mmWave Vehicular Networks Research Articles

Learning-assisted User Scheduling and Beamforming for mmWave Vehicular Networks

Learning-assisted User Scheduling and Beamforming for mmWave Vehicular Networks

Modeling and Analysis of Edge Caching for 6G mmWave Vehicular Networks

As one of the most promising solutions, edge caching is emerging to reduce the content retrieval latency and relieve the huge burden on the backhaul links in the 6th generation (6G) millimeter wave (mmWave) intelligent vehicular networks. In this paper, we first formulate the optimization problem in terms of request hit probability and investigate two crucial factors, which impact the effectiveness of cache placement in device-to-device (D2D) enabled mmWave vehicular networks. Then, the general mathematical expressions for optimization of the request hit probability with the constraints of caching capacity are derived. Moreover, a group caching scheme (GCS) is proposed to obtain the sub-optimal results by utilizing relaxation method and Karush-Kuhn-Tucker (KKT) conditions. Inspired from the Matern hard-core processes and by exploiting the spatially correlated characteristics of users distribution, hard-cored based caching algorithm (HCCA) is proposed to avoid the simultaneous caching for a particular file. In addition, a joint caching and scheduling strategy is investigated which maximizes the number of concurrent transmissions by D2D enabled vehicle pairs matching and antennas adjustment. Comprehensive simulations validate our theoretical analysis and demonstrate that the proposed scheme can achieve higher performance in terms of request hit probability and the number of concurrent transmissions compared to the existing methods. Consequently, the proposed caching scheme has great potential in future intelligent vehicular applications.

Stochastic Modeling of Beam Management in mmWave Vehicular Networks

Mobility management is a major challenge for the wide-spread deployment of millimeter-wave (mmWave) cellular networks. In particular, directional beamforming in mmWave devices renders high-speed mobility support very complex. This complexity, however, is not limited to system design but also the performance estimation and evaluation. Hence, some have turned their attention to stochastic modeling of mmWave vehicular communication to derive closed-form expressions characterizing the coverage and rate behavior of the network. In this article, we model and analyze the beam management for mmWave vehicular networks. To the best of our knowledge, this is the first work that goes beyond coverage and rate analysis. Specifically, we focus on a multi-lane divided highway scenario in which base stations and vehicles are present on both sides of the highway. In addition to providing analytical expressions for the average number of beam switching and handover events, we provide design insights for the network operators to fine-tune their network within the flexibility provided by the standard in the choice of system parameters, including the number of resources dedicated to channel feedback and beam alignment operations.

Joint Optimization of Relay Selection and Transmission Scheduling for UAV-Aided mmWave Vehicular Networks

To deal with the explosive growth of mobile traffic, millimeter-wave (mmWave) communications with abundant bandwidth resources have been applied to vehicular networks. As mmWave signal is sensitive to blockage, we introduce the unmanned aerial vehicle (UAV)-aided two-way relaying system for vehicular connection enhancement and coverage expansion. How to improve transmission efficiency and to reduce latency time in such a dynamic scenario is a challenging problem. In this paper, we formulate the joint optimization problem of relay selection and transmission scheduling, aiming to reduce transmission time while meeting the throughput requirements. To solve this problem, two schemes are proposed. The first one is the random relay selection with concurrent scheduling (RCS), a low-complexity algorithm implemented in two steps. The second one is the joint relay selection with dynamic scheduling (JRDS), which fully avoids relay contentions and exploits potential concurrent ability, to obtain further performance enhancement over RCS. Through extensive simulations under different environments with various flow numbers and vehicle speeds, we demonstrate that both RCS and JRDS schemes outperform the existing schemes significantly in terms of transmission time and network throughput. We also analyze the impact of threshold selection on achievable performance.

