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A joint cluster-based RRM and Low-latency framework using the full-duplex mechanism for NR-V2X networks
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In this paper, we study the joint optimization problem of the spectrum and power allocation for multiple vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) users in cellular vehicle-to-everything (C-V2X) communication, aiming to maximize the sum rate of V2I links while satisfying the low latency requirements of V2V links. However, channel state information (CSI) is hard to obtain accurately due to the mobility of vehicles. In addition, the effective sensing of state information among vehicles becomes difficult in an environment with complex and diverse information, which is detrimental to vehicles collaborating for resource allocation. Thus, we propose a framework of multi-agent deep reinforcement learning based on attention mechanism (AMARL) to improve the V2X communication performance. Specifically, for vehicle mobility, we model the problem as a multi-agent reinforcement learning process, where each V2V link is regarded an agent and all agents jointly intercommunicate with the environment. Each agent allocates spectrum and power through its deep Q network (DQN). To enhance effective intercommunication and the sense of collaboration among vehicles, we introduce an attention mechanism to focus on more relevant information, which in turn reduces the signaling overhead and optimizes their communication performance more explicitly. Experimental results show that the proposed AMARL-based approach can satisfy the requirements of a high rate for V2I links and low latency for V2V links. It also has an excellent adaptability to environmental change.
We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, and a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network that should be extremely intelligent and capable of concurrently supporting hyperfast, ultrareliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning (ML) will play an instrumental role in advanced vehicular communication and networking. To this end, we provide an overview of the recent advances of ML in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies.
With increasing data traffic requirements in vehicular networks, vehicle-to-everything (V2X) communication has become imperative in improving road safety to guarantee reliable and low latency services. However, V2X communication is highly affected by interference when changing channel states in a high mobility environment in vehicular networks. For optimal interference management in high mobility environments, it is necessary to apply deep reinforcement learning (DRL) to allocate communication resources. In addition, to improve system capacity and reduce system energy consumption from the traffic overheads of periodic messages, a vehicle clustering technique is required. In this paper, a DRL based resource allocation method is proposed with remote radio head grouping and vehicle clustering to maximize system energy efficiency while considering quality of service and reliability. The proposed algorithm is compared with three existing algorithms in terms of performance through simulations, in each case outperforming the existing algorithms in terms of average signal to interference noise ratio, achievable data rate, and system energy efficiency.
With the recent advancement of vehicular ad-hoc networks (VANETs) or the internet of vehicles (IoVs), vehicles are getting more powerful and generating huge amount of traffic data, including computation-intensive and delay-sensitive applications in the vehicular edge computing (VEC) networks, which are difficult to be processed by an individual vehicular node. These resource-demanding tasks can be transferred to another vehicular node with idle computing resources for processing. Due to high mobility and limited resources of vehicular nodes, it is challenging to execute lengthy computation-intensive tasks until completion within the delay constraint. There is a need to provide an efficient task offloading strategies to support these applications. In this paper, an efficient distributed task offloading scheme is proposed to select nearby vehicles with idle computing resources, to process the tasks in parallel by considering some vital metrics, including link reliability, distance, available computing resources, and relative velocity. In order to complete the lengthy computation-intensive tasks in vehicular edge computing networks, a task is divided into several subtasks before offloading. The performance of the proposed scheme is evaluated in several VEC network conditions. Results show that the proposed computation task offloading scheme achieves better performance in latency, throughput, resource utilization and packet delivery ratio than the existing schemes.
