicles autonomously pick their resources using the sensing-based semi-persistent scheduling (SB-SPS) algorithm by considering local data, such as channel conditions, traffic density, and application needs. Furthermore, distributed techniques enable the delegation of decision-making, allowing for swift adaptation to local changes in network conditions and minimizing dependence on centralized coordination. Moreover, DRA facilitates proactive and opportunistic sharing of resources among nearby vehicles, hence promoting collaborative and efficient utilization of the resources. This adaptability is especially beneficial in dynamic vehicular environments where the structure of the network and the conditions of traffic might change rapidly.

Numerous solutions for LTE-V2X and NR-V2X modes have been presented in the past, concentrating primarily on mode 4 and mode 2, respectively. It is worthwhile to highlight that eNB/gNB control is the main reason why mode 3 and mode 1 are less challenging. In contrast, mode 4 and mode 2 present a variety of collision, congestion control, and interference issues induced by their decentralized structure.

According to our literature, several survey articles [4-14] are concerned with resource allocation for vehicular networks. The work by the authors [6, 7, 9] provided information regarding the resource allocation for the C-V2X sidelink (SL) specifications detailed across different 3GPP Releases. However, they do not report or include any discussion of the related research types that have flourished in recent years, nor do they acknowledge the crucial reality of SL requirements in latency and reliability for vehicular network applications.

An analysis of the research literature review has been presented in the study by the authors [9, 10], which have a broad focus on V2X and a negligible concentration on SL resource allocation. Noor-A-Rahim et al. [7] provided a broad overview of resource allocation for vehicular communication, their focus on C-V2X is limited by considering both dedicated short-range communication (DSRC) and C-V2X technologies. They do not explore all the proposed algorithms for managing resources in both centralized and decentralized C-V2X modes. Furthermore, Allouch et al. [12] concentrated on the resource allocation of C-V2X in their study. The limitation is that they focused exclusively on LTE-V2X technology, thereby excluding the latest research concepts related to resource allocation for NR-V2X. In addition, Le and Moh [11] demonstrate the various resource allocation algorithms that can be implemented in an NR-V2X scenario, as do Sehla et al. [13], where the authors present a comprehensive overview of resource allocation in all modes for LTE-V2X and NR-V2X. Finally, the most recently published article is presented by Shin et al. [14], which addressed the key features of resource allocation for V2X in terms of LTE and NR. Table 1 depicts the summary of the most relevant and important survey articles.

As a result, the absence of a systematic literature review (SLR) focusing on distributed resource allocation (DRA) for LTE mode 4 and NR mode 2 in the context of C-V2X communications presents a critical gap in the current body of knowledge. As C-V2X technology continues to gain momentum in the realm of Intelligent Transportation Systems (ITS), understanding how to efficiently allocate resources in a distributed manner is paramount to ensuring reliable and effective communication among vehicles and infrastructure. By conducting this SLR, our objective is to fill this void and make significant contributions to the advancement of C-V2X DRA techniques.

Our comprehensive analysis will begin by examining the relevant 3GPP specifications, such as LTE mode 4 and NR mode 2, which define the standards for C-V2X communication. We will then delve into the existing literature, covering research publications from 2017 onwards. This approach ensures that our review captures the latest developments and insights in the field.

Table 1. Summary of the most related survey articles







Zeadally et al.


LTE and NR

Concise explanation to 802.11p/bd, PC5 LTE-V2X, and PC5 NR-V2X


Lien et al.



Specifics on the NR-V2X physical layer as well as its control channels


Ashraf et al.



Enhancement to PC5 LTE-V2X in Release 16


Noor-A-Rahim et al.



Survey on vehicular network resource allocation schemes


Gyawali et al.


LTE and NR

Review of the research activities related to C-V2X


Garcia et al.


LTE and NR

3GPP specifications for PC5 LTE-V2X and NR-V2X


Bazzi et al.


LTE and NR

Review of PC5 for LTE-V2X and NR-V2X


Le and Moh



Survey of resource selection schemes for NR-V2X communications


Allouch et al.



Overview on the LTE-V2X resource allocation techniques


Sehla et al.


LTE and NR

Review of resource allocation in LTE-V2X and NR-V2X for all modes


Shin et al.


LTE and NR

Review the key features of resource allocation in LTE and NR for V2X


This Review


LTE and NR

Systematic literature review on distributed resource allocation for C-V2X

Figure 1. SLR organization

The SLR will provide valuable insights into a range of methodologies employed for DRA in C-V2X networks. We will assess existing techniques to identify their strengths and limitations, while also exploring modifications and proposing new alternatives to improve resource allocation performance. Additionally, we will investigate the integration of machine learning (ML) algorithms to enhance the efficiency and adaptability of DRA schemes. This analysis will shed light on the potential of ML in optimizing resource allocation decisions based on real-time network conditions, traffic patterns, and application requirements.

Moreover, the SLR will address the challenges and considerations associated with implementing DRA for C-V2X. This includes aspects such as dynamic channel conditions, diverse quality-of-service (QoS) requirements, scalability, interference management, besides fairness among competing users. Understanding these challenges will provide valuable insights into the design and optimization of DRA schemes that can effectively meet the unique requirements of C-V2X communication. Furthermore, we will investigate the integration of DCC mechanisms to optimize resource allocation decisions and mitigate congestion effects in C-V2X networks.

By bridging the current knowledge gap through this systematic review, our research endeavors aim to drive innovation and enhance the performance of C-V2X systems. The insights gained from this review will serve as a solid foundation for future studies and developments in this rapidly evolving field. Ultimately, our work strives to pave the way for a more efficient, connected, and safer future in intelligent transportation by optimizing resource allocation in C-V2X networks.

The remainder of this SLR article is structured as follows; Section 2 presents an overview of cellular vehicular communication technologies, i.e., LTE-V2X and NR-V2X. Section 3 presents the design for the SLR. In Section 4, We provide an in-depth analysis of the research questions related to DRA in LTE-V2X and NR-V2X, with a specific emphasis on modes 4 and 2, respectively. Our analysis aims to provide a thorough overview of current state-of-the-art in this field, as well as the new technologies that improve DRA (i.e., machine learning), the impact of CC in DRA, and the performance metrices and simulation tools used. This section also addresses the unresolved issues, challenges, and future interesting research concepts regarding the DRA in C-V2X. Ultimately, we conclude our SLR study by presenting a final section that summarizes the findings and provides insights for future research. The SLR organization can be seen in Figure 1.

2. Overview of Cellular Vehicular Communication Technologies

Prior to addressing the main concern of DRA in C-V2X, this part begins by providing an overview of cellular communication technologies used in vehicle networks. 3GPP has standardized the LTE-V2X as a new communication technology in Release 14. The key advantage of LTE-V2X is its utilization of cellular infrastructure, specifically roadside units (RSUs), and its reliance on device-to-device (D2D) connections [15]. The SL interface, also known as the PC5 interface, is specifically designed for D2D communication to handle the mobility nature of vehicle environments, particularly for high velocities. An enhancement has been implemented in this interface to fulfill these requirements. This enhancement comprises integrating demodulation reference symbols (DMRS) into resource frame structure in order to mitigate the impact of the Doppler effect.

