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This paper studies the secure transmission in a multiuser broadcast channel where only the statistical channel state information of the eavesdropper is available. We propose to apply secret dirty-paper coding (S-DPC) in this scenario to support the secure transmission of one user and the normal (unclassified) transmission of the other users. By adopting the S-DPC and encoding the secret message in the first place, all the information-bearing signals of the normal transmission are treated as noise by potential eavesdroppers and thus provide secrecy for the secure transmission. In this way, the proposed approach exploits the intrinsic secrecy of multiuser broadcasting and can serve as an energy-efficient alternative to the traditional artificial noise (AN) scheme. To evaluate the secrecy performance of this approach and compare it with the AN scheme, we propose two S-DPC-based secure transmission schemes for maximizing the secrecy rate under constraints on the secrecy outage probability (SOP) and the normal transmission rates. The first scheme directly optimizes the covariance matrices of the transmit signals, and a novel approximation of the intractable SOP constraint is derived to facilitate the optimization. The second scheme combines zero-forcing dirty-paper coding and AN, and the optimization involves only power allocation. We establish efficient numerical algorithms to solve the optimization problems for both schemes. Theoretical and simulation results confirm that, in addition to supporting the normal transmission, the achievable secrecy rates of the proposed schemes can be close to that of the traditional AN scheme, which supports only the secure transmission of one user. [ABSTRACT FROM PUBLISHER]

With the introduction of automated driving, new requirements for the design and control of a vehicle powernet and powertrain arise, since the increasing level of driving automation shifts the responsibility for passengers’ safety from a driver towards automation system. Therefore, the functionality of the subsystems required for the automated transition to a standstill in case of a failure must be guaranteed. The work presented in this paper proposes a novel and generic system architecture for the energy management of a fail-operational powernet and powertrain in fault-free and failure case operation. The underlying control strategy is based on a concept enabling safety based range extension with the main goal to complete a driving mission at the safest possible location for the passengers. The proposed Energy Management System (EMS) approximates a driving trajectory to the destination based on route preview, which is then used for the dynamic optimization of the torque split in a powertrain with multiple motors. In this way, the total energy required for propulsion, which is also used as an input for the energy distribution, can be accurately predicted. The optimal distribution of the energy for the supply of safety-critical subsystems and powernet auxiliaries is then estimated by solving a mixed-integer optimization problem. If the desired driving mission cannot be completed, a three-level-degradation concept adapting the driving mission is applied. The individual modules of the EMS are presented in this paper and a system architecture enabling model predictive and adaptive energy distribution in a vehicle powertrain and powernet with automated fault reactions is proposed and exemplified with simulation results. [ABSTRACT FROM AUTHOR]

The papers in this special section were presented at the IEEE Vehicle Power and Propulsion Conference (VPPC) that was held in Coimbra, Portugal, in October 2014. Advanced traction and propulsion systems are developing in order to ensure better energy performance, reduced operating cost, and higher lifetime of future transportation systems like road vehicles, as well as more electric trains, subways, ships, and airplanes. Such a powertrain integrates several complex subsystems (including power or energy sources, electricmachines, power electronics,mechanical transmission), and it becomesmandatory to consider the whole system in order to reach the best performance. [ABSTRACT FROM AUTHOR]

This paper analyzes priority-based distributed access control in vehicular networks. Characterizing the spatial correlation between vehicles, users, and roads, this paper uses Cox point processes to model vehicles and users, respectively. Then, this paper proposes that each vehicle transmits if it has the highest priority among all of its neighbors. Using the stochastic geometry framework, this paper derives the probability that a typical vehicle is retained to transmit. Then, this paper derives a second-order moment measure of the retained point process to evaluate the signal-to-interference (SIR) distribution of a typical user. The derived SIR distribution is compared to the system-level simulation of the SIR distribution of a typical user. [ABSTRACT FROM AUTHOR]