Employing machine learning into 6G vehicular networks to support vehicular application services is being widely studied and a hot topic for the latest research works in the literature. This article provides a comprehensive review of research works that integrated reinforcement and deep reinforcement learning algorithms for vehicular networks management with an emphasis on vehicular telecommunications issues. Vehicular networks have become an important research area due to their specific features and applications such as standardization, efficient traffic management, road safety, and infotainment. In such networks, network entities need to make decisions to maximize network performance under uncertainty. To achieve this goal, Reinforcement Learning (RL) can effectively solve decision-making problems. However, the state and action spaces are massive and complex in large-scale wireless networks. Hence, RL may not be able to find the best strategy in a reasonable time. Therefore, Deep Reinforcement Learning (DRL) has been developed to combine RL with Deep Learning (DL) to overcome this issue. In this survey, we first present vehicular networks and give a brief overview of RL and DRL concepts. Then we review RL and especially DRL approaches to address emerging issues in 6G vehicular networks. We finally discuss and highlight some unresolved challenges for further study.
In this paper, the resource allocation for vehicle-to-everything (V2X) underlaying 5G cellular mobile communication networks is considered. The optimization problem is modeled as a mixed binary integer nonlinear programming (MBINP), which minimizes the interference to 5G cellular users (CUs) subject to the quality of service (QoS), the total available power, the interference threshold, and the minimal transmission rate. To achieve that, the original MBINP is decomposed into three steps: transmission power initialization, subchannel assignment, and power allocation. Firstly, the minimum transmission power required by the V2X users (VUs) is set as the initial power value. Secondly, the Hungarian algorithm is used to obtain the appropriate subchannel. Finally, an optimization mechanism is proposed to the power allocation. Simulation results show that the proposed algorithm can not only ensure the minimal transmission rate of VUs but also further improve the CUs’ channel capacity under the premise of guaranteeing the QoS of the CUs.
In device‐to‐device (D2D) communication underlaying cellular networks, the D2D user equipments (DUEs) can communicate by sharing the radio resources assigned to cellular user equipments. D2D communication is expected to improve the network throughput and save more energy for the user equipments. However, D2D communication causes severe interference to both the cellular users and other DUEs which is a challenge to radio resource allocation. The authors propose joint channel and power allocation algorithm to maximise the network achievable throughput, control the interference from the DUEs transmissions to cellular users and improve the energy efficiency of the user equipments. The authors formulate the problem and solve it using convex optimisation methods to select DUEs and set their powers in each channel. Simulation results show that the proposed algorithm can offer near‐optimal performance and outperforms the comparable algorithms especially in terms of achievable throughput even with markedly reduced complexity levels.
Internet of Things is a promising paradigm that provides the future network of interconnected devices. Device‐to‐Device (D2D) communication, which is considered as an enabler for vehicle‐to‐everything applications, has become an emerging technology to optimize network performance. In this paper, we study the Radio Resource Management (RRM) issue for D2D‐based Vehicle‐to‐Vehicle communication. The RRM key role is to assure the proficient exploitation of available resources while serving users according to their quality of service parameters. An Ant Colony Optimization (ACO)‐based Resource Allocation (ACORA) scheme is proposed in this paper. Swarm intelligence algorithm ACO is adopted to reduce the computational complexity while realizing satisfactory performance. Simulation results show promising performance of our proposed ACORA scheme. In this paper, we study the radio resource management issue for device‐to‐device‐based vehicle‐to‐vehicle communication. An ant colony optimization‐based resource allocation scheme is proposed to improve the overall network sum rate while satisfying the quality of service requirements of cellular and vehicular users. Simulation results show promising performance of the proposed algorithm.
The paradigm of Device-to-Device (D2D) communication has recently emerged as a potential LTE-Advanced cellular network technology to cope with the spectrum crunch issue in future ultra-dense heterogeneous networks. This paper proposes a novel algorithm for D2D resource channel allocation that minimizes interference among D2D, cellular and small cells while maximizing overall network capacity. Resource allocation is a mixed integer non-linear program (i.e., an NP-hard problem). The authors developed a Sequential Max Search (SMS) algorithm that is less computationally demanding approach for D2D resource allocation. Results demonstrate that SMS achieved a significant space search reduction than that of brute-force algorithm, but SMS had sub-optimal solution.