Deep Reinforcement Learning Based Joint Beam Allocation and Relay Selection in mmWave Vehicular Networks

Millimeter-wave (mmWave) can provide abundant spectrum resource in vehicular communication networks. Nevertheless, due to the high path-loss and blocking effects in mmWave propagation, and high mobility of vehicles, downlink services for vehicles would be seriously degraded. In this paper, we firstly propose a deep reinforcement learning-based joint beam allocation and relay selection (JoBARS) scheme to mitigate blocking effects and optimize the total transmission rate of the vehicular network, where the mmWave base station (mmBS) provides multi-user services. When downlinks are blocked, the mmBS can select appropriate idle vehicles as relay nodes to enhance service quality from a global perspective. We set the rate punishment restriction in JoBARS scheme to guarantee each vehicle can obtain high-quality service. Besides, a relaying incentive mechanism (RIM) is proposed to avoid vehicles being overly selected for relaying and ensure that relay vehicles have a higher chance of being served in the next round. We demonstrate that JoBARS scheme can effectively enhance the total transmission rate while alleviating transmission outages caused by severe propagation attenuation of mmWave signals. Compared with Greedy Selection scheme, the total rate and average connection probability of vehicles under JoBARS scheme are nearly 17% and 14% higher when blocking effects are severe.

A Directional TDMA Protocol for High Throughput URLLC in mmWave Vehicular Networks

Future vehicular wireless applications, such as autonomous and remote driving, will demand High Throughput Ultra-Reliable Low Latency Communication (HT-URLLC) unsupported by current wireless standards. Hybrid network designs that combine Millimeter-Wave (mmWave) and Sub-6GHz communications were recently proposed to establish HTURLLC in Vehicle-To-Everything (V2X) networks. To enable such integration, new Medium Access Control (MAC) protocols must be developed. As such, we consider a linear vehicular network, in which vehicles driving closely in the same lane utilize both front and rear directional mmWave antennas to form a Lane Cluster (LC). Data packets, then, propagate forwards and backwards along the LC towards its terminals in a multihop fashion. In turn, the LC terminals utilize omnidirectional antennas at Sub-6GHz frequencies to disseminate the cluster's packets to nearby LCs, Road Side Units (RSUs), and cellular base stations. To resolve the channel contention between subsequent LC members, we devised a two-timeslot MAC scheme with a schedule alignment mechanism. Additionally, we developed centralized and distributed schedule coordination protocols aligning the schedules of adjacent LCs to mitigate cross-lane interference due to antenna beam misalignment. The proposed protocols were evaluated using VEINS (extension of OMNET++ network simulator) and compared against the CSMA-CA scheme of IEEE 802.11p under different parameters and scenarios. The results show that the proposed linear topology along with the scheduling protocols can potentially achieve HT-URLLC while addressing the mmWave practicality issues.

Cooperative Gigabit Content Distribution with Network Coding for mmWave Vehicular Networks

Millimeter-wave (mmWave) directional communication has been deemed as a promising means to achieve the goal of multi-gigabit data rate with low latency in vehicular ad-hoc networks (VANETs). However, due to the frequent transmission errors caused by the lossy nature of mmWave wireless links and high dynamic of VANETs, the channel resources are not fully exploited. In order to enhance the resilience to transmission errors, we introduce the symbol level network coding (SLNC) to mmWave wireless communications. We propose a greedy network coding strategy based on graph-theoretic approach, named GTNC, so as to take full advantage of SLNC. Moreover, considering the directionality of mmWave and the effect of GTNC on degrading transmission redundancy, it has great potential to realize concurrent transmission in mmWave vehicular networks. Therefore, we propose a cooperative concurrent distribution scheme with GTNC, named CCDS-GTNC, to coordinate transmissions between vehicles, which carries out collaborative vehicle-to-vehicle (V2V) and vehicle-to-Infrastructure (V2I) communications and achieves concurrent transmissions of multiple transmitters simultaneously. The simulation results show that our proposed scheme can effectively improve the efficiency of content dissemination in view of reducing the transmission delay and transmission redundancy.

