Vehicular User Equipment Research Articles

Optimizing V2X Communication: Spectrum Resource Allocation and Power Control Strategies for Next-Generation Wireless Technologies

The upcoming wireless technology developments in the next generations are expected to substantially transform the vehicle-to-everything (V2X) communication network. The challenge of limited spectrum resources in V2X communication, caused by the need for high data rates, necessitates a thorough analysis of spectrum resource allocation and power control. This complex problem falls under the domain of mixed-integer nonlinear programming; a strategic approach is implemented to overcome these issues, which divides the main challenge into two sub-problems. The issue of resource allocation is addressed by implementing a multiaccess spectrum allocation method, which is deliberately designed to optimize the utilization of the spectrum resources that are currently accessible. Concurrently, the power control issue is resolved by employing a continuous convex approximation technique, which effectively converts non-convex power-allocation issues into convex equivalents. This approach helps to alleviate interference between users. Finally, the simulation results prove that the proposed approaches can improve vehicle performance. The algorithms proposed in this article significantly improve the system throughput and access rate of vehicular user equipment (VUEs) while ensuring the data rate of cellular user equipment (CUEs).

Target-to-User Association in ISAC Systems With Vehicle-Lodged RIS

Target-to-user (T2U) association is a prerequisite to fully exploit the potential of the sensing function in communication-centric integrated sensing and communication (ISAC) systems, e.g., for beam and blockage management. This letter proposes to purposely mount a RIS on the roof of the vehicular user equipment (VUE), which can serve as an intentional back-reflector towards the base station. By controlling the reflection pattern over time, it is possible to transmit information to the sensing system, i.e., back-reflection as bit 1, no back-reflection as bit 0. The VUEs are configured to back-reflect a Hadamard code sequence, which enables T2U association. The numerical results confirm the validity of our proposal.

Cost-Effective Task Offloading in NOMA-Enabled Vehicular Mobile Edge Computing

Nonorthogonal multiple access (NOMA) and mobile edge computing (MEC) are two key emerging technologies for vehicular networks, where NOMA allows multiple vehicular user equipments (VUEs) to share the same wireless resources, and thus to enhance the spectrum utilization and system capacity, and MEC permits VUEs to offload their complex applications to MEC servers, and thus to provide support for computationally intensive intelligent applications. In this article, a NOMA-based vehicle edge computing (VEC) network model is proposed, and the cost minimization problem is constructed. Under the premise of ensuring the delay tolerance of all VUEs, the total system cost is minimized through the joint optimization of offloading decision-making, VUE clustering, subchannel and computation resource allocation, and transmission power control. Since the proposed problem is a mixed-integer nonlinear programming problem, which is difficult to solve, we decouple it into two subproblems and propose two heuristic algorithms to solve the task offloading and the MEC resource assignment problem, respectively, and finally, we obtain the closed-form solutions for cloud-related optimization problems through simple analysis. Simulation results show that the proposed joint algorithm is superior to other baseline algorithms in terms of system cost minimization.

Resource Allocation for URLLC Service in In-Band Full-Duplex-Based V2I Networks

This paper investigates the first resource management problem for vehicular-to-infrastructure (V2I) networks under the in-band full-duplex (IBFD) backhauling scheme with guaranteed ultra-reliable and low-latency (URLLC) service. The considered networks suffers from interference caused by three node transmission in IBFD scheme and the mobility of vehicular user equipments (VUEs). The resource allocation problem is formulated to jointly optimize VUE association, resource block assignment (RA) and power allocation (PA), while satisfying the reliability and latency constraints at the same time. The formulated problem is a mixed integer non-linear problem with non-convex objective function and constraints. Finding globally optimal solution for this type of problem is still an open problem. To develop tractable solutions, the original problem is firstly simplified using derived equivalent expression for the objective function. The proposed method then decomposed it into RA and PA sub-problems. In each iteration, the PA sub-problem is solved for a set of given PA solution; while the solution for PA sub-problem is searched under determined RA results. The RA and PA sub-problems are solved iteratively until the converging condition is achieved. Theoretical analysis proves that the proposed method achieves Nash-stable equilibrium and local optimality for RA and PA sub-problems respectively. Simulation results verify the effectiveness of the derived performance analysis and demonstrate that the proposed algorithm outperforms the state-of-the-art algorithms in the literatures.