Moreover, LTE-V2X supports the combination of both D2D communication, i.e., vehicle-to-infrastructure (V2I) and vehicle to-vehicle (V2V), utilizing the Uu interface, the communication between vehicles and the network is dependent on SL and cellular connectivity, specifically vehicle-to-network (V2N), as illustrated in Figure 2. In order to facilitate V2X communications, the LTE core network design incorporates two main parts: the V2X application server and the V2X control function [16]. The first component manages V2X network data, while the second offers a user equipment (UE) with required settings of V2X communication. The 3GPP introduced two communication modes in LTE-V2X, referred to as modes 3 and 4. In mode 3, the eNB schedules and assigns resources to vehicles. Meanwhile, vehicles reserve their own resources autonomously, utilizing the SB-SPS algorithm in mode 4 [17]. It is important to mention that mode 3 performs effectively because it utilizes the cellular network, whereas mode 4 operates independently of the cellular network. The various abbreviations used throughout this article are detailed in Table 2.

Figure 2. Overview of cellular vehicular network

Table 2. Abbreviations






Third Generation Partnership Project


Peak Age-of-Information


Fifth Generation


Pocket Delivery Ratio


Adaptive Full-Duplex Deep

Reinforcement Learning


Packet Inter-Reception


Adaptive Modulation


Probability of Keeping a Radio Resource


Adaptive Modulation and Collision Detection


Packets Per Second




Physical Resource Block


Adaptive Transmission Power

and Message Interval Control


Predictive Assessment

of Resource Usage


Adaptive-Transmit Power Control


Packet Reception Ratio


Block Error Rate


Physical Sidelink Control Channel


Base Station


Physical Sidelink Shared Channel


Basic Safety Messages


Quadrature Amplitude Modulation


Bandwidth Part


Quadrature Phase-Shift Keying


Cooperative Awareness Message


Resource Block


Channel Busy Ratio


Resource Block Pair


Cumulative Density Function


Reselection Counter


Cluster Head


Resource Element


Counter Learning and Reselection


Resource Reservation Interval


Cyclic Prefix


Reference Signal Received Power


Channel Occupancy Ratio


Received Signal Strength Indication


Single-Subframe Resource


Sensing-Based Semi-Persistent Scheduling




Single Carrier Frequency Division Multiple Access


Decentralized Environmental Notification Message


Sidelink Control Information


Delimited Out-of-Coverage Area


Sub-Carrier Spacing


Dedicated Short-Range Communication




Extended Estimation and Reservation Resource Allocation


Systematic Literature Review


Evolved Node B


Sidelink-Received Signal Strength Indicator


Estimation and Reservation Resource Allocation


Short-Term Sensing-based Resource Selection


Frequency-Division Multiplexing


Selection Window


Full-Duplex Prioritized Scheduling


Traffic-Aware SPS


generation Node B


Transport Block


Inter-Carrier Interference


Transport Block Size


Internet of Things


Transport Channel


Inter-Symbol Interference


Time-Division Multiplexing


Key Performance Indicators


Time Slot


Listen Before Talk


Transmission Time Interval


Long-Term Evolution


Update Delay


Medium Access Control




Modulation and Coding Scheme




Machine Learning




New Radio




Orthogonal Frequency Division Multiplexing


Wireless Blind Spot


Online Reinforcement Learning Approach


World Health Organization

Table 3. Advanced V2X service applications QoS requirements

V2X Services

Automation Level

Latency (ms)

Tx Rate (msg/sec)

Reliability (%)

Data Rate (Mbps)

Vehicle platooning






Extended sensors






Advanced driving






Remote driving





UL: 25 DL: 1

In Releases 15 and 16, NR-V2X was developed to manage sophisticated V2X applications. These applications maintain road safety by requiring low latency, high throughput, high reliability, and scalability. However, different V2X communications require varying degrees of quality of service (QoS), which depends on the transmitter and receiver for various V2X services; these services are described in greater detail below [18].

· Vehicle platooning: refers to several vehicles travelling in close proximity to one another as they form a single unit called a platoon. The leader vehicle leading the platoon relays messages to the other vehicles.

· Extended sensors: enable V2V, V2I, and vehicle-to-pedestrian (V2P) data exchanges for generating a comprehensive map of the surrounding area.

· Remote driving: enables people to control vehicles from a remote location.

Table 3 summarizes the requirements of the advanced V2X applications QoS.

Release 14 of the 3GPP standard introduced the radio resource allocation method necessary for V2X communication. This Release also described the technological enablers for V2X advanced service applications at the physical and MAC levels. 3GPP Release 16 [19] was designed to address the substantial needs of V2V services, which include high reliability, flexible transmission technology, and low latency. These criteria must be met in a highly dynamic environment. The NR-V2X services are supported by 3GPP Release 16, which focuses on enhancing V2X scenarios and implementing stricter criteria for advanced automation capabilities. These requirements include the management of resources and the physical frame structure [20]. As shown in Table 3, the NR-V2X advanced services demand an exceptionally high level of reliability, between 90 and 99.99%, and a low level of latency, between 100 and 10 milliseconds, or as low as 3 milliseconds.

NR-V2X technology now supports these newly developed applications, in addition to those currently supported by LTE-V2X technology and relating to essential safety services. If a vehicle is equipped with both cellular technologies, in this particular situation, RAT can be utilized for fundamental safety applications and for advanced service applications, respectively., which are also known as C-V2X RATs. The nature of the messages that are sent and received might either be periodic or aperiodic, depending on the application. Additionally, certain communications are transmitted for whole vehicles, whereas others are directed for a particular set of vehicles, for instance in the platooning scenario, wherein the commander interacts with the members within the platoon. To accomplish this objective, NR-V2X provides support for unicast and groupcast communication in addition to broadcast communication, which is already supported by LTE-V2X [21].

These new communications are operable both inside and outside the cellular service area. It is important to keep in mind that the vehicle is capable of simultaneously using both the groupcast and the broadcast communications. This indicates that the vehicle is capable of participating in groupcast communication, which involves communicating with a specific group of other vehicles. Additionally, it can convey messages to other vehicles using broadcast communication. The best example of this scenario is platooning, in which the leader member communicates with other members using groupcast communication to keep the platoon together as well as broadcasts periodic messages to vehicles that are not members of the platoon as part of the cooperative awareness service. NR-V2X technology includes the following essential features:

  • Supporting New Numerologies: The new numerologies have been established for the NR in 3GPP Release 15 [22]. These numerologies are supported as well as the NR-V2X standard, which was presented in 3GPP Release 16. NR-V2X offers greater flexibility in subcarrier spacing (SCS) compared to LTE-V2X. Unlike LTE-V2X, which is fixed at 15 kHz, NR-V2X allows for SCS values that are multiples of 15 kHz, including 30 kHz, 60 kHz, and 120 kHz. Due to the fact that the SCS is a subject that is changeable, the time slot (TS), which is the time required for transmitting 14 OFDM symbols, is also subject to variation. This TS diminishes with an increase in the SCS; this procedure will lower the latency and, as a result, benefit applications that are crucial for latency. Table 4 provides a more detailed analysis of the differences and similarities between LTE and NR of V2X.
  • PSCCH and PSSCH Multiplexing in the Time Domain: The second crucial and noteworthy characteristic is that PSCCH and PSSCH multiplex in the time domain. On one hand, this means that the PSCCH will be transmitted first, and then the PSSCH. On the other hand, LTE-V2X combines these channels in the frequency domain. Both features contribute to reducing latency.
  • Use of the PSFCH: It is defined in NR-V2X as a novel channel aimed at guaranteeing the reliability of both unicast and groupcast communications.