The integration of antennas into disposable paper drones using inkjet printing technology is presented. These drones or unmanned aerial vehicles (UAVs) are developed using origami folding structures. Two vertical monopole antennas based on the same design concept are proposed and their performance assessed for two different conditions. The conditions relate to the placement of the other electronic components and circuits on the origami drones as reported in the literature. The first is when the electromechanical components and corresponding metallic layers are located in the wings. In this case, the effect of the possible location of the antenna as well as the deformation of the wings on S11 is discussed. The second is a more general case scenario which includes when the components and motors are placed at the tail and lower part of the body of the drone. The antenna elements are directly printed onto a photo paper substrate using silver nanoparticle conductive ink. Subsequently, the substrate is folded to create a paper drone. Low-cost desktop inkjet printing equipment is used to deposit the metallic tracks of the antenna. The designs target the current frequency bands employed in the control and wireless communication of commercial drones (2.4 GHz and 5 GHz bands). The purpose of this work is to investigate potential antenna scenarios for disposable drones which may one day be fully fabricated using inkjet printing technology. All antenna designs and studies have been simulated using CST Microwave Studio and compared well with experimental results. [ABSTRACT FROM AUTHOR]

This paper considers the downlink of a cognitive radio (CR) network formed by multiple primary and secondary transmitters, where each multiantenna transmitter serves a preknown set of single-antenna users. This paper assumes that the secondary and primary transmitters can simultaneously transmit their data over the same frequency bands to achieve high system spectrum efficiency. This paper considers the downlink balancing problem of maximizing the minimum signal-to-interference-plus-noise ratio (SINR) of the secondary transmitters subject to both the total power constraint of the secondary transmitters and the maximum interference constraint at each primary user due to secondary transmissions. This paper proposes solving the problem using the alternating direction method of multipliers, which leads to a distributed implementation through limited information exchange across the coupled secondary transmitters. This paper additionally proposes a solution that guarantees feasibility at each iteration. Simulation results demonstrate that the proposed solution converges to the centralized solution in a reasonable number of iterations. [ABSTRACT FROM AUTHOR]

The papers in this special section focus on 5G mobile communication enabled vehicular netwowrks. This section is designed to provide the academic and industrial communities an excellent venue to present the vision, research, and new solutions on the key technologies emerging for 5G enabled vehicular networks. [ABSTRACT FROM PUBLISHER]

To exploit richer spectrum resources for even better service quality, millimeter wave (mmWave) communication has been considered for high-speed railway (HSR) communication systems. In this paper, we focus on scheduling as many flows as possible while satisfying their QoS requirements. Due to interference, eavesdropping, or other problems, some flows may not be directly transmitted from the track-side BS. In this paper, we propose an UAV-assisted scheduling scheme which utilizes a UAV to serve as relay for such flows. The proposed scheme also utilize two mmWave bands, one for the BS links and the other for the UAV links. The proposed algorithm aims to maximize the number of flows with their QoS requirements satisfied. Simulations demonstrate that the proposed scheme achieves a superior performance on the number of completed flows and the system throughput over two baseline schemes. [ABSTRACT FROM AUTHOR]

The aspects of sum rate and fairness for dirty paper coding (DPC) based MIMO downlink communications are investigated in this paper. We first apply the $\ell _{1}$ -norm fairness measure to formulate the problem of fairness maximization for a given sum rate as an optimization problem. The problem is unfortunately nonconvex and cannot be efficiently solved. To overcome the difficulty, we invoke the uplink-downlink duality to transform the problem back and forth between uplink and downlink communications. An efficient, iterative waterfilling based algorithm is then proposed to yield achievable rates with the best possible fairness values. Simulation results show that the proposed approach offers an enormous gain in the achievable sum rates for a wide range of fairness values, when compared to the popular successive zero-forcing DPC-based and block diagonalization based coding schemes. [ABSTRACT FROM AUTHOR]

The explosive growth of Internet-of-Things (IoT) applications such as smart cities and Industry 4.0 have led to drastic increase in demand for wireless bandwidth, hence motivating the rapid development of new techniques for enhancing spectrum utilization needed by new generation wireless communication technologies. Among others, dynamic spectrum access (DSA) is one of the most widely accepted approaches. In this paper, as an enhancement of existing works, we take into consideration of inter-node collaborations in a dynamic spectrum environment. Typically, in such distributed circumstances, intelligent dynamic spectrum access almost invariably relies on self-learning to achieve dynamic spectrum access improvement. Whereas, this paper proposes a DSA scheme based on deep reinforcement learning to enhance spectrum and access efficiency. Unlike traditional Q-learning-based DSA, we introduce the following to enhance the spectrum efficiency in dynamic IoT spectrum environments. First, deep double Q-learning is adopted to perform local self-spectrum-learning for IoT terminals in order to achieve better dynamic access accuracy. Second, to accelerate learning convergence, federated learning (FL) in edge nodes is used to improve the self-learning. Third, multiple secondary users, who do not interfere with each other and have similar operation condition, are clustered for federated learning to enhance the efficiency of deep reinforcement learning. Comparing with the traditional distributed DSA with deep learning, the proposed scheme has faster access convergence speed due to the characteristic of global optimization for federated learning. Based on this, a framework of federated deep reinforcement learning (FDRL) for DSA is proposed. Furthermore, this scheme preserves privacy of IoT users in that FDRL only requires model parameters to be uploaded to edge servers. Simulations are performed to show the effectiveness of theproposed FDRL-based DSA framework. [ABSTRACT FROM AUTHOR]