Recently, the 3rd Generation Partnership Project has developed vehicle-to-everything communication to manage and advance future traffic safety and mobility. In this study, we propose a position-based resource allocation scheme for vehicle-to-vehicle communication. The proposed scheme allocates a different frequency and time resources based on vehicle speed, density, direction, and position. Our performance analysis is based on urban and freeway cases involving periodic message transmission. Simulation results show that the proposed scheme can improve the cumulative distribution function of packet reception ratio (PRR) and average PRR.
In this paper, a novel proximity and load-aware resource allocation for vehicle-to-vehicle (V2V) communication is proposed. The proposed approach exploits the spatio-temporal traffic patterns, in terms of load and vehicles' physical proximity, to minimize the total network cost which captures the tradeoffs between load (i.e., service delay) and successful transmissions while satisfying vehicles's quality-of-service (QoS) requirements. To solve the optimization problem under slowly varying channel information, it is decoupled the problem into two interrelated subproblems. First, a dynamic clustering mechanism is proposed to group vehicles in zones based on their traffic patterns and proximity information. Second, a matching game is proposed to allocate resources for each V2V pair within each zone. The problem is cast as many-to-one matching game in which V2V pairs and resource blocks (RBs) rank one another in order to minimize their service delay. The proposed game is shown to belong to the class of matching games with externalities. To solve this game, a distributed algorithm is proposed using which V2V pairs and RBs interact to reach a stable matching. Simulation results for a Manhattan model shown that the proposed scheme yields a higher percentage of V2V pairs satisfying QoS as well as significant gain in terms of the signal-to-interference-plus-noise ratio (SINR) as compared to a state-of-art resource allocation baseline.
Direct device-to-device (D2D) communication has been proposed as a possible
enabler for vehicle-to-vehicle (V2V) applications, where the incurred
intra-cell interference and the stringent latency and reliability requirements
are challenging issues. In this paper, we investigate the radio resource
management problem for D2D-based V2V communications. Firstly, we analyze and
mathematically model the actual requirements for vehicular communications and
traditional cellular links. Secondly, we propose a problem formulation to
fulfill these requirements, and then a Separate Resource Block allocation and
Power control (SRBP) algorithm to solve this problem. Finally, simulations are
presented to illustrate the improved performance of the proposed SRBP scheme
compared to some other existing methods.
SUMO is an open source traffic simulation package
including net import and demand modeling components. We
describe the current state of the package as well as future
developments and extensions. SUMO helps to investigate
several research topics e.g. route choice and traffic light
algorithm or simulating vehicular communication. Therefore
the framework is used in different projects to simulate
automatic driving or traffic management strategies.
The performance of IEEE 802.11 in multi-hop wireless networks depends on the characteristics of the protocol itself, and on those of the upper layer routing protocol. Extensive work has been done to analyze and evaluate the performance of single hop networks under saturated traffic conditions, either through simulations or mathematical modeling. Little work has been done on the analysis of the performance of IEEE 802.11 protocol under the unsaturated traffic conditions that arise in multi-hop networks. The paper proposes analytical models and scenarios for such an analysis. ns-2 simulations with different network configurations validate the proposed models for performance metrics such as throughput, message delay, average queue length, and energy consumption. Simulation results show that the proposed models work well. Key to the modeling of the multi-hop networks is a treatment of the upper layer routing protocols that can affect the network performance through the way they forward packets and the impact of that on the traffic load. The model proposed takes into account the impact of the upper layer routing protocol by introducing a packet acceptance factor with which each relay station accepts packets from the wireless medium before forwarding the same.