In addition to the features listed above, the NR-V2X offers a number of features on its physical layer, such as a variable quantity of DMRS symbols within the slot and a high Modulation and Coding Scheme (MCS) level, capable of supporting up to 64 QAM coding.

In Section 4, we will present a detailed explanation of the DRA in both LTE-V2X and NR-V2X technologies.

Table 4. Features comparison of LTE-V2X and NR-V2X





15 kHz

15, 30, 60, and 120 kHz

Communication Modes


Unicast, Groupcast, and Broadcast


QPSK and 16QAM

QPSK, 16QAM, and 64QAM







Feedback Channel






Sidelink Modes

3 and 4

1 and 2

3. SLR Design

This section of the research focuses on the methodology or framework that was utilized to complete this SLR. The structure is based on instructions to complete the SLR that was directed by Boell and Cecez-Kecmanovic [23] and reported in the study by Kamal et al. [24], with the emphasis on V2X DRA in LTE and NR. The primary focus of an SLR is on the formulation of research questions, in addition to the presentation of the many aspects of motivation that are included in this section. The included articles were selected from a variety of different data sources. Specifically, a research strategy was developed with the intent of focusing on articles associated with a particular domain, which will be discussed in the next part of the article. Then, the research articles were gathered for this study according to the inclusion and exclusion assessment criteria. As a means to significantly assess the state-of-the-art of DRA in both LTE-V2X and NR-V2X, motivations and research questions were developed.

3.1 Research questions

The following is a list of the main research issues that were explored and evaluated during this study:

(1) What is the DRA in LTE-V2X?

(2) What are the existing state-of-the-art articles for DRA in LTE-V2X Mode 4?

(3) What is the DRA in NR-V2X?

(4) What are the existing state-of-the-art articles for DRA in NR-V2X Mode 2?

(5) What are the ML algorithms that improve the DRA in C-V2X?

(6) What is the impact of CC on DRA in C-V2X?

(7) Which metrics, simulation tools, and parameters are considered for DRA of LTE-V2X and NR-V2X?

(8) Which challenges, open issues, and promising future directions are for DRA in C-V2X?

3.2 Research criteria

The primary focus of this SLR was on DRA in C-V2X since it is more closely associated with the IoT and enhanced vehicular network performance. The articles published in 2017 and onward were taken into account for this SLR since the 3GPP released the first official C-V2X specification in Release 14, which was complete in 2017. Focusing on 2017 onwards allows for the inclusion of research and developments following the standardization efforts, providing insights into how C-V2X technologies have evolved and been implemented in real-world scenarios. Many pilot projects and field trials of C-V2X technologies have been launched by automotive manufacturers, infrastructure providers, and government agencies worldwide since 2017. Research during this period can shed light on the practical challenges, solutions, and lessons learned from these deployment initiatives.

Table 5. Database sources





Google Scholar


Science Direct






We gave the search keywords that were used for seeding the objectives to select the group of papers that will be taken into consideration. These search phrases are based on the research questions that were submitted and the planned subject. The study team used the phrases "C-V2X," "resource allocation," "LTE," and "NR" when doing the search. These phrases were nominated for use as primary keywords. In order to link the significant search phrases, we used the logical "OR" and "AND" operators. Eventually, after conducting a small number of tests, we chose the related search string that yielded a suitable number of relevant research papers by making use of the keywords to encase the search question that was displayed in Figure 3.

3.3 Database sources

Different data sources were examined. These databases, which include Scopus, Google Scholar, IEEE, Springer, and Science Direct, were primarily investigated for relevant conference papers, journal articles, review articles, and magazines, as illustrated in Table 5.

3.4 Articles selection process strategy

The perspectives presented in research papers were the most common criterion utilized in the selection of various quantitative types of studies. In order to determine whether particular articles should be included or not, standards of quality evaluation were applied to those pieces. As stated previously, the selection of papers began with the formulation of the research questions that would guide the study. The search and selection process were aided by outlining the string of searches that were conducted. In this review, only articles written in English were considered. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) flowchart [25] was applied, as illustrated in Figure 4.

The data extraction process involved systematically collecting essential information from selected articles on DRA in C-V2X networks. Using a standardized template, details such as article title, authors, publication venue, research objectives, methodology, resource allocation techniques, application scenarios, performance metrics, results, challenges, and future research directions were recorded. This method ensured consistency and facilitated thorough analysis to meet the review's objectives efficiently.

After acquiring the main papers according to the keywords and strings, we examined each article to analyze how resource allocation techniques in vehicular networks were discussed. The classification of the resource distribution plan marked the conclusion of the search technique, which served to further validate the exhaustive nature of this study. There was a mismatch between the names of a few of the publications and the robustness of the measures that were used, which led to their exclusion. In addition, the inclusion of abstracts by themselves was not considered for this study.

It is clear from Figure 4 that the search query yielded a total of 507 published research articles. The articles were published in a variety of high-quality journals between the years 2017 and 2023, as shown in Table 5. Using the inclusion and exclusion criteria, the selection of compelling research articles has been carried out. These criteria are listed in Table 6, and they were used to reduce the number of research articles to 246 (after removing duplicates) and then to 76 (after filtering abstracts and titles). These 76 research articles were subsequently analyzed and classified according to their DRA technologies as LTE-V2X and NR-V2X, ML and CC for C-V2X.

The papers selected for this study are represented in Figure 5, which organizes them according to their publication year. The selection of these publications was further categorized by publisher and C-V2X resource allocation algorithms.

Finally, there were only 76 articles that were considered for the SLR study regarding the DRA in C-V2X. These papers were extracted from different widely known journals, like IEEE, Springer, Science Direct, Scopus, Google Scholar, and other publishers. Figure 6 illustrates the distribution of the 76 selected articles based on the SLR research questions, to be explained in Section 4.

As presented in Figure 7, the essential components of the specified C-V2X taxonomy are 1) Communication technologies, 2) Approaches, 3) Requirements, 4) Performance metrics, and 5) Objectives.

Table 6. The criteria of inclusion and exclusion



Research articles published in high-quality journals.

Research articles peer reviewed.

Research articles were written in English.

Research articles were focusing on DRA in C-V2X.

Research articles published by the abovementioned publishers.


Research articles were from editorials, keynote speeches, and white papers.

Research articles were not in English language.

Research articles do not peer reviewed.

Research articles were not focusing on issues other than DRA in C-V2X.

Figure 3. Research query

Figure 4. PRISMA flow chart screening process

Figure 5. Publication articles per year

Figure 6. The articles distribution based on the SLR questions

Figure 7. Taxonomy of C-V2X

4. Discussion

The literature study identified various findings that were relevant to each one of the research questions, as will be presented in the following sub-section:

4.1 What is the DRA in LTE-V2X?

In this first subsection, we will discuss the fundamental concepts underlying the LTE-V2X resource configuration. In the second sub-section, study on resource allocation mode 4 LTE-V2X, is discussed in detail.

4.1.1 Resource configuration in LTE-V2X

LTE-V2X uses orthogonal frequency division multiplexing (OFDM) and single carrier frequency division multiple access (SC-FDMA) for its physical layer and medium access control (MAC) layer, respectively. For the most efficient use of the given bandwidth, such as a 10 MHz or 20 MHz channel, it is partitioned into many orthogonal resources throughout the time and frequency domains, as shown in Figure 8.