In this paper, a traction motor for maximum power 76kW electrical vehicle (EV) was designed, and the design results were verified through experiments. One of the important design objectives of the traction motor for EV is to improve energy efficiency. The energy efficiency of a traction motor affects the mileage of electric vehicles (EVs). Among the design factors influencing energy efficiency, shape parameters include torque per rotor volume (TRV), torque density (TD) and shape ratio (SR). These shape parameters have an impact on the motor's phase back EMF, inductance, winding resistance and core loss resistance, eventually affecting the energy efficiency in the entire operating range of the motor. In this paper, a New European Driving Cycle (NEDC) was applied to calculate the energy efficiency of the traction motor for EV. Changes in motor parameters such as phase back electromotive force (EMF), inductance, winding resistance and core loss resistance were identified according to variations in shape parameters. In addition, the motor characteristics according to the changes of these parameters were confirmed, and shape parameters to improve energy efficiency were selected. Since the traction motor designed in this paper adopted a water cooling method, thermal analysis was performed by reflecting the water cooling method in the thermal equivalent circuit (TEC). In order to confirm the reliability of the analysis method and design, a load test was conducted on the entire operating range to calculate energy efficiency, and a temperature test was performed to compare the test results with the analysis results. [ABSTRACT FROM AUTHOR]

This paper provides a new design framework for 3D flight path tracking control of unmanned aerial vehicles (UAVs) under wind environments. The new design framework simultaneously achieves the following three points: (i) 3D path tracking error system representation using the Serret-Frenet frame under wind environments, (ii) guaranteed cost control, (iii) simultaneous stabilization by a single controller for different 3D paths with a common interval parameter setting in the Serret-Frenet frame. To realize the three points, a path tracking error system based on the 3D kinematic model of UAVs under wind environments is constructed in the Serret-Frenet frame. The path tracking error system is exactly represented with the Takagi-Sugeno (T-S) fuzzy model in considered operation domains. As an advantage of the T-S fuzzy model construction, this paper considers a guaranteed cost controller design that minimizes the upper bound of a given performance function. The guaranteed cost controller design problem is cast in terms of linear matrix inequalities (LMIs). Thus, the designed controller guarantees not only the path stabilization but also the guaranteed cost control even in path tracking control for a given desired 3D flight path under wind environments. In addition, a simultaneous stabilization problem is also considered in terms of finding a common solution in a set of the LMIs. The simulation results show the utility of the proposed 3D flight path tracking control under wind environments. [ABSTRACT FROM AUTHOR]

Absolute time synchronization is a basic issue in wireless communication. With the advent of 5G, many emerging vertical industry applications have put forward demands for timing accuracy over air interface. High Precision Timing method over Air Interface based on PHY Layer Signals (TAP) is proposed for mobile networks. It is designed to provide microsecond level timing accuracy at a relatively low cost compared to the existing timing methods, such as GNSS, NTP, PTP, etc. The purpose of this paper is to comprehensively improve TAP, including timing accuracy, stability and compatibility, so as to promote its practical application. This paper proposes improvements to TAP from four aspects: timing algorithm, timing pilot signal, terminal timing process and timing parameters determination. We improve the TAP’s deficiencies in delay estimation, delay compensation accuracy and real-time matching of timing information. We implement the workflow of TAPv2 for UE based on FPGA. Finally, we settle the problem of TAP parameters determination by applying a DQN method. The simulations and tests not only show that the DQN model can effectively determine the TAP parameters, but also show TAPv2 can achieve higher accuracy and stability than TAP in multiple scenarios based on the interaction with baseband. [ABSTRACT FROM AUTHOR]