The paradigm of Internet of Vehicles (IoV) as an extension to the IoT concept can assist the development of smart cities. IoV relies on vehicular communications to allow vehicles to communicate in real time with other vehicles, roadside infrastructure and pedestrians. The main goal is to enable not only road safety services but also time-constrained IoT applications. In this context, Cellular-Vehicle-to-Everything communication (C-V2X) is a prominent technology to accomplish IoV goals. Currently there are two versions of C-V2X available, LTE-V2X and NR-V2X, and in both, mechanisms of resource allocation play an important role in their performance. Therefore, our goal in this paper is to present an extensive state-of-the-art of resource allocation on C-V2X technology. Thus, we first present the main technical aspect related to LTE-V2X and NR-V2X technologies. Afterwords, we present and categorize the related works for both technologies, pointing out their main differences, advantages and drawbacks. Finally, we present the challenges, open issues and future technical trends related to resource allocation in C-V2X.
With the growing interest in both public and private 5G services based on millimeter wave (mmWave) communication for indoor usage scenarios such as smart factories, site design specialists are seeking sophisticated methods and tools for simulating indoor radio coverage based on highly accurate path loss prediction models. Although machine learning approaches can be used in path loss modeling thanks to the highly accurate prediction capability, their performance can be limited by the size of available measurement data set used for training. In this paper, we propose new approaches to train path loss models in the few-shot learning scenarios of smart factories. The proposed approaches are based on meta-learning with slight modifications to perform fine-tuning over an entire train data set rather than a meta-test data set. It is shown that the indoor path loss models based on convolutional neural networks (CNNs) trained by meta-learning based on three different meta-train task assignment schemes outperform both a conventional CNN model and an empirical model.
In the past few years, the industry and academia have been working hard to establish and standardise vehicle-to-everything (V2X) communications, which is one of the vital emerging services for next generation wireless networks. Therewith, to control radio resource properly with balanced implementation complexity, a full-duplex V2V discovery mechanism in a 5G-V2X network based on a random backoff procedure is proposed to better utilize radio resources on which nearby vehicles establish their links. The main aims are to reduce complexity, latency, improve throughput and guarantee stability for V2V communication. The unemployed cellular network channel operates in full-duplex mode and leaves the channel as soon as they are informed that someone is in place through latency and throughput analysis technique. The channel sensing and identification are done in a cooperative manner before transmission. Furthermore, two effective resource distribution algorithms are proposed that grant the optimum resource distribution for V2V and V2I-Users. The detection and the throughput of full-duplex V2V communication in the proposed scheme have been formulated and the performance of the proposed scheme and validity of corresponding analysis are verified through simulation experiments.
In this paper we investigate collision detection and avoidance in a vehicular network of full duplex (FD) operating nodes. Each vehicle in this network senses the energy level of the channel before and during its transmission. The measured energy is compared against a dynamic threshold which is preset based on the target detection probability, transmitter's power, sensing time and self-interference cancellation (SIC) capability of the vehicles' on board units (OBU). Probabilities of detection and false alarm, detection threshold before and during transmission, and effect of residual self interference (SI) on these metrics have been formulated. It is shown that the proposed scheme would experience a shorter collision duration. Meanwhile, it also requires a minimum SIC capability for acceptable operation, below which, system throughput would be poor due to high false alarm probability. Numerical simulations verify the accuracy of our analysis. They also illustrate that the proposed model perform better than the fixed threshold strategy. A trade-off between half duplex (HD) and FD has been found and the scheme would be applicable even if SIC capability of OBUs is relatively poor, with no need for complicated and expensive devices for future vehicular communication.
Recently, Vehicle-to-Everything (V2X) communications has attracted much attentiveness due to its ubiquitous content sharing and massive data exchange among vehicular users (V-UEs) with the growth of the mobile communication technologies. Vehicular networks is one of the emerging applications for Long-Term Evolution (LTE) which is a capable technology to be useful by different applications due to its Quality-of-Service (QoS) support, high data rate, reliability and high penetration. As proposed by 3GPP the device-to-device (D2D) communication based on LTE network is also a good candidate to be applied for Vehicle-to-Vehicle (V2V) communications.