The signal is broken up into 10 ms frames within the time domain. Consequentially, each frame consists of 10 subframes, each lasting 1 ms, and each subframe comprises two TSs. Resource block pairs (RBP) define the signal in the frequency domain; it contains 12 subcarriers separated by 15 kHz and carries 14 OFDM symbols. A group of RBPs in the LTE-V2X subframe defines a subchannel, all subchannels within an eNB must maintain uniform subchannel sizes, which can be adjusted within the range of 4 to 50 RBPs [26].

When sending their data, individual vehicles have the option of using either one or multiple subchannels. A vehicle can have its cooperative awareness message (CAM) packet transmitted across a subchannel; it is the lowest resource unit that can be given to a vehicle. In C-V2X, the transport block (TB) is sent using a combination of 16-quadrature amplitude modulation (QAM) and quadrature phase-shift keying (QPSK) modulations. On the other hand, the sidelink control information (SCI) is transmitted only utilizing the last one. Both conventional cyclic prefix (CP) and turbo coding are utilized by LTE-V2X.

The data transport channel (TCH) and the SCI are carried by the PSSCH and PSCCH, respectively. The essential information required for the receiver to decode the message successfully is provided by the SCI, i.e., information regarding the Modulation and Coding Scheme (MCS), the Resource Reservation Interval (RRI), the priority of the message, and the resource blocks (RBs) that are occupied by the associated TB. The RRI calculates when the vehicle utilizes the reserved resources for the next transmission and reports the findings to the driver. Both PSSCH and PSCCH are multiplexed within the frequency domain, which means they are sent in the same sub-frame but make use of various frequency resources. This is accomplished through the process of frequency domain multiplexing.

Both the PSSCH and the PSCCH can be configured in two different ways as illustrated in Figure 9. The first configuration is known as "adjacent." It occurs when the PSCCH uses the first two RBs of the designated subchannel, and the PSSCH immediately follows. In the second one, which is called “non-adjacent.” PSSCH and PSCCH are not allocated consecutive RBs within the same subframe; the RB count in a given subchannel might vary [27]. In addition to this data, four DMRS symbols are transmitted within the OFDM subframe for use in channel estimation in conditions of high mobility.

Figure 8. Resource configuration in time and frequency domain for LTE-V2X

Figure 9. Adjacent (A) and nonadjacent (B) PSSCH and PSCCH

4.1.2 DRA mode 4 for LTE-V2X

Figure 10 represents two modes of resource allocation supported by LTE-V2X. These modes rely on the availability of the cellular network infrastructure. Mode 3, operating within cellular coverage, involves the eNB scheduling resources to the vehicles. Recognizing the need for critical safety services even when vehicles are beyond cellular network coverage, 3GPP introduced mode 4. In this mode, vehicles utilized the SB-SPS algorithm to autonomously select their resources, which includes detecting the channel before selecting resources from a list of candidate resources. The SB-SPS is described in greater detail in the study by the authors [28, 29].

The SB-SPS algorithm employs sensing in a pre-configured or configured resource prior to selecting a resource from a candidate resource. The fundamental tenets of this approach involve three primary stages, as delineated in the following manner, and visually shown in Figure 11.

Step 1: When any vehicle reserves and uses a single resource for transmitting a random number of messages one after another. The frequency at which Cooperative Awareness Messages (CAM) are transmitted determines the magnitude of this random number; it is also referred to as the reselection counter (RC). For a periodicity of 10 Hz, the RC is set to a value between [5-15], for a periodicity of 20 Hz, it is set to a value between [10-30], and for a periodicity of 50 Hz, it is set to a value between [25-75].

Step 2: The SCI field encompasses information pertaining to both the periodicity of the CAM as well as the RC value. As a result of having access to this information, the vehicle is able to determine which resources are available for use and which ones are now being used by other vehicles.

Step 3: The value of the RC is reduced by one after every CAM message that is sent out, as this value is kept track of. Upon depletion of the RC counter, a new resource needs to be selected. This selection is not always random; there is a probability (1-P) of keeping the current resource for subsequent transmissions. This probability is denoted by P.

Inside step 1 of the process, the allocation of candidate resources is performed by a vehicle inside a Selection Window (SW), which indicates a specific time interval that identified by [T + T1, T + T2], where T1 is the vehicle processing time that is used to detect and choose candidate resources for transmission within 1 ≤ T1 ≤ 4 being the range of possible values for this parameter. The value of T2 is similarly chosen by the vehicle and must fall within the range of 20 ≤ T2 ≤ 100 for it to be valid. Afterward, the vehicle requires 1 s during the SW to listen in to the channel for the next transmission. The SW is equal to 1000 subframes. The SW for the SB-SPS algorithm is presented graphically in Figure 12.

Figure 10. LTE resource allocation

Figure 11. The SB-SPS algorithm flowchart for DRA LTE-V2X mode 4

Figure 12. The SW of SB-SPS for LTE-V2X mode 4

Step 2 involves the vehicle creating a first list L1 that contains the resources that were previously selected during the SW, with the exception of those resources that a reference signal received power (RSRP) exceeding a predefined threshold. According to the information provided by the SCI, L1 does not take into account the resources that are being used by other vehicles for their subsequent transmissions. At least 20% of the total resources chosen in the first phase should be included on this list. However, if this is not the case, then step 2 is frequently repeated by raising the Th by 3 dB each time.

Finally, step 3 comprises the vehicle generating a list of resources called L2 that provides the minimum values for the received signal strength indication (RSSI). It is necessary that the amount of these resources corresponds to 20% of the total amount of the resources that have already been chosen in the very first step. Therefore, the vehicle will finalize its resource selection within the L2 list using a random number generator. The resource that is selected will then be saved by the vehicle for use in its subsequent transmissions using the RC parameter. Random selection eliminates the possibility of collisions occurring in the event that more than one vehicle chooses the same resource because their RSSI value is the lowest.

In LTE-V2X mode 4, resource distribution can be tailored based on the geographical positioning of zones. This means that one geographical region is capable of being divided into many zones. By employing a spatially based resource reuse strategy that operates on distance-based principles, various resource pools are distributed to zones that are geographically close to one another.

4.2 What are the existing state-of-the-art articles for DRA in LTE-V2X mode 4?

In this context, it is important to point out that mode 4 has been the subject of a significantly larger number of study articles published than mode 3. Due to its decentralized structure, mode 3 presents fewer challenges than mode 4. In contrast, mode 4 is susceptible to a new set of challenges, including collision, interference, and congestion management issues.

The research works associated with mode 4 can be divided into three groups. The first group seeks to evaluate how SB-SPS algorithm parameters affect performance. The second group illustrates modification in the SB-SPS algorithm to improve its performance. The third group presents novel alternatives schemes to the previously proposed SB-SPS algorithm and demonstrates that the performance of these proposed algorithms is superior to the previous method.

4.2.1 First group: Evaluating the performance effect of SB-SPS algorithm parameters

As indicated by the existing research literature, the article by Molina-Masegosa and Gozalvez [30] is the first study that presents an evaluation of the SB-SPS algorithm performance. In this article, the SB-SPS algorithm’s is evaluated in an actual traffic situation in metropolitan scenarios in order to better understand its performance. Through the use of simulations, the authors show that considerable performance deterioration owing to packet collisions can occur in the SB-SPS algorithm. In addition to this, Nabil et al. [31] demonstrated that the RRI has a substantial impact on the performance of the packet data rate. Based on their simulations, the authors conclude that with an increase in the RRI, it leads to a rise in the packet delivery ratio (PDR). This is because the probability of another vehicle utilizing the same resource has decreased. As a result, the PDR grows larger.