Due to the non-uniform geographic distribution and time-varying characteristics of the ground traffic request, how to make full use of the limited beam resources to serve users flexibly and efficiently is a brand-new challenge for beam hopping satellite systems. The conventional greedy-based beam hopping methods do not consider the long-term reward, which is difficult to deal with the time-varying traffic demand. Meanwhile, the heuristic algorithms such as genetic algorithm have a slow convergence time, which can not achieve real-time scheduling. Furthermore, existing methods based on deep reinforcement learning (DRL) only make decisions on beam patterns, lack of the freedom of bandwidth. This paper proposes a dynamic beam pattern and bandwidth allocation scheme based on DRL, which flexibly uses three degrees of freedom of time, space and frequency. Considering that the joint allocation of bandwidth and beam pattern will lead to an explosion of action space, a cooperative multi-agents deep reinforcement learning (MADRL) framework is presented in this paper, where each agent is only responsible for the illumination allocation or bandwidth allocation of one beam. The agents can learn to collaborate by sharing the same reward to achieve the common goal, which refers to maximize the throughput and minimize the delay fairness between cells. Simulation results demonstrate that the offline trained MADRL model can achieve real-time beam pattern and bandwidth allocation to match the non-uniform and time-varying traffic request. Furthermore, when the traffic demand increases, our model has a good generalization ability. [ABSTRACT FROM AUTHOR]

This paper aims to resolve a clear and unrealistic limitation found in recent works that have proposed solutions to the massive demand for the autonomous electric mobility-on-demand (AEMoD) services. Namely, this paper will focus on the matched charge-to-trip only service by enabling sub-class service; i.e., allowing vehicles to serve customer classes with trips needing less charge. The paper proposes a multi-class management system with a new queuing model for one city zone, and then derives the stability conditions of the system. Proportions of vehicles from each class that will immediately serve customers with sub-class service or full/partial charge are modeled, and the maximum expected response time of the system is then minimized by optimizing these vehicle service decisions. The advantages of the new model are then compared with previously proposed schemes and non-optimized models through simulations. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive performance comparison between a current robust military waveform; namely, MIL-STD-188-110D, Waveform 0, and a filter bank multicarrier spread-spectrum (FBMC-SS) waveform proposed for communications through ionospheric/skywave HF channels. Waveform 0 is effectively a direct sequence spread spectrum waveform that uses Walsh multi-codes to enhance the information transmission rate. It may thus be referred to as Walsh-DSSS. FBMC-SS, on the other hand, makes use of filter banks to provide excellent performance when the received signal is subject to partial band interference. Successful application of FBMC-SS for communications across skywave HF channels has been previously demonstrated, both theoretically and through experimental work. However, very little has been done to contrast FBMC-SS against Walsh-DSSS. The goal of this paper is to first add new features to FBMC-SS to bring it on par with Walsh-DSSS. These features include: (i), introduction of multi-codes that achieve a comparable (or better) data rate to Walsh-DSSS; and (ii), addition of a scrambling step applied to the multi-codes to make the receiver detection robust against widely spread multipaths. With this established, in the second part of the paper, we examine the performance of the developed FBMC-SS against Walsh-DSSS when both are applied for communications across skywave HF channels. The two waveforms are compared both through a theoretical study and through experimental works across several skywave channels ranging from hundreds to thousands of kilometers. [ABSTRACT FROM AUTHOR]

The paper studies a reconfigurable intelligent surface (RIS)-assisted multi-user uplink massive multiple-input multiple-output (MIMO) system with imperfect hardware. At the RIS, the paper considers phase noise, while at the base station, the paper takes into consideration the radio frequency impairments and low-resolution analog-to-digital converters. The paper derives approximate expressions for the ergodic achievable rate in closed forms under Rician fading channels. For the cases of infinite numbers of antennas and infinite numbers of reflecting elements, asymptotic data rates are derived to provide new design insights. The derived power scaling laws indicate that while guaranteeing a required system performance, the transmit power of the users can be scaled down at most by the factor $\frac{1}{M}$ when $M$ goes infinite, or by the factor $\frac{1}{MN}$ when $M$ and $N$ go infinite, where $M$ is the number of antennas and $N$ is the number of the reflecting units. Furthermore, an optimization algorithm is proposed based on the genetic algorithm to solve the phase shift optimization problem with the aim of maximizing the sum rate of the system. Additionally, the optimization problem with discrete phase shifts is considered. Finally, numerical results are provided to validate the correctness of the analytical results. [ABSTRACT FROM AUTHOR]

