High Mobility Vehicular Networks Research Articles

Spatial-Index Modulation Based Orthogonal Time Frequency Space System in Vehicular Networks

In this paper, a spatial-index modulation (SIM) based orthogonal time frequency space (OTFS) system, named SIM-OTFS, is proposed to enhance the effectiveness and the reliability of high mobility vehicular networks in intelligent transportation systems. The SIM-OTFS system adopts a three dimensional index modulation (IM) technique which utilizes the transmit antenna, delay, and Doppler indexes in the space and delay-Doppler domains, respectively, to achieve a higher transmission rate. Considering the characteristics of vehicular networks, we first present the SIM-OTFS system design and the corresponding signal processing. Then, we derive the average bit error rate (ABER) upper bound of the SIM-OTFS system by the union bound theory. Moreover, the diversity, the coding gain, and the complexity of the SIM-OTFS system are further investigated. Numerical results verify the theoretical analysis of the ABER and the diversity of the SIM-OTFS system, which shows the superiority of the SIM-OTFS system in the ABER performance over the multiple-input and multiple-output (MIMO) based OTFS (MIMO-OTFS) system. Meanwhile, the SIM-OTFS system realizes better performance than the spatial modulation (SM) and IM based orthogonal frequency division multiplexing (SM-OFDM-IM) system in high mobility vehicular communication with reasonable complexity sacrifice. Furthermore, the influence of the resolvable multipaths of the channel in vehicular networks on the ABER performance of the SIM-OTFS system is also illustrated.

Integrated Sensing and Communication-Assisted Orthogonal Time Frequency Space Transmission for Vehicular Networks

Orthogonal time frequency space (OTFS) modulation is a promising candidate for supporting reliable information transmission in high-mobility vehicular networks. In this paper, we consider the employment of the integrated (radar) sensing and communication (ISAC) technique for assisting OTFS transmission in both uplink and downlink vehicular communication systems. Benefiting from the OTFS-ISAC signals, the roadside unit (RSU) is capable of simultaneously transmitting downlink information to the vehicles and estimating the sensing parameters of vehicles, e.g., locations and speeds, based on the reflected echoes. Then, relying on the estimated kinematic parameters of vehicles, the RSU can construct the topology of the vehicular network that enables the prediction of the vehicle states in the following time instant. Consequently, the RSU can effectively formulate the transmit downlink beamformers according to the predicted parameters to counteract the channel adversity such that the vehicles can directly detect the information without the need of performing channel estimation. As for the uplink transmission, the RSU can infer the delays and Dopplers associated with different channel paths based on the aforementioned dynamic topology of the vehicular network. Thus, inserting guard space as in conventional methods are not needed for uplink channel estimation which removes the required training overhead. Finally, an efficient uplink detector is proposed by taking into account the channel estimation uncertainty. Through numerical simulations, we demonstrate the benefits of the proposed ISAC-assisted OTFS transmission scheme.

Integrated Sensing and Communication-Enabled Predictive Beamforming With Deep Learning in Vehicular Networks

Accurate beam tracking in high-mobility vehicular networks has become a key issue for reliable communication link establishment. Recent work on integrated sensing and communication has shown the superiority of the sensing-assisted beam tracking. By estimating the angular parameters of vehicles based on the radar echoes, the roadside unit (RSU) can perform a so-called predictive beamforming to pair the beams with high accuracy as well as low latency. To further reduce the estimation error due to the nonlinear measurements, in this letter, we develop a deep learning-based approach to exploit the dependency between the angles and the measurements, and to obtain the angle estimates. Simulation results show that our proposed approach can improve the estimation performance and maintain reliable communications in high-mobility vehicular networks.

Resource Allocation in D2D Enabled Vehicular Communications: A Robust Stackelberg Game Approach Based on Price-Penalty Mechanism

This paper studies how to apply game theory to realize a well-function device-to-device enabled vehicular (D2D-V) communication system, where the uplink channel allocated to the cellular user (CU) is reused by multiple D2D-V users. Considering a non-cooperative setting where the CU and D2D-V users are selfish and profit-driven, a novel Stackelberg game framework is proposed to model the single-leader-multiple-follower hierarchical competition, where the CU and D2D-V users act as the leader and the followers, respectively. For the current D2D-V networks, the interference in the dense vehicle scene often leads to extremely poor communication quality. Moreover, a vehicle's mobility leads to an uncertain channel state and further affects the stability of communication. To achieve effective communication, robust Stackelberg game-based resource allocation is developed, and a price-penalty mechanism is further proposed. Unlike previous Stackelberg games, the robust game is highlighted by handling the channel uncertainty which is embedded in the interference probability constraints. Besides, a game equilibrium (GE) is considered to be the solution and its existence and uniqueness are investigated. Also, a distributed robust power control and nonuniform price bargaining algorithm is proposed to approach the GE. Numerical simulations are performed to evaluate the algorithm performances, and the results indicate that the proposed algorithm is effective in high mobility vehicular networks under uncertain channel environments.