However, Molina-Masegosa et al. [32] showed that increasing in reselection probability P can enhance the overall system of the SB-SPS scheme, especially once vehicles are transmitting data frequently (at 10 packets per second in this case). This may be understood when considering the fact that the decrease in the resource reservations is a result of the increase in P, which in turn makes the sensing environment more stable. Simulations, on the other hand, indicate that a decrease in PDR is possible if there is an increase in P when the channel load increases. The decrease in communication range is a direct result of the increased channel load. Due to high vehicle mobility, selecting resources for a long duration increases the chance of encountering other vehicles. These vehicles were previously outside the sensing awareness range and thus not regarded during the initial resource selection process. The SB-SPS algorithm does not take into consideration vehicles that come within a range of the selected resource after the resource has been selected. As a direct consequence, this vehicle is going to be subject to packet collisions, this might lead to a decline in the efficiency and effectiveness of the SB-SPS algorithm, impacting its ability to adequately manage resource allocation and network performance. Furthermore, the study shows that utilizing a low RSSI threshold in step 2 of SB-SPS combined with high-power transmission leads to improved overall system performance.

Simulations have been used to evaluate the performance of the SB-SPS algorithm, which forms the basis of the aforementioned research papers. On the other hand, Gonzalez-Martin et al. [28] made use of analytical models for the goal of their investigation. The authors selected Packet Delivery Ratio (PDR) as a performance metric. This work simulates four common types of errors that could impact the transmissions quality. These errors stem from the fact that the SL interface PC5 operates in a half-duplex (HD) mode, collision issues, and propagation conditions. Another objective of the project is to compare the results obtained from analytical models with those produced by the simulator built on the Veins platform. The study's findings suggest that analytical models can effectively model the mode 4 performance.

McCarthy et al. [33] presented an implementation of through the utilization of an open-source simulator offers an invaluable opportunity to delve into the intricacies of vehicular communication protocols. This implementation has been validated to ensure its accuracy, Enabling the utilization of V2X communications performance by the vehicular networking community. An in-depth analysis of the restrictions of the SB-SPS scheme when it comes to supporting aperiodic application traffic features has been provided, specifically for the European Telecommunications Standards Institute (ETSI) CAM standard as well as the 3GPP application model. Assessment of performance enhancement methods, including parameterization within the SB-SPS mechanism and dedicated aperiodic scheduling techniques, has been conducted as possible solutions for future C-V2X. It has been highlighted that the variability degree in the packet inter-arrival time period significantly impacts the efficacy of these schemes.

In addition to the above, Eckermann et al. [34] created and evaluated an open-source C-V2X mode 4 simulator that was implemented in NS-3. The authors investigated the simulator performance using the PRR and PIR standards. Even in the most catastrophic situations, the simulation findings reveal that the C-V2X mode 4 is highly scalable even in realistic scenarios, with the performance exceeding 3GPP Release 14 standards. In addition, an analysis of the effect of the resource reselection probability and resource reservation period analysis was conducted.

The performance of the LTE-V2X mode 4 algorithm was discussed by Bazzi et al. [29]. The results of three different simulations, each of which takes into account the effects of the PHY and MAC layers independently, are presented. According to the findings, changing any of the characteristics does not seem to make much of a difference in scenarios, but when congestion arises, it may become significant. It has been notified that the optimal number somewhat fluctuates by vehicle density, and thus lowering the percentage of the resources transmitted to the MAC layer from the current value of 20% by the requirements can yield a minimal benefit. Furthermore, some performance enhancements at the PHY layer can be made by adjusting the sensing period, which is currently set to 1 s; Nevertheless, this necessitates a reevaluation of how the channel is sensed, as merely reducing it might be counterproductive, especially considering that only certain nodes may be transmitting at 1 Hz.; and in order to achieve a balance between a reduced update latency and a greater packet reception probability, the keep probability can be modified at the MAC layer. As outlined in the specifications, setting specific quantities for the maximum and minimum number of beacon durations before reallocation is deemed appropriate. The variability of keep probability shows up to be adequate for maintaining system performance.

Using the sensing-based resource allocation approach developed in C-V2X mode 4, Romeo et al. [35] investigated the performance of Decentralized Environmental Notification Message (DENMs) delivery via the PC5 interface under various load scenarios. mode 4's most important characteristics for an adequate configuration have been determined. The results indicate that short-term sensing is superior to long-term sensing because it offers precise channel measurements, and that reducing the SW to fit delay constraints alone is inconvenient until further countermeasures are used as well. Cutting back on resources by selecting the appropriate one to send might be useful in such a scenario. Overall, the study makes a substantial contribution to our knowledge of NR-V2X requirements and C-V2X mode 4's sufficient configuration for handling asynchronous traffic, which is crucial in many cutting-edge vehicle applications.

Chourasia et al. [36] examined how the general system performance in PC5 interface-based C-V2X networks was affected by a variety of different configurations of the RC and pKeep parameters used by the SB-SPS algorithm. According to the findings of the study, a new version of the SPS system, which has been given the name TA-SPS and modifies these parameters in accordance with the existing traffic density, is recommended. Compared to traditional SB-SPS, the utilization of TA-SPS results in a 4% increase in PDR for time-varying traffic cases. This is an improvement over the traditional SB-SPS. RC and pKeep each have a vast range of possible combinations; hence, these parameters must be configured appropriately to meet the performance requirements of ITS systems. Bartoletti et al. [37] examined the influence of CAM packet generation; it investigated scenarios where the interval Tg deviates from the allocation period Tb. Specifically, it considered situations where Tg is either longer or shorter than Tb. The research focused on determining the impact of these factors on overall performance, with a primary emphasis on the Packet Reception Ratio (PRR). Simulation findings indicate that the optimal value of Tb varies based on circumstances, but an overall optimal value is 0.1 s. Additionally, the study demonstrates that mode 4's general performance declines as the CAM packet production interval surpasses the allocation period.

Recent research conducted by Yin and Hwang [38] evaluated the performance of the traditional SB-SPS mechanism in LTE-V2V mode 4. However, they identified limitations in the fixed and unified reselection probability Pk for each VUE. To overcome these limitations, the article proposed an enhanced SB-SPS mechanism that incorporates channel-sensing information to dynamically adjust Pk based on real-time CSI. The proposed algorithm divides Pk into multiple ranges to account for different user scenarios. To validate its effectiveness, simulations were conducted for three scenarios: highways with varying densities, high-speed highways, and congested urban areas. Comparative analysis revealed that the proposed algorithm outperformed the conventional approach in terms of PRR, Zhang et al. [39] investigated the SB-SPS process specified in the standard. Initially, they mathematically described the scheduling methodology and employed PPR as a measure of reliability. To further enhance understanding, the authors leveraged the semi-persistent characteristics of the scheduling to establish a reliability equation with CBR as a variable. Through simulations, they demonstrated the successful representation of the scheduling's reliability by the theoretical curve. Their analyses and simulations led to the conclusion that the reliability of SB-SPS in LTE-V2X exhibits a quadratic function relationship with the CBR. This finding provides valuable insights into the reliability characteristics of SB-SPS and paves the way for future research in this area.