In complex indoor environments, there is often no Line of Sight (LOS) path between the transmitter and the receiver due to walls, moving people, and many other obstacles. Traditional localization method based on LOS measurement is no longer available which provides lower localization accuracy in Non-Line of Sight (NLOS) environments. In response to this problem, this paper proposes a joint localization algorithm for scatterers and target user based on indoor prior information in NLOS scenarios. The algorithm makes use of the prior localization information of the access point (AP) and the wall to determine the position of the scatterers and target user simultaneously. Since the established constrained optimization problem is non-convex which is a difference of convex (DC) problem, it is transformed into a convex one based on a Taylor series expansion (TSE) method before it is solved. At the same time, as only a single AP signal can be received normally, the proposed method is then extended to the scenario with only one AP. Simulation and real-world testing results both show that not only the proposed method based on multiple APs, but also the method based on a single AP achieve high positioning accuracy, which are better than the existing positioning methods mentioned in this article as well. [ABSTRACT FROM AUTHOR]

This paper presents a modified orthogonal propagator method (OPM) with spline interpolation for direction of arrival (DoA) estimation. The conventional OPM is based on the noise-free signal model. When noise exists, the main contribution of noise (additive temporally and spatially white Gaussian noise) is in the diagonal elements of the data covariance matrix. Therefore, the influence of noise can be minimized by restoring the noise free diagonal elements through an interpolation procedure and the propagator can then be directly extracted from the denoised data covariance matrix, which is the main idea of the proposed method in this paper. The performance of the proposed modified OPM is tested through numerical simulations. Simulation results show that the proposed modified OPM achieves low root-mean square errors (RMSEs) with small computational load in DoA estimation. [ABSTRACT FROM AUTHOR]

In this paper, we consider a cooperative simultaneous wireless information and power transfer (SWIPT) network with one source–destination pair and multiple energy harvesting relays. How to efficiently use these relays has been addressed in conventional cooperative networks, and this paper investigates the impact of SWIPT on the performance of such multirelay cooperative networks. In particular, the main contribution of this paper is that a closed-form expression of the outage probability achieved by the multirelay cooperative protocol is obtained, as well as its approximation at high SNR. In addition, the performance of the cooperative network with the SWIPT relays is compared with that in conventional systems with self-powered relays. [ABSTRACT FROM PUBLISHER]

Wireless channel characterization and modeling is the foundation of vehicular communication systems. However, most of the existing researches on vehicle-to-vehicle (V2V) channel are aimed at the traditional scenarios such as urban and suburban, and the researches on some complicated vehicular scenarios are insufficient. For example, little attention is paid to viaduct, tunnel, and cutting scenarios in vehicular communications, and these complex scenarios often become the high-incidence area of communication interruption, and then affect the overall performance of the vehicular communication system due to the bad and unique channel characteristics. In this paper, to fill this gap, time-varying characteristics of V2V channels in viaduct, tunnel, and cutting scenarios are investigated. Specifically, based on 5.9 GHz channel measurements, channel non-stationarity is quantitatively evaluated and compared. Further, a detailed analysis of time-varying power and delay of multipath components (MPCs) is presented. Based on the obtained results, the mechanism of physical environment affecting channel characteristics is analyzed, and the impacts of vehicular scenarios are quantified. Besides, the impacts of scenarios on the spatial distribution of MPCs are investigated. The angular distributions of MPCs in the three typical V2V scenarios are revealed, and the corresponding statistical characteristics are presented. Moreover, the characteristics of MPC clusters in different scenarios are further quantified and analyzed, such as MPC number in each cluster and cluster lifetime. It is revealed that the non-stationarity of the V2V channel originates from the birth-death process of MPC clusters, and the birth-death process is modeled statistically. The results in the paper show the V2V channel propagation mechanism and can be used for the design of vehicular communication systems in complicated scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes on-driving experience sharing algorithms at junctions in infrastructure-assisted vehicles-to-everything networks. For the purpose, a millimeter-wave (mmWave) technology is used because it provides multi-Gbps data rates which is helpful for handling users’ short stay times at junctions and spatial reuse due to high beam directionality which is helpful for interference-avoidance among densely deployed vehicles at junctions. To realize on-driving experience sharing, the proposed algorithms focus on joint resource allocation and scheduling for 3GPP-compliant multiple unicast vehicle-to-vehicle (V2V) communications where the vehicles are group leaders (GLs) in 3GPP Mode 4(d). The resource allocation stands for the roadside unit (RSU) allocation to scheduled V2V GL links where RSU is essentially required for overcoming blockage by establishing two-hop relaying. Because vehicles stay for short times at junctions, this paper designs two algorithms without or with delay considerations. Without delay considerations, the joint optimization of RSU allocation and scheduling was originally formulated as mixed 0-1 non-convex optimization. However our proposed algorithm reformulates the problem into mixed 0-1 convex optimization, which is computationally easier to solve. With delay considerations, our proposed algorithm dynamically controls video contents frame rates for time-average on-driving video sharing quality maximization subject to delay constraints, inspired by Lyapunov optimization. Extensive simulation results demonstrate that our algorithms can significantly outperform in a variety of scenarios. Furthermore, we conduct the cost analysis for the proposed algorithms in terms of capital expenditure (CAPEX) and operating expenditure (OPEX). [ABSTRACT FROM AUTHOR]