PMBM-Based SLAM Filters in 5G mmWave Vehicular Networks

Radio-based vehicular simultaneous localization and mapping (SLAM) aims to localize vehicles while mapping the landmarks in the environment. We propose a sequence of three Poisson multi-Bernoulli mixture (PMBM) based SLAM filters, which handle the entire SLAM problem in a theoretically optimal manner. The complexity of the three proposed SLAM filters is progressively reduced while sustaining high accuracy by deriving SLAM density approximation with the marginalization of nuisance parameters (either vehicle state or data association). Firstly, the PMBM SLAM filter serves as the foundation, for which we provide the first complete description based on a Rao-Blackwellized particle filter. Secondly, the Poisson multi-Bernoulli (PMB) SLAM filter is based on the standard reduction from PMBM to PMB, but involves a novel interpretation based on auxiliary variables and a relation to Bethe free energy. Finally, using the same auxiliary variable argument, we derive a marginalized PMB SLAM filter, which avoids particles and is instead implemented with a low-complexity cubature Kalman filter. We evaluate the three proposed SLAM filters in comparison with the probability hypothesis density (PHD) SLAM filter in 5G mmWave vehicular networks and show the computation-performance trade-off between them.

Content Distribution Based on Joint V2I and V2V Scheduling in mmWave Vehicular Networks

With the explosive growth of vehicle applications, vehicular networks based on millimeter wave (mmWave) bands have attracted interests from both academia and industry. mmWave communications are able to utilize the huge available bandwidth to provide multiple Gbps transmission rates among vehicles. In this paper, we address the content distribution scheduling problem in mmWave vehicular networks. It has been challenging for all vehicles in the same network to complete content downloading due to the limited communication resources of roadside units (RSUs) and the high mobility of vehicles. We propose a joint vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) scheduling scheme to minimize the total number of content distribution time slots from a global optimization perspective. In the V2I phase, the RSU serially transmits integrity content to vehicles, which are selected according to the vehicular network topology and transmission scheduling scheme. In the V2V phase, full-duplex communications and concurrent transmissions are exploited to achieve content sharing between vehicles and improve transmission efficiency. Performance evaluations demonstrate that our proposed scheme reduces the number of time slots and significantly improves system throughput when compared with other schemes, especially under large-size file transfers and a large number of vehicles.

Optimal Beam Association for High Mobility mmWave Vehicular Networks: Lightweight Parallel Reinforcement Learning Approach

In intelligent transportation systems (ITS), vehicles are expected to feature with advanced applications and services which demand ultra-high data rates and low-latency communications. For that, the millimeter wave (mmWave) communication has been emerging as a very promising solution. However, incorporating the mmWave into ITS is particularly challenging due to the high mobility of vehicles and the inherent sensitivity of mmWave beams to dynamic blockages. This article addresses these problems by developing an optimal beam association framework for mmWave vehicular networks under high mobility. Specifically, we use the semi-Markov decision process to capture the dynamics and uncertainty of the environment. The Q-learning algorithm is then often used to find the optimal policy. However, Q-learning is notorious for its slow-convergence. Instead of adopting deep reinforcement learning structures (like most works in the literature), we leverage the fact that there are usually multiple vehicles on the road to speed up the learning process. To that end, we develop a lightweight yet very effective parallel Q-learning algorithm to quickly obtain the optimal policy by simultaneously learning from various vehicles. Extensive simulations demonstrate that our proposed solution can increase the data rate by 47% and reduce the disconnection probability by 29% compared to other solutions.

Coalition Game Based Full-Duplex Popular Content Distribution in mmWave Vehicular Networks

The millimeter wave (mmWave) communication has drawn intensive attention with abundant band resources. In this paper, we consider the popular content distribution (PCD) problem in the mmWave vehicular network. In order to offload the communication burden of base stations (BSs), vehicle-to-vehicle (V2V) communication is introduced into the PCD problem to transmit contents between on-board units (OBUs) and improve the transmission efficiency. We propose a full-duplex (FD) cooperative scheme based on coalition formation game, and the utility function is provided based on the maximization of the number of received contents. The contribution of each member in the coalition can be transferable to its individual profit. While maximizing the number of received contents in the fixed time, the cooperative scheme also ensures the individual profit of each OBU in the coalition. We evaluate the proposed scheme by extensive simulations in mmWave vehicular networks. Compared with other existing schemes, the proposed scheme has superior performances on the number of possessed contents and system fairness. Besides, the low complexity of the proposed algorithm is demonstrated by the switch operation number and CPU time.