In summary, the primary limitation of the first group's research is the absence of an examination into the effect of the SB-SPS algorithm on the restricted requirements of V2X applications. This includes aspects like the reliability and latency of safety messages under varying channel conditions. Given that DRA mode 4 is considered a foundational mode, addressing these issues would provide valuable insights, this feature needs to be investigated in greater depth. After demonstrating that the SB-SPS algorithm may be susceptible to significant performance issues, it became evident that mode 4 functioning required further enhancements. Therefore, the second group is concerned with the modification of the SB-SPS algorithm for enhancing mode 4 performance.

4.2.2 Second group: Modifying the SB-SPS algorithm to enhance performance

Prior research has identified collision as one of the primary performance issues, as it has a significant influence on overall performance. In the SB-SPS algorithm, if multiple vehicles select the same radio channel during resource selection, a packet collision can occur. While the random selection in the SB-SPS’s last step helps mitigate resource collisions among vehicles, it is not adequate to fully address the issue of the packet collisions probability significantly.

Therefore, Bonjourn et al. [40] presented a novel approach in order to prevent collision issues within the SB-SPS algorithm. It intends to reduce the probability of transmission collisions and enhance the reliability of DRA mode 4 communications. In reality, the overlap in the reselection window may cause a resource collision among vehicles. To overcome this issue, they introduced a CLR technique. In this technique, the counter values are transmitted with each packet transfer. The vehicles are then made aware of upcoming concurrent reselections. Consequently, the algorithm prevents vehicles from reselecting resources, which could lead to resource collisions in the reselection window.

Moreover, Jeon and Kim [41] developed a novel technique for reserving resources to prevent packet collisions. In order to improve the SB-SPS algorithm, a new reservation system was implemented. In this technique, vehicles communicate the reserved resource information utilizing a time-frequency coordinate. This resource is reserved well in advance of its actual use, necessitating several announcements to improve the accuracy of this reservation. Through simulations, the authors demonstrated that this approach outperforms the conventional SB-SPS scheme in terms of both latency and reliability. However, the presented technique may exacerbate the problem of resource overload resulting from the necessary notifications regarding the allocated resource.

He et al. [42] proposed a short-term sensing and reservation scheme, STS-RS. It is utilized and configured immediately prior to the resource selection step. In contrast to the standard SB-SPS algorithm, where any transmitting vehicle can initiate this process during the initial resource in the SW. Vehicle then performs the STS measurements at a predetermined back-off time. Consequently, the outcome of this STS process determines the selection of the right resource. The simulation findings demonstrate that decreasing the likelihood of packet collisions can improve transmission reliability. During resource selection in SB-SPS, vehicles choose channels based on the average power (linear average) sensed across all subchannels within the SW. Nonetheless, Abanto-Leon et al. [43] suggested a novel approach of nonlinear power averaging where the major observations are given higher importance by exponential weighting. This strategy is implemented in steps 2 and 3 in the SB-SPS algorithm. Through simulations, the authors show that their method has a positive influence on PRR performance and improves the reliability of vehicular networks in mode 4.

In the same regard, it is very important to point out that incorrect resource selection may result in packet loss over consecutive transmissions of packets during the resource reserve time. Bazzi et al. [44] characterized this problem as a WBS and attempted to determine its occurrence probability. To circumvent this issue, they propose a modification to the conventional SB-SPS procedure that restricts the maximum length of time of any incorrectly reserved resource. They proposed that each vehicle maintain an additional parameter that specifies the maximum length of time of the resource reserve period. Through findings corroborated within simulations, the authors demonstrated that their proposal outperforms the conventional SB-SPS algorithm.

Molina-Masegosa and Gozalvez [45] drew attention to an additional significant feature associated with the change in packet size. In actuality, V2V communication messages consist of a 300-byte packet followed by four 190-byte packets. Consider that the transmission of 300-byte boxes requires only two subchannels, whereas 190-byte boxes require only one subchannel. In this instance, only one of the two subchannels designated for transmitting packets of 300-bytes, that was reserved throughout RC, would be utilized to send the 190-bytes packet. To address this issue, the authors suggest reserving resources via the RC exclusively for packets of 190-byte, which are the most common. In contrast, with a 300-byte packet, the SB-SPS only utilizes the appropriate resource selection, however this resource is not reserved within the RC. In terms of PDR, the suggested SB-SPS outperforms the standard SB-SPS algorithm. Here, we notice that the authors omit aperiodic traffic application scenarios, such as the DENM.

Sabeeh and Wesolowski [46] published a paper on the AM and AMCD algorithms for reselection of resources. The authors created both techniques to enhance SB-SPS resource reselection in order to address the resource collision issue for distributed resource reselection. They hypothesized that when more than two vehicles in the consciousness region select the same resources, or the channel load exceeds highest threshold, the resource collision problem arises. They compared the performance between both algorithms in a freeway scenario with five distinct zones of vehicle density. In comparison to the AM method, the suggested AMCD algorithm achieves a greater improvement in the PRR and an outstanding collision ratio, as demonstrated by the simulation results.

In addition, Dayal et al. [47] proposed an adaptive SB-SPS protocol known as SPS ++ to improve the performance of on-road safety applications in DRA. Specifically, SPS ++ enables every vehicle to dynamically adjust RRIs according to the availability of channel resources and select appropriate transmission opportunities for timely BSM transmissions at adjusted RRIs, while taking into account various traffic applications. Experiments revealed that the SPS ++ protocol improves road safety performance in all C-V2X test scenarios.

4.2.3 Third group: New alternative methods to mode 4 to improve the SB-SPS algorithm

The third and final group is dedicated to literature proposing new alternative methods for DRA-V2X. These new solutions are considered as incremental improvements on the SB-SPS algorithm.

Sabeeh et al. [48] presented an ERRA approach as a new method. This ERRA algorithm attempts to improve mode 4's reliability by resolving packet collisions and improving PRR. The primary aspect of the proposed algorithm is that every vehicle specifies the positions of all received packets using a random counter, so that each vehicle may forecast the available resources during the subsequent resource selection. Thus, the vehicle is independent of the sensing process. Simulations are used to validate the ERRA method, and the authors demonstrate that it is vastly superior to the conventional SB-SPS technique. The same authors then developed E-ERRA, a new variant of the ERRA algorithm [49]. Its main objective is to resolve the lost reserved resources issue. This issue may arise while vehicles with pre-allocated resources have recently entered or exited the broadcast range prior to reselection. The methods provide alternative resources that the vehicle will utilize if this problem occurs. Through simulations, the authors demonstrate that the algorithm can reduce the collisions rate and boosts the PRR. Similarly, Yang et al. [50], developed a novel method called PRESS. By using the overall RC information, before broadcasting, vehicle must initially predict the channel status for upcoming transmissions prior transmitting. Utilizing the CAM periodicity signals, The PRESS algorithm is capable of estimating the future resource usage status at the time of resource utilization. As a result, a vehicle may select the resources that are least utilized. Simulations show that the proposed algorithm outperforms the SB-SPS method in PRR.

For reducing the probability of collisions, Zhao et al. [51] have presented a novel autonomous cluster-based resource selection technique that breaks the pool of resources into orthogonal resource sets. The vehicles are clustered according to their method of operation. Then, based on the sensing, every single Cluster Head (CH) selects a resource set with minimal collision. They demonstrate that this strategy delivers superior PRR performance in comparison to the legacy SB-SPS algorithm. However, this research is limited by the fact that collision problems may result due to the CH selection technique of resource set.