The age of information (AoI) is a key performance metric for data freshness in real-time systems, measuring the time elapsed between status updates received at a remote destination. This paper addresses the analysis of both the AoI and the delay for a system demanding timely status updates, consisting of an internet of things (IoT) community, and a multi-core edge computing node (EN). The IoT devices perform channel contention according to a slotted aloha non-orthogonal multiple access scheme with two transmission power levels to access the EN. The objective of the paper is the dynamic selection of both the EN computation capabilities and the suitable access probability for the two power levels, aiming at maximizing the time-average system utility, while guaranteeing the queues stability. The device end-to-end delay is analyzed by resorting to the martingale theoretical approximation under the condition that the Lyapunov optimization framework is performed on a time slot basis, hence leading to a novel instantaneous mixed integer problem formulation. Furthermore, the generalized benders decomposition method is employed to provide a suboptimal solution for the formulated problem. Finally, the accuracy of the proposed framework is validated by comparing the obtained analytical predictions with simulation results. [ABSTRACT FROM AUTHOR]

Radio signal recognition has a wide application in future communication systems and the vehicular communication, whose core is the extraction of signal features such as electromagnetic fingerprints. With the rapid development of artificial intelligence technology, deep learning has made amazing breakthroughs in image recognition, speech recognition and other fields. Deep learning is applied to electromagnetic fingerprint extraction in this paper. Firstly, thousands of the downlink aircraft communications addressing and reporting system (ACARS) signals used for communication between civil aircraft and airport tower are collected and generated. Then a pre-transformation network suitable for electromagnetic signals is constructed to convert one-dimensional signals into two-dimensional feature maps, and afterwards the feature maps are input into the convolutional neural network (CNN) for classification. By adopting the attention modules, the classification results were improved by a few percentage points over the baseline with a little cost. The method proposed in this paper achieves an accuracy rate of 94.1% and can obtain the aircraft type in a shorter time than traditional method. Moreover, the robustness of the proposed model in response to additive Gaussian white noise (AWGN) and phase deviation is studied and tested. [ABSTRACT FROM AUTHOR]

Millimeter-wave rotary-wing (RW) unmanned aerial vehicle (UAV) air-to-ground (A2G) links face unpredictable Doppler effect arising from the inevitable wobbling of RW UAV. Moreover, the time-varying channel characteristics during transmission lead to inaccurate channel estimation, which in turn results in the deteriorated bit error probability performance of the UAV A2G link. This paper studies the impact of mechanical wobbling on the Doppler effect of the millimeter-wave wireless channel between a hovering RW UAV and a ground node. Our contributions of this paper lie in: i) modeling the wobbling process of a hovering RW UAV; ii) developing an analytical model to derive the temporal autocorrelation function (ACF) for the millimeter-wave RW UAV A2G link in a closed-form expression; and iii) investigating how RW UAV wobbling impacts the Doppler effect on the millimeter-wave RW UAV A2G link. Numerical results show that different RW UAV wobbling patterns impact the amplitude and the frequency of ACF oscillation in the millimeter-wave RW UAV A2G link. For UAV wobbling, the temporal ACF decreases quickly and the impact of the Doppler effect is significant on the millimeter-wave A2G link. [ABSTRACT FROM AUTHOR]