Performance Limits of Visible Light-Based Positioning for Internet-of-Vehicles: Time-Domain Localization Cooperation Gain

In this paper, we aim to give a unified performance limit analysis of the visible light-based positioning (VLP) for a vehicular user equipment (UE), which will help to understand the essence of time-domain localization cooperation and gain insights into how to improve the performance limit of the vehicular VLP system. This is challenging due to the complex system models and the complex dependency between UE location performance and orientation performance. To achieve the above goal, we will first characterize the closed-form error bounds of the UE location and orientation at each time slot, respectively, in terms of Fisher information. Generally, the VLP error will propagate over time as the vehicular UE moves, and hence the VLP error at the current time slot is affected by the VLP performance at the previous time slot, the UE mobility and the channel quality. Based on the obtained VLP error bounds, we then reveal the impact of prior UE location knowledge, UE mobility and signal-to-noise-ratio on the VLP performance. Furthermore, the time-domain evolution of the VLP error is studied, where the convergence of the time-domain VLP error evolution is established and its closed-form stable state is quantified, which will shed light on the long-term performance of the vehicular VLP system.

Data Aggregation in UAV-Aided Random Access for Internet of Vehicles

Recently, the Internet of Vehicles (IoV) has been employed as an enabling technology for smart transportation, which can be further enhanced by integrating space–air–ground-integrated networks (SAGIN). Since the data packets of vehicular user equipments (VUEs) are generally short, random access is usually considered for VUEs to connect to the network. However, collisions caused by the multiple VUEs initiating random access simultaneously are inevitable. To relieve the performance degradation by collisions, data aggregation can be carried out in IoV, where aggregated packets can be relayed to a base station. In this article, we first propose an aggregators-aided random access scheme for IoV, where unmanned aerial vehicles (UAVs), as one of the key components in SAGIN, are deployed as data aggregators to help transmissions of VUEs. Then, a semi-Markov chain is used to analyze the average number of aggregated packets, and the metric of the average data to overhead ratio (ADOR) is presented to evaluate the efficiency of aggregation. Finally, the altitude of UAVs and the duration of data aggregation are optimized to maximize ADOR. By numerical simulations, the accuracy of the analysis as well as the effectiveness of the proposed scheme are validated.

Deep Reinforcement Learning-Empowered Resource Allocation for Mobile Edge Computing in Cellular V2X Networks

With the rapid development of vehicular networks, vehicle-to-everything (V2X) communications have huge number of tasks to be calculated, which brings challenges to the scarce network resources. Cloud servers can alleviate the terrible situation regarding the lack of computing abilities of vehicular user equipment (VUE), but the limited resources, the dynamic environment of vehicles, and the long distances between the cloud servers and VUE induce some potential issues, such as extra communication delay and energy consumption. Fortunately, mobile edge computing (MEC), a promising computing paradigm, can ameliorate the above problems by enhancing the computing abilities of VUE through allocating the computational resources to VUE. In this paper, we propose a joint optimization algorithm based on a deep reinforcement learning algorithm named the double deep Q network (double DQN) to minimize the cost constituted of energy consumption, the latency of computation, and communication with the proper policy. The proposed algorithm is more suitable for dynamic scenarios and requires low-latency vehicular scenarios in the real world. Compared with other reinforcement learning algorithms, the algorithm we proposed algorithm improve the performance in terms of convergence, defined cost, and speed by around 30%, 15%, and 17%.

How Much Can Radio Resource Allocation Help to Improve Secrecy Capacity of V2V Underlay Cellular Networks?

Vehicle-to-vehicle (V2V) underlay cellular communication plays an important role in the fifth-generation (5G) and beyond communications and its security issues attract a lot of attention. Physical layer security, which explores channel characteristics to protect wireless communications, is very attractive due to its low latency. This paper focuses on secrecy capacity maximization in V2V underlay cellular communications, where the secrecy capacity of vehicular user equipments (VUEs) is optimized first, followed by sum proportional fairness function optimization for both VUEs and cellular user equipments (CUEs). The optimization problems can be solved in two sub-problems, e.g., power and subcarrier allocation problems. The power allocation can be done with the first strategy using a one-dimensional searching algorithm, whereas a genetic algorithm (GA) is used to solve the second optimization problem. In addition, as VUEs share uplink resources with CUEs, the co-channel interference exists and the subcarrier allocation sub-problem is a 3D search problem, which is solved by a two-step iterative Hungarian algorithm (IHA). Finally, simulations are performed to validate the performance of the proposed schemes.