Another motivational approach to DRA is proposed by Molina-Masegosa et al. [52] with aiming of mitigating the hidden node issue. The authors proposed a novel resource allocation mechanism with the goal of minimizing packet collisions. In this system, vehicle resource selection is dependent on the order of neighboring vehicles and their location. Analytical and simulation evaluations demonstrate that the proposed method outperforms the conventional SB-SPS algorithm. The performance of the suggested DRA scheme could be decreased if the vehicles’ position, upon which it is based, is inaccurate.

Nevertheless, Sahin and Boban [53] investigated resource allocation for vehicles in delimited out-of-coverage areas (DOCA). Imagine a DOCA as a tunnel with no signal from cell towers (Base Stations). However, cell towers at the tunnel entrances can communicate with vehicles just before entering or exiting. This technique for DRA attempts to increase vehicular communication reliability. Therefore, the authors recommend that the BS continue to manage DRA for vehicles entering the tunnel based on predicted vehicle positions and conditions of propagation.

Moreover, Heo et al. [54] presented a novel hybrid DSRC and autonomous C-V2X approach for enhancing the reliability of the IoV networking. The suggested H-V2X scheme makes use of the derived equations of the collision probability to manage the SB-SPS period and adaptively support DRA, allowing C-V2X mode 4 users to maintain a reliable network and efficiently utilize the DSRC communication. C-V2X mode 4 scheduling was given a higher priority than DSRC scheduling due to the fact that it utilizes a more reliable licensed frequency channel that enables a greater transmission range and is much better suited for CAM traffic. In terms of PDR, the H-V2X protocol outperforms previous technologies in the area of interest with the highest traffic density.

Recently, Ha et al. [55] presented the SS and DP-ASTS schemes to enhance PDR performance for DENM and CAM communications. The PDR performance of the DP-ASTS method was enhanced by getting the SD value restricted by the necessary number of data symbols to include the payload of DENM or CAM. Simulation findings indicate that the DP-ASTS strategy may significantly increase PDR performance for both traffic types compared to the current random resource selection scheme for DENM traffic. In the same context, Yin and Hwang [56] proposed a separate resource pool specifically dedicated to CAM and DENM messages for C-V2X transmission. A key aspect of their proposal is the dynamic adjustment of the DENM load, which closely aligns with real-world scenarios. By dynamically adjusting the number of allocated resources for DENM based on the message count, this mechanism ensures that important aperiodic emergency data can have dedicated resources with minimal interference. Additionally, it prevents excessive resource allocation to aperiodic messages, thereby avoiding congestion and preserving resources for periodic messages. Through comprehensive simulations, the researchers concluded that their proposed separate resource pool method effectively enhances the PRR and transmission range of aperiodic messages. Importantly, it achieves these improvements while maintaining a satisfactory PRR for periodic messages. The performance of their proposed method surpassed that of the conventional scheme, further validating the effectiveness and advantages of their approach.

In addition, Asano and Fujii [57] suggested two techniques that, without the need for further information, helped LTE-V2X mode 4 function better. By utilizing the fact that Prsvp reaches 0 when the resource is reselected, the suggested methods can lower the number of packet collisions. Using three RRI patterns, the authors assessed the efficacy of the suggested techniques. In the long run, the suggested PA approach was quite effective and efficiently utilized the resources that were released. The suggested OA technique was quite successful in the short range and did not involve an overlapping SW. Because of the way the two systems interacted, the combination of the PA and OA procedures performed better than the conventional scheme in every range. Moreover, there was no verified decline in performance. Making efficient use of the data in Prsvp, which is utilized in the SB-SPS scheme. While many approaches have been researched to use more information to improve the DRA problem's performance, these approaches necessitate standard-breaking extensions. On the other hand, this study makes use of Prsvp, which is a standard for improving performance without sacrificing compatibility. This method's biggest benefit is that it can be implemented without changing current standards. The two approaches to using Prsvp that the authors suggest are the PA and OA, together with their fusion method, which consistently performs better than each of them by itself. The suggested approach has no drawbacks but might not have a major impact because it is meant to be compatible with current standards.

To conclude this subsection which is related to DRA schemes in LTE-V2X. Although mode 4 has attracted the interest of a significant number of researchers over the past several years, some of these works are assessing the performance effect of SB-SPS mechanism parameters as in the first group, such as in the study by Zhang et al. [39], where the authors present a mathematical model and simulations to increase the reliability curve. Whereas the second group is dedicated to refining the SB-SPS algorithm to bolster its performance, exploring innovative adjustments and optimizations to optimize resource allocation and network efficiency, Dayal et al. [47] extend the SB-SPS in order to accommodate an adaptive RRI, referred as SPS ++, this modification outperform the standard scheme. Moreover, in the third group which propose new alternative methods to mode 4 to improve the SB-SPS algorithm, as in the study by Asano and Fujii [57], the work introduce two reselection methods to improve the system performance.

However, there is still a need for additional research into various areas pertaining to the management of interference and collision avoidance. As mode 4 is considered the cornerstone of C-V2X due to its ability to function without cellular networks, innovative approaches are crucial for its continued development. Figure 13 depicts the distribution of state-of-the-art DRA in LTE-V2X according to the three groups. For the benefit of readers, we provide a summary in Table 7 of the current state-of-the-art regarding the primary ideas for DRA approaches in mode 4.

Figure 13. State-of-the-art of DRA in LTE-V2X

4.3 What is the DRA in NR-V2X?

In this subsection, we will first begin by discussing some of the more fundamental concepts associated with NR-V2X resource allocation. Secondly, we provide a comprehensive discussion on the resource allocation mode 2 utilized by NR-V2X. Last but not least, we will discuss the current state-of-the-art regarding DRA-relevant research studies.

4.3.1 Resource configuration in NR-V2X

In 3GPP specifications Release 16 [18, 58], NR-V2X services and applications shared the same standard physical infrastructure for system resources such as storage, bandwidth, and computing. This was made possible through the use of 3GPP's standard physical infrastructure specification. According to the link shown [59], a SL physical channel is responsible for carrying information that has been created at a higher layer.

In NR-V2X, regardless of the numerology employed, the 10 ms radio frame and 1 ms subframe are identical to those in LTE-V2X [22], as illustrated in Figure 14. In the frequency domain, a physical resource block (PRB) is comprised of 12 successive subcarriers, whereas every TS is comprised of 14 OFDM symbols. Whereas the resource elements (REs) are the most basic and minimal representation of resources, specified in both the time and frequency domains. The RE is characterized by a single OFDM symbol for the time domain and a single subcarrier within the frequency domain. The TS length is dependent on the numerology value and satisfies the condition TS=1/2n, where n is the numerology sequence. For instance, the TS value for numerology 0 is 1 ms, while the value for numerology 1 is 0.5 ms. Table 8 tabulates the main parameters associated with each numerology in NR-V2.

Multiplexing various numerologies is an additional level of NR frame structure flexibility. Numerology multiplexing may accommodate the various applications need. As an illustration, driving safety applications with ultra-low latency needs can utilize a short TS. Nonetheless, infotainment applications need ultra-high throughput, that may be achieved within a long TS.