The use of antenna arrays is indispensable in radar and multiple-input multiple-output (MIMO) wireless communications areas, as it permits the treatment of wavefronts in the spatial domain, enabling important applications such as beamforming and target detection. Spatial information is commonly given in terms of spatial correlation functions (SCFs), but its representation in terms of angular domain, such as the power azimuth spectrums (PASs), is sometimes more convenient. Therefore, transforming from SCF to PAS, and vice versa, is often required. This is commonly done by employing so-called beamformers, where the Bartlett beamformer (BB) is the most commonly utilized. However, the use of beamformers alone is not enough for retrieving PAS information from a given SCF. Particularly, this paper shows that a non-linear operation is missing in relating SCFs to PASs as provided by current beamformers. The paper also shows the mathematical expressions that correctly relate SCFs to PASs and wavenumber spectrums (WNSs) for the general case of systems with antenna arrays. The developed theory results in two new beamformers, which, corroborated by simulation results, solve any previous inconsistency in estimating PASs from SCFs. [ABSTRACT FROM AUTHOR]

Factor graph optimization (FGO) is a well-known approach in the robotics field. Due to multiple iterations and application of large amounts of data, it usually could superior performance than the Kalman Filter (KF) under complex scenarios. However, it is rarely used in the underwater environment, especially for applications based on fiber-optic inertial navigation. Taking the ultra-short baseline system (USBL) as the application background, a novel inertial navigation system (INS)/USBL integrated navigation scheme based on FGO is presented in this paper. The traditional pre-integration factor simplifies the earth rotation and coordinate system, which limits its application in high-precision sensors. This paper proposed an improved pre-integration model using the high precision measurements of fiber optics gyroscope in FGO. Simulation and field tests proved that, with the FGO method, improved accuracy of INS/USBL integration was obtained compared to the traditional KF method. It confirms the potential of FGO in the applications of underwater navigation and positioning. [ABSTRACT FROM AUTHOR]

In order to achieve the purpose of extending the speed range and reducing power electronics devices for the permanent-magnet linear motor drive system, a half-centralized open-end winding (OEW) topology is studied for rail transit applications in this paper. In this topology, compared with the traditional OEW topology, the number of voltage-source-inverters (VSIs) can be reduced from four to three to drive two OEW movers and all these VSIs share a common dc bus voltage. In order to improve the steady-state performances, a four-vector phase model predictive voltage control (FV-PMPVC) is proposed in this paper. Due to the spatial distribution of phase voltage vector (PVV), an orthometric synthesis principle (OSP) is proposed to calculated the duration time for the vector synthesis, which reduces the computation burden and enhances the robustness. Comparing the existing control method, FV-PMPVC can get similar dynamic performances but better steady-state performances. Additionally, the half-centralized topology using FV-PMPVC can have same speed range while less VSIs are required. The effectiveness of FV-PMPVC is verified by experimental results. [ABSTRACT FROM AUTHOR]

In this paper, we consider an energy efficient resource allocation technique for a hybrid time division multiple access (TDMA) - non-orthogonal multiple access (NOMA) system. In such a hybrid system, the available time for transmission is divided into several sub-time slots, and a sub-time slot is allocated to serve a group of users (i.e., cluster). Furthermore, signals for the users in each cluster are transmitted based on the NOMA approach. With NOMA, multiple users can be served simultaneously through utilizing power domain multiplexing at transmitter and successive interference cancellation (SIC) at receiver. In this paper, to maximize the energy efficiency (EE), we jointly allocate both the available time slots and the available transmit power in the hybrid TDMA-NOMA system. In particular, we formulate an EE maximization (EE-Max) problem aiming to maximize the overall EE of the system with a per-user minimum rate and transmit power constraints. However, this joint optimization problem is non-convex in nature, and thus, cannot be solved directly. Therefore, we develop an iterative algorithm by approximating the original problem into a convex one with sequential convex approximation (SCA) and a novel second-order cone (SOC) approach. Simulation results demonstrate that the performance of the proposed hybrid TDMA-NOMA system with joint resource allocation outperforms the system with equal time allocation in terms of the overall EE. Simulation results further confirm that the proposed iterative approaches with SCA and SOC techniques converge within a few number of iterations while yielding the solution to the original non-convex problem. [ABSTRACT FROM AUTHOR]