Beyond Vision: Hidden Car Detector With On-Demand Relaying in Vehicular Communications

Cellular Vehicle-to-Vehicle (C-V2V) communications take autonomous driving technology to the next level by allowing a Vehicular User Equipment (V-UE) to receive Cooperative Awareness Messages (CAMs) from other V-UEs, and enable the V-UE to see beyond what is detectable by vision-based sensors, thereby preventing accidents and ensuring user safety. However, there remains a fundamental limitation in the conventional CAM broadcasting since a transmitter (TX) V-UE cannot confirm whether its CAM is successfully received at other V-UEs. Without a feedback process, a significant uncertainty arises in CAM reception, posing a critical threat to user safety. To address this threat, we propose Beyond-Vision, an effective C-V2V on-demand relay system that allows CAMs that are not well received at nearby V-UEs to be better received. Through simulation that reflects realistic vehicle mobility and road environments in urban scenarios, we verify the superiority of Beyond-Vision over the conventional C-V2V, which improves performance by up to 215% in terms of message reception ratio (MRR) within a communication range under Non-Line-Of-Sight (NLOS) channels.

Resource Allocation for 5G-Enabled Vehicular Networks in Unlicensed Frequency Bands

In this paper, a resource allocation problem is formulated to maximize the throughput of vehicular user equipments (VUEs) in both licensed, and unlicensed frequency bands under constraints of reliability, and latency for vehicular communications as well as the Quality of Service (QoS) for WiFi network based on a network system with coexisting VUEs, and WiFi user equipments (WUEs). In the system, the VUEs are able to access to the unlicensed frequency bands, and the interference among the VUEs, and WUEs are mitigated by the listen-before-talk (LBT) scheme or the duty cycle scheme. Due to the mixed integer nonlinear programming (MINLP) objective in the optimization problem, the problem is hard to be solved directly. Instead, we solve the problem by two steps. The number of VUEs offloaded to unlicensed frequency bands as well as the time factor for duty cycle scheme is firstly determined, and then, the optimization problem is converted into a convex optimization problem, which is solved by the proposed Lagrange Duality Method (LDM). Numerical results show the efficiency of our proposed application scenario compared to case with only LTE system or only WiFi system. Moreover, compared to traditional Greedy algorithm, the proposed algorithm performs better in terms of throughput with a guaranteed QoS of WUEs.

Resource Allocation for D2D-Based V2X Communication With Imperfect CSI

Different from the traditional device-to-device (D2D) communication, vehicle-to-everything (V2X) communication is characterized by the high mobility of vehicles, which introduces the fast variations of the channel state information (CSI) such that the accurate CSI is difficult to be obtained. Unlike other previous work which focuses on the perfect CSI, in this article, we investigate the joint power control and resource allocation problem for the D2D-based V2X communication in a more practical case where the CSI is imperfect. We aim at maximizing the sum ergodic capacity of all vehicular user equipments (VUEs) with imperfect CSI under the minimum signal-to-interference-plus-noise-ratio (SINR) requirements of cellular user equipments (CUEs) and the outage probability constraints of VUEs. We derive an approximate closed-form expression of VUE's ergodic capacity based on Jensen's inequality and propose a simple and efficient lower bound-based power control approach to solve the power control problem with low computational complexity. Based on the optimal power allocation results, we further propose an enhanced Gale-Shapley algorithm to solve the spectrum resource allocation problem, which is tailored to the scenario where each VUE can reuse spectrum resources of multiple CUEs but the resource of each CUE can be shared with one VUE at most and the maximum number of reuse CUEs for each VUE is limited by the maximum transmit power of the VUE. The numerical results verify the tightness of the proposed algorithm and demonstrate the proposed algorithm can enhance the sum ergodic capacity of VUEs efficiently and effectively.