The cellular Vehicle-to-Everything (C-V2X) radio access technology, specified by the Third Generation Partnership Project (3GPP), enables direct communications between vehicles, using the sidelink radio resources over the PC5 interface. In C-V2X Mode 4, a vehicle autonomously selects the resources and keeps them for consecutive transmissions before a resource reselection is triggered, according to a sensing-based semi-persistent scheduling mechanism. A wrong estimation of the resource occupancy status may lead to persistent packet losses, due to collisions with simultaneous transmissions from other vehicles. In this paper, we propose to provide vehicles with on-board full-duplex transceivers and harness this capability (i) to make the sensing phase more accurate, (ii) to make a more informed resource reselection decision, and (iii) to enhance packet decoding while transmitting. Achieved simulation results show that the proposal provides several improvements in terms of packet transmission reliability and timeliness when compared against the legacy C-V2X Mode 4.
In vehicular ad hoc networks (VANETs), communication links break more frequently due to the high-speed vehicles. In this paper, a novel cluster-based VANET oriented evolving graph (CVoEG) model is proposed by extending the existing VoEG model to improve the reliability of vehicular communications. Here, the link reliability is used as a criterion for cluster members (CMs) and cluster heads (CHs) selection. The proposed CVoEG model divides VANET nodes (vehicles) into an optimal number of clusters (ONC) by using Eigen gap heuristic. In a given cluster, a vehicle will be selected as a CH, if it has a maximum Eigen-centrality score. Based on the CVoEG model, a reliable routing scheme called CEG-RAODV is proposed to find the most reliable journey (MRJ) from source to destination. Our simulation results show that the proposed scheme significantly outperforms the existing schemes in terms of reliability, reliable routing request (RRR), packet delivery ratio (PDR), end to end (E2E) delay, and throughput.
In vehicular ad hoc networks (VANETs), cellular
vehicle-to-everything (C-V2X) is an emerging technology for
communications between vehicle-to-infrastructure, vehicle-to-pedestrian,
and vehicle-to-network which improves traffic efficiency,
road safety, and the availability of infotainment services.
Herein, a novel V2V-enabled resource allocation scheme based
on C-V2X technology is proposed to improve the reliability and
latency of VANETs. The key idea is that V2V communications
based on cellular-V2X technology among vehicles remove the
contention latency and can assist for longer distance communications.
Particularly, we propose a hybrid architecture,
where the V2V links are controlled by the cellular eNodeB in
the overlay scheme. In this scheme, every vehicle periodically
checks its packet lifetime and requests the cellular eNodeB to
determine V2V links. The optimum resource allocation problem
at the cellular eNodeB is to choose optimum receiver vehicles to
determine V2V links and allocate suitable channels to minimize
the total latency. This problem is equivalent to the maximum
weighted independent set problem (MWIS-AW) with associated
weights, which is NP-hard. In order to compute the weights,
an analytical approach is developed to model the expected latency
and packet delivery ratio. Moreover, a greedy cellular-based V2V
link selection algorithm is proposed to solve MWIS-AW problem
and develop a theoretical performance lower bound. Simulation
results show that the proposed scheme significantly outperforms
the existing schemes in terms of latency, throughput, and packet
delivery ratio.
In this paper we propose a novel adaptive scheme for full duplex communication of secondary users (SU) in a cognitive radio network. The secondary network operates adaptively in three modes; Cooperative Sensing (CS), Full Duplex Transmit and Sensing (FDTS), and in-band bidirectional Full Duplex Transmit and Receive (FDTR). In the CS mode, the secondary nodes will detect the signal of a primary user (PU) through a novel cooperative MAC protocol and will decide the system's mode of operation in the subsequent spectrum hole. This adaptive decision is based on dual-threshold detection (DTD) introduced for the first time. When the primary's signal at SU's receivers is weak, the system switches to the FDTS mode to avoid higher collisions probability and long or endless collision durations. In the FDTS mode, one of SUs senses the PU activity continuously whilst transmitting to another node. When the channel conditions allow, the system switches to the FDTR mode, in which the secondary users would communicate bidirectionally in an asynchronous full duplex manner. The novel idea of asynchronous transmission in this mode will result in decreased maximum and average collision durations. Analytical closed forms for probability of collision, average collision duration and cumulative collision duration, as well as throughput of the SU network are derived, and performance of the proposed scheme in terms of the above-mentioned metrics, its effectiveness, and advantages over conventional methods of sensing and transmission are verified via simulations.