With the advancement of wireless communication technologies and rich embedded sensors in smart mobile devices, mobile crowdsensing has become an attractive paradigm for collecting sensing data from surrounding environments. Effective incentive mechanisms often play an important role in guaranteeing high quality of services and wide involvement of workers. Much work has been carried out in this aspect to encourage workers to strenuously and truthfully provide high quality data. However, task quality does not only depend on workers’ subjective effort or truthfulness, but also workers’ distribution and task quality growth law. In this paper, we consider a scenario where worker arrivals are unevenly distributed and the growth of task quality conforms to the law of diminishing margin of workers’ efforts. We propose a Quality-driven Online Task-Bundling-based incentive mechanism (QOTB). The design objective is to maximize the social welfare while maximally satisfying the task quality requirements. In QOTB, we introduce Mental accounting Theory to build accounts for task execution profit and bonus, respectively, which are then used to derive the participation willingness of workers. We adopt task bundling to stimulate workers to change their original travel schedules for balancing the task participations according to the popularities of task locations and also the traveling cost. We present the detailed mechanism design of QOTB. We prove that the QOTB mechanism has desired properties of willingness truthfulness, individual rationality, and computation efficiency. We conduct extensive simulations and the results show that QOTB can effectively improve the social welfare while satisfying the task quality requirements. [ABSTRACT FROM AUTHOR]

Future communication networksrequire higher bandwidth, greater coverage, and better throughput. The Space-Air-Ground Integrated Network (SAGIN) has the advantage of wide-area coverage and can cover global communications. It can meet the needs of network resources for maritime activities and remote mountainous areas, which is of great significance to the realization of a new generation of communications networks. With the development of the network and the emergence of delay-sensitive applications such as the Internet of Things, improving the delay performance of the system has received extensive attention. SAGIN involves multiple networks and is more complex than other networks. If there is no reasonable management between different networks, it is easy to lead to difficult link deployment and high time delay. On the basis of research and customization of SFC technology, it can provide a wide range of services and other advantages. It has significant performance in application scenarios involving multi network integration. In order to solve the above problems, this paper studies the SAGIN architecture of SFC based on business types. A service function chain mapping method based on delays prediction is proposed. Calculate the delay of the deployment path and select the path with the lowest delay as the SFC mapping path. The service model is constructed according to the mapping path, and the network slices are divided based on the service type. The simulation results show that the SFC mapping algorithm based on time delay prediction is compared with the traditional SFC mapping scheme. The algorithm does not affect other indicators, the CPU resource utilization rate is 27.8 $\%$ higher, and the link resource utilization rate is 22.7 $\%$ higher. The service acceptance rate increased by 21.5 $\%$ , the latency performance increased by 38.2 $\%$ , and the total resource consumption is reduced by 25.2 $\%$. [ABSTRACT FROM AUTHOR]

Highway platooning of vehicles has been identified as a promising framework in developing intelligent transportation systems. By autonomous or semi-autonomous vehicle control and intervehicle coordination, an appropriately managed platoon can potentially offer enhanced safety, improved highway utility, increased fuel economy, and reduced emissions. This paper is focused on quantitative characterization of the impact of communication information structures and contents on platoon safety. By comparing different information structures that combine front sensors, rear sensors, and wireless communication channels, as well as different information contents such as distances, speeds, and drivers' actions, we reveal a number of intrinsic relationships between vehicle coordination and communications in platoons. Typical communication standards and related communication latency are used as benchmark cases in this paper. The findings of this paper provide useful guidelines in sensor selections, communication resource allocations, and vehicle coordination. [ABSTRACT FROM AUTHOR]

The papers in this special section focus on electric power trains, with particular emphasis on state-of-the-art research and development and new design concepts. These papers were presented at the IEEE Vehicle Power and Propulsion Conference (VPPC) that was held in Hangzhou, China, in October 2016. [ABSTRACT FROM PUBLISHER]

This paper proposes a model predictive current control (MPCC) method of six-phase permanent magnet synchronous hub motor (PMSHM) using auxiliary voltage vectors. First of all, 26 voltage vectors can be obtained from the switching states of six inverters, and 24 auxiliary voltage vectors can be generated by combining them. Second, four virtual voltage vectors can be found through the required locations, and the two vectors with the lowest cost will be selected by cost function. Then, additional current error correction module can better track the current. Finally, the proposed method is compared with the conventional MPCC method and another method with virtual voltage vectors. The innovation of this paper is that the recombination of vectors