MmWave-NOMA-Based Low-Latency and High-Reliable Communications for Enhancement of V2X Services

The enhanced vehicle-to-everything (eV2X) systems have stricter constraints of latency and reliability than before. To meet the low latency and high reliability (LLHR) demands, the investigation of semi-persistent scheduling (SPS) is of great significance. For the first time, we integrate millimeter wave (mmWave) and non-orthogonal multiple access (NOMA) into SPS strategy. The proposed strategy includes the beam division and user clustering process, the power allocation process, and the vehicular user equipment (VUE) – resource block (RB) matching process. Firstly, the joint-optimization problem in terms of user scheduling indicators and power allocation factors is formulated to minimize the SPS period. This problem is then proved to be non-deterministic polynomial hard (NP-hard) by being reduced into a vertex coloring problem. To solve it with polynomial complexity, we divide it into two subproblems and calculate them by iteration. For one subproblem, the LLHR power control algorithm is proposed to solve the non-convex reliability-optimization problem, which can provide an evaluation indicator for the user scheduling. For the other subproblem, the beam division and VUE clustering algorithm is designed to reduce the complexity of the VUE-RB matching. After that, the matching problem with peer effects is solved by the proposed union-based matching algorithm. The analytical results on stability, convergence and computational complexity are presented, and simulation results show that the proposed SPS strategy outperforms the existing schemes.

Dynamic Scheduling Algorithm based on NOMA Access and Priority Assignment for V2X communications

Nowadays, heterogeneous and stringent requirements for the fifth generation (5G) mobile communication system are imposed to adapt with the fast growing usage of the mobile equipment including connected vehicles and rapid development of IoT. As a consequence, the limitation and inefficient using of the spectrum becomes a major problem to meet the demand of mobile broadband, extreme capacity and high throughput. Thus, to handle the challenges of access collisions, the scarcity of spectral efficiency due to the limited bandwidth and massive connectivity, non-orthogonal multiple access (NOMA) schemes have been introduced as a potential solution for 5G wireless networks. Accordingly, this paper presents a new optimal scheduling algorithm for V2X connections called Dynamic Scheduling Algorithm based on NOMA and Priority Assignment for vehicular Networks (DSA-PA-NOMA), which improves performances in terms of Quality of Service (QoS), throughput and bit error rate (BER). The proposed algorithm consider the traffic classification imposed by V2X exigency and taking into account the channel conditions of Vehicular User Equipment (VUE) expressed by the Signal-to-Interference-plus-Noise Ratio (SINR) value.

Three-Dimensional Resource Allocation in D2D-Based V2V Communication

Device-to-Device (D2D) communication is the major enabler of Vehicle-to-Everything communication in 3rd Generation Partnership Project (3GPP) Release 14. The user equipment/device can engage either in direct communication with the infrastructure, use a relay node, or it can communicate directly with another device with or without infrastructure support. The user equipment can be either a hand-held cellular device or a moving vehicle. The coexistence of cellular user equipment with the vehicular user equipment imposes different Quality of Service (QOS) requirements due to the rapid mobility of the vehicles and interference. Resource allocation is an important task by which the user equipment is allocated the required resources based on different QOS parameters. In this paper, we introduced the case of three types of users which share uplink resources: two types of vehicular users, and a third user that acts as a handheld cellular phone, which is nearly static. By keeping in mind, the differential QOS requirements for the three types of users, we have calculated the optimum power and then applied a 3-dimensional graph-based matching and hypergraph coloring based resource block (RB) allocation.

Joint Power Control and Resource Allocation Mode Selection for Safety-Related V2X Communication

Safety-related vehicle-to-everything (V2X) communication plays an essential role in road safety and requires stringent high reliability and low latency. In this paper, we propose a novel approach of joint power control and resource allocation mode selection to solve the resource allocation problem of the mixed centralized/distributed V2X communication system under different network load conditions. Two major resource allocation modes are considered for V2X communication: scheduled resource allocation (centralized system) and UE autonomous resource selection mode (distributed system). In order to meet the specific quality-of-service-related demands of safety-related V2X communication, we aim to maximize overall information value of V2X communication by considering both ProSe Per-Packet Priority and communication link quality of V2X messages while meeting the requirements of minimum SINR and maximum transmit power limitation for both pedestrian user equipments (PUEs) and vehicular user equipments (VUEs). In particular, scenarios where the network is lightly loaded we propose Vacant Resource Blocks and Power Allocation algorithm while for scenarios wherein the network is heavily loaded we propose Occupied Resource Blocks and Power Allocation algorithm. Furthermore, the proposed algorithm can achieve a stable matching between VUEs and their reuse PUEs partners. Extensive simulation results show that both the information value of VUEs as well as the spectral efficiency of PUEs can be improved efficiently by the proposed approach.