This paper evaluates the use of full-duplex nodes to increase the performance of a vehicular ad hoc network (VANET) composed of one transmitter, multiple full-duplex relays and one destination, where the best relay is selected to assist communication. With the use of full-duplex communication, it is possible to obtain a better use of the radio resources, eliminating the multiplexing loss of half duplex systems. We consider Nakagami-m fading, aimed at analyzing the schemes in scenarios with line-of-sight and where the channel condition is critical, for instance a sub-Rayleigh channel, which are typical propagation scenarios to model vehicular networks. Furthermore, the analysis includes the self-interference at the relay node. Results presented in terms of outage probability shown that the full-duplex scheme is suitable for improving communication over vehicular channels.
Vehicular Ad-hoc Networks (VANETs) are an emerging field, whereby vehicle-to-vehicle communications can enable many new applications such as safety and entertainment services. Most VANET applications are enabled by different routing protocols. The design of such routing protocols, however, is quite challenging due to the dynamic nature of nodes (vehicles) in VANETs. To exploit the unique characteristics of VANET nodes, we design a moving-zone based architecture in which vehicles collaborate with one another to form dynamic moving zones so as to facilitate information dissemination. We propose a novel approach that introduces moving object modeling and indexing techniques from the theory of large moving object databases into the design of VANET routing protocols. The results of extensive simulation studies carried out on real road maps demonstrate the superiority of our approach compared with both clustering and non-clustering based routing protocols.
Direct device-to-device (D2D) links are proposed as a possible enabler for vehicle-to-vehicle (V2V) communications, where the incurred intracell interference and the stringent latency and reliability requirements are challenging issues. In this paper, we investigate the radio resource management problem for D2D-based V2V communication. First, we analyze and transform the latency and reliability requirements of V2V communication into optimization constraints that are computable using only the slowly varying channel information. This transformation opens up the possibility of extending certain existing D2D techniques to cater to V2V communication. Second, we propose a problem formulation that fulfills the different requirements of V2V communication and traditional cellular communication. Moreover, a Separate resOurce bLock and powEr allocatioN (SOLEN) algorithm is proposed to solve this problem. Finally, simulations are presented to evaluate different schemes, which illustrate the necessity of careful design when extending D2D methods to V2V communication, as well as show promising performance of the proposed SOLEN algorithm.
Position-based routing, as it is used by protocols like Greedy Perimeter Stateless Routing (GPSR) [5], is very well suited for highly dynamic environments such as inter-vehicle communication on highways. However, it has been discussed that radio obstacles [4], as they are found in urban areas, have a significant negative impact on the performance of position-based routing. In prior work [6] we presented a position-based approach which alleviates this problem and is able to find robust routes within city environments. It is related to the idea of position-based source routing as proposed in [1] for terminode routing. The algorithm needs global knowledge of the city topology as it is provided by a static street map. Given this information the sender determines the junctions that have to be traversed by the packet using the Dijkstra shortest path algorithm. Forwarding between junctions is then done in a position-based fashion. In this short paper we show how position-based routing can be aplied to a city scenario without assuming that nodes have access to a static street map and without using source routing.
Completing the First Phase of the 5G evolution
- Qualcomm
- Relase
Qualcomm, 3GPP Relase 17: Completing the First Phase of the 5G evolution,
San Diego CA, 2022.
The 5G Evolution: 3GPP Release 16-17, 5G Americas, White Paper
3GPP, The 5G Evolution: 3GPP Release 16-17, 5G Americas, White Paper, 2020.