As illustrated in Figures 15 and 16, the numerology multiplexing of NR-V2X can be accomplished using either the time-division multiplexing (TDM), the frequency-division multiplexing (FDM), or a combination of the FDM and TDM techniques [60]. The bandwidth part method (BWP) enables FDM-based numerology multiplexing. A feature introduced in 3GPP Release 15, this has been recently incorporated into the NR-V2X SL tandard. This addition aims to support UE with limitations in processing power, preventing them from supporting wide bandwidths [22]. The BWP is a chunk of PRBs on a specific channel, configured for numerology include in 1) symbol duration, 2) CP length, and 3) SCS.

Figure 14. The frame structure of NR-V2X numerology

Figure 15. TDM and FDM numerology multiplexing

Figure 16. Mixing the FDM and TDM numerology multiplexing

Table 7. State-of-the-art of DRA schemes in LTE-V2X mode 4



Performance Metric

Traffic Application

Simulation Scenario

Simulation Tool

Findings and Limitations


SB-SPS algorithm evolution.



Urban scenario


High packet collision occurs in the SB-SPS algorithm.


Examine the effect of RRI on the SB-SPS algorithm performance.



Highway scenario


The PDR rises as the RRI increases.


Impact of P and RSRI on the performance of the SB-SPS algorithm.



Highway scenario


P affects SB-SPS algorithm performance depending on channel load, and RSRP to increase mode 4 performance.


Analytical evaluation of the SB-SPS algorithm performance.



Highway scenario


Analytical models adequately depict the performance of C-V2X mode 4.


Examine the restrictions of SB-SPS algorithm in aperiodic traffic.



Highway scenario


The SB-SPS system performance degrades for aperiodic traffic.


Analyzed an open-source mode 4 simulators utilizing PRR and PIR performance criteria.



Urban and Highway scenario


Even in worst-case circumstances, C-V2X mode 4 scales.


Analyzed SB-SPS algorithm parameters and system performance.



Urban and Highway scenario


Changes to certain settings have no noticeable impact, while others, when selected carefully, may significantly improve service quality, and another set of parameters permits balancing reliability and update latency.


Examined DENM delivery performance via the PC5 interface using mode 4's sensing-based resource allocation mechanism under varied load situations.



Highway scenario


Short-term sensing outperforms long-term sensing because it provides more precise channel measurements and lowering the SW to fit delay restrictions is inconvenient without additional countermeasures.


Examined how SB-SPS algorithm pKeep and RC parameters affect system performance in PC5-based C-V2X networks.



Highway scenario


Incorporating TA-SPS results in a 4% increase in PDR.


Examine the effect of aperiodic CAM packet creation on mode 4's overall performance.



Highway scenario


Performance deteriorates when there is an imbalance between CAM packet generation and allocation.


Proposal of a separate resource pool for C-V2X CAM/DENM with dynamic aperiodic data transmission.


Periodic, aperiodic

Highway scenario

Not Detailed

The proposed method outperformance the conventional scheme.


Provided a theoretical representation of the scheduling reliability for LTE-V2X mode 4.



Not Detailed

Not Detailed

The theoretical curve can successfully represent the reliability of the scheduling.


A proposal for a novel CLR mechanism.



Not detailed


Reduce the probability of collision issues.


A proposal for a new reservation system to lessen the collisions probability.



Urban scenario


Superior performance regarding reliability and latency.


A proposal for a novel STS-RS scheme pre-resource selection.


Periodic, aperiodic

Highway scenario


Decrease the packet collision problems.


SB-SPS algorithm employs a novel non-linear power averaging approach.



Urban and Highway scenario


An increase in PRR performance.


Improve the SB-SPS algorithm using a new approach that sidesteps the WBS issue.



Highway scenario


The suggested approach is superior to the SB-SPS algorithm.


New approach for reserving resources that considers the varying packet sizes of transmitted messages.



Highway scenario

Not Detailed

Superior PDR performance compared to the conventional SB-SPS.


Developed AM and AMCD algorithms to enhance SB-SPS resource reselection to address resource collision for autonomous resource selection.



Highway scenario


The AMCD algorithm outperforms AM in PRR and Collision Ratio.


SPS++, an adaptive SB-SPS, improves on-road safety in decentralized V2X networks.



Highway scenario


In all C-V2X situations, SPS++ exceeded standard SB-SPS in on-road safety.


To improve mode 4's reliability, the ERRA algorithm is proposed.



Urban scenario


ERRA outperforms the SB-SPS algorithm.


E-ERRA is an extension of ERRA to tackle the lost reserved resource issue.



Highway scenario


E-ERRA decreases the collisions and enhances the PRR.


PRESS algorithm for mode 4 sensing and resource selection.



Highway scenario


PRESS algorithm is more efficient than the traditional SB-SPS algorithm.


Orthogonal resource sets for autonomous resource selection.


Periodic, aperiodic

Urban and Highway scenario

Not detailed

The PRR performance was significantly enhanced.


Autonomous resource selection based on vehicle location and route order.



Highway scenario


Increased efficiency in C-V2X mode 4 communication.


Proposal of a DOCA-specific algorithm for resource allocation

Multi KPI


Highway scenario


Increased the reliability of DOCA's V2V communication.


A hybrid C-V2X mode 4 and DSRC method is proposed to improve IoV networking reliability and performance.



Not detailed


Improved the PDR based on traffic density range compared to previous technologies.


Proposed SS and DP-ASTS schemes to improve DENM and CAM communication.


Periodic, aperiodic

Not detailed


DP-ASTS can increase DENM and CAM performance in terms of PDR.


Presented the performance of the conventional SB-SPS algorithm for the LTE-V2V mode 4.



Urban and Highway scenario


The proposed algorithm superior to the conventional algorithm and enhance the performance of each scenario.


Proposed two techniques to increase LTE V2X mode 4 performance without extra information to prevent packet collisions.



Highway scenario


The proposed techniques can boost PRR without extra information.

Table 8. Supported numerologies in NR-V2X SL



Symbol Length (µs)

Slot Duration (ms)

SCS (kHz)



Frequency Range

Maximum Carrier Bandwidth (MHz)


5. Conclusion

One of the most important enablers of the IoV concept in ITS is the C-V2X. The DRA in C-V2X significantly impacts the efficiency of V2X communications. In this article, we started by explaining the resource allocation configuration in C-V2X for both LTE and NR technologies focusing on DRA and then provided A comprehensive state-of-the-art of DRA for these technologies. Even though, there are many existing research works from 2017 onwards that related to mode 4 and mode 2 of LTE and NR, respectively, as a DRA were presented. However, mode 4 still presents additional critical issues related to resource collisions and interference. Furthermore, mode 2 requires additional investigation on DRA to meet the QoS requirements and system accomplishments. We also demonstrated the impact of ML and CC on DRA by addressing the problems, policies, and algorithms that were implemented for improving the DRA system efficiency. We also pointed out the primary performance metrics and simulation tools used in the related work. In addition, based on several readings of the studies presented in this review article, we have identified a number of research challenges and limitations related to DRA in C-V2X i.e., faulty sensing, collision likelihood, and resource-efficient allocation that still need attention. Ultimately, we have outlined the challenges, identified open issues, and highlighted promising future directions for research in this field. We have proposed potential solutions leveraging new emerging technologies such as physical layer structure, NOMA based techniques, power consumption, scheduling UE, congestion control, machine-learning-based, and in-band full-duplex in terms of DRA in C-V2X.


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