Deep-Learning-Based Wireless Resource Allocation With Application to Vehicular Networks

It has been a long-held belief that judicious resource allocation is critical to mitigating interference, improving network efficiency, and ultimately optimizing wireless communication performance. The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve the problem to a certain level of optimality. Nonetheless, as wireless networks become increasingly diverse and complex, e.g., in the high-mobility vehicular networks, the current design methodologies face significant challenges and thus call for rethinking of the traditional design philosophy. Meanwhile, deep learning, with many success stories in various disciplines, represents a promising alternative due to its remarkable power to leverage data for problem solving. In this paper, we discuss the key motivations and roadblocks of using deep learning for wireless resource allocation with application to vehicular networks. We review major recent studies that mobilize the deep learning philosophy in wireless resource allocation and achieve impressive results. We first discuss deep learning assisted optimization for resource allocation. We then highlight the deep reinforcement learning approach to address resource allocation problems that are difficult to handle in the traditional optimization framework. We also identify some research directions that deserve further investigation.

Toward Intelligent Vehicular Networks: A Machine Learning Framework

As wireless networks evolve toward high mobility and providing better support for connected vehicles, a number of new challenges arise due to the resulting high dynamics in vehicular environments and thus motive rethinking of traditional wireless design methodologies. Future intelligent vehicles, which are at the heart of high mobility networks, are increasingly equipped with multiple advanced onboard sensors and keep generating large volumes of data. Machine learning, as an effective approach to artificial intelligence, can provide a rich set of tools to exploit such data for the benefit of the networks. In this paper, we first identify the distinctive characteristics of high mobility vehicular networks and motivate the use of machine learning to address the resulting challenges. After a brief introduction of the major concepts of machine learning, we discuss its applications to learn the dynamics of vehicular networks and make informed decisions to optimize network performance. In particular, we discuss in greater detail the application of reinforcement learning in managing network resources as an alternative to the prevalent optimization approach. Finally, some open issues worth further investigation are highlighted.

Routing Protocols in Vehicular Ad hoc Networks: Survey and Research Challenges

A Vehicular Ad hoc Network (VANET) is a type of wireless ad hoc network that facilitates ubiquitous connectivity between vehicles in the absence of fixed infrastructure. Mul ti-hop routing and beaconing approaches are two important research challenges in high mobility vehicular networks. Routing protocols are divided into two categories of topology-based and position-based routing protocols. In this article, we perform a comparative study among the existing routing solutions, which explores the main advantages and drawbacks behind their design. After implementing the representatives of geographical and topology routing protocols, we analyze the simulation results and discuss the strengths and weaknesses of these routing protocols with regard to their suitability to vehicular networks. Lastly, we discuss the open issues and research directions related to VANET routing protocols.

In VANET single Network types i.e. WAVE is not sufficient to provide QoS. UMTS in integration with WAVE can provide efficient network connectivity – A Survey

This survey provides an insight to the Qualtity of Service (QoS) provision in Vehicular Adhoc Networks (VANET) through Wireless Access in Vehicular Environment (WAVE) and Universal Mobile Telecommunication System (UMTS). In order to provide ubiquitous and pervasive connectivity for the high mobility vehicular networks in dynamically changing context and service adapting situations, careful techniques must be adopted to provide QoS in VANET applications. To address different implementation scenarios and overcome the shortcomings of WAVE and UMTS, I some alternatives were captured to improve QoS in VANET either by amalgamating WAVE/UMTS or through some other viable protocols

Beaconing Approaches in Vehicular Ad Hoc Networks: A Survey

A vehicular ad hoc network (VANET) is a type of wireless ad hoc network that facilitates ubiquitous connectivity between vehicles in the absence of fixed infrastructure. Beaconing approaches is an important research challenge in high mobility vehicular networks with enabling safety applications. In this article, we perform a survey and a comparative study of state-of-the-art adaptive beaconing approaches in VANET, that explores the main advantages and drawbacks behind their design. The survey part of the paper presents a review of existing adaptive beaconing approaches such as adaptive beacon transmission power, beacon rate adaptation, contention window size adjustment and Hybrid adaptation beaconing techniques. The comparative study of the paper compares the representatives of adaptive beaconing approaches in terms of their objective of study, summary of their study, the utilized simulator and the type of vehicular scenario. After implementing the representatives of beaconing approaches, we analysed the simulation results and discussed the strengths and weaknesses of these beaconing approaches with regard to their suitability to vehicular networks. Finally, we discussed the open issues and research directions related to VANET adaptive beaconing approaches.