Reducing Message Collisions in Sensing-Based Semi-Persistent Scheduling (SPS) by Using Reselection Lookaheads in Cellular V2X.

In the C-V2X sidelink Mode 4 communication, the sensing-based semi-persistent scheduling (SPS) implements a message collision avoidance algorithm to cope with the undesirable effects of wireless channel congestion. Still, the current standard mechanism produces a high number of packet collisions, which may hinder the high-reliability communications required in future C-V2X applications such as autonomous driving. In this paper, we show that by drastically reducing the uncertainties in the choice of the resource to use for SPS, we can significantly reduce the message collisions in the C-V2X sidelink Mode 4. Specifically, we propose the use of the “lookahead”, which contains the next starting resource location in the time-frequency plane. By exchanging the lookahead information piggybacked on the periodic safety message, vehicular user equipment (UEs) can eliminate most message collisions arising from the ignorance of other UEs’ internal decisions. Although the proposed scheme would require the inclusion of the lookahead in the control part of the packet, the benefit may outweigh the bandwidth cost, considering the stringent reliability requirement in future C-V2X applications.

RA-eV2V: relaying systems for LTE-V2V communications

Emerging Long Term Evolution-based vehicle-to-vehicle (LTE-V2V) communication will support exchange of safety-related broadcast messages among vehicles. Such messages are generated periodically to announce one's own current state to other neighboring vehicles. The message reception performance in LTE-V2V drops sharply as communication distance increases if the communication channel is in a non-line-of-sight (NLOS) situation. To alleviate the problem, we propose a relaying system, called relayassisted enhanced V2V (RA-eV2V). In RA-eV2V, Road side unit (RSU) is in charge of relaying messages, received from vehicular user equipments (V-UEs). The operation of RSU is fully independent from other entities, meaning that the deployment of RSU on the road has no constraint at all. Moreover, there is no change from the current 3GPP standard operation on V-UE side either. In our realistic simulation, RA-eV2V hugely outperforms the 3GPP baseline scheme, e.g., by up to 36.5% in terms of message reception ratio in NLOS situations. Moreover, the feasibility of RA-eV2V is proven through real-map based simulation, which realistically reflects the actual situations on the roads.

A Latency and Reliability Guaranteed Resource Allocation Scheme for LTE V2V Communication Systems

By leveraging direct device-to-device interaction, LTE vehicle-to-vehicle (V2V) communication becomes a promising solution to meet the stringent requirements of vehicular communication. In this paper, we propose jointly optimizing the radio resource, power allocation, and modulation/coding schemes of the V2V communications, in order to guarantee the latency and reliability requirements of vehicular user equipments (VUEs) while maximizing the information rate of cellular user equipment (CUE). To ensure the solvability of this optimization problem, the packet latency constraint is first transformed into a data rate constraint based on random network analysis by adopting the Poisson distribution model for the packet arrival process of each VUE. Then, utilizing the Lagrange dual decomposition and binary search, a resource management algorithm is proposed to find the optimal solution of joint optimization problem with reasonable complexity. Simulation results show that the proposed radio resource management scheme can reduce the interference from V2V communication to CUEs and ensure the latency and reliability requirements of V2V communication.

Mobility Management for Vehicular User Equipment in LTE/Mobile Femtocell Networks

Vehicular User Equipment (UE) performance during mobility faces two issues relating to signaling and transmission, namely Handover (HO) and link adaptation. This paper shows that both processes are experiencing degradation during mobility and that vehicular UEs suffer from call drops and loss of connections. Therefore, this work presents an effective technique using Mobile-Femtos to improve vehicular UEs' HO process and link quality. Results show that vehicular UEs attached to a Mobile-Femto achieved better signalling and Link Ergodic capacity and as a consequence the outage probability was reduced. The achieved results indicated that deploying Mobile-Femtos under 25dB Vehicular Penetration Loss (VPL) has improved the vehicular UE Link Ergodic capacity by 1% and reduced the signal outage probability by 1.8% compared to the eNB direct transmission. Consequently, Drop Calls Probability (DCP) and Block Calls Probability (BCP) have been reduced by 7% and 14% respectively compared to the direct transmission from the eNB.