Smart vehicular communication via 5G mmWaves

Millimeter-Wave (mmWave) communication is conceived as a viable approach for 5G vehicular communication systems, where vehicles are equipped with more sensors that generate Gbps data for future autonomous driving. However, such directional mmWave communication relies on accurate beam alignment and is sensitive to blockage. Dense deployment of mmWave base stations (mmBSs) and high mobility of vehicles also lead to frequent handovers and complex beam alignment calculation. 5G mmWave vehicular communication calls for a smart and stable solution. To this end, we propose an online learning scheme to address the problem of beam selection with blockage-free guarantee in 5G mmWave vehicular networks. We first model this problem as a contextual combinatorial multi-armed bandit (MAB) problem with QoS constraints and delayed feedback. Next, we propose an online learning algorithm, BPG, to predict beam directions, with provable sub-linear regret and blockage-free bounds. BPG exploits the context space and learns the expected weight of each beam from arrived vehicles’ contexts and the delayed feedback. To validate the efficiency of BPG, we also conduct trace-driven simulations based on real-world traffic patterns. Simulation results show that BPG achieves close-to-optimal throughput with low violation and outperforms other benchmark algorithms.

A Deep Reinforcement Learning Based D2D Relay Selection and Power Level Allocation in mmWave Vehicular Networks

5G millimeter wave (mmWave) communication is an efficient technique for low delay and high data rate transmission in vehicular networks. Due to the high path loss in 5G mmWave band, 5G base stations need to be densely deployed, which may result in great deployment expenditures. In this letter, we jointly consider a relay selection problem in multihop 5G mmWave device to device (D2D) transmissions and a power level allocation problem of mmWave D2D links. We propose a centralized hierarchical deep reinforcement learning based method to find an optimal solution for the problem. The proposed method does not rely on the information of links, and it tries to find an optimal solution based on the information of vehicles. Simulation results show that the convergence of the proposed method, and the transmission delay performance of proposed method is better than a link-quality-prediction based method, and close to a link-quality-known method.

Concurrent Transmission Scheduling for Perceptual Data Sharing in mmWave Vehicular Networks

Sharing perceptual data with other vehicles enhances the traffic safety of autonomous vehicles because it helps vehicles locate other vehicles and pedestrians in their blind spots. Such safety applications require high throughput and short delay, which cannot be achieved by conventional microwave vehicular communication systems. Therefore, millimeter-wave (mmWave) communications are considered to be a key technology for sharing perceptual data because of their wide bandwidth. One of the challenges of data sharing in mmWave communications is broadcasting because narrow-beam directional antennas are used to obtain high gain. Because many vehicles should share their perceptual data to others within a short time frame in order to enlarge the areas that can be perceived based on shared perceptual data, an efficient scheduling for concurrent transmission that improves spatial reuse is required for perceptual data sharing. This paper proposes a data sharing algorithm that employs a graph-based concurrent transmission scheduling. The proposed algorithm realizes concurrent transmission to improve spatial reuse by designing a rule that is utilized to determine if the two pairs of transmitters and receivers interfere with each other by considering the radio propagation characteristics of narrow-beam antennas. A prioritization method that considers the geographical information in perceptual data is also designed to enlarge perceivable areas in situations where data sharing time is limited and not all data can be shared. Simulation results demonstrate that the proposed algorithm doubles the area of the cooperatively perceivable region compared with a conventional algorithm that does not consider mmWave communications because the proposed algorithm achieves high-throughput transmission by improving spatial reuse. The prioritization also enlarges the perceivable region by a maximum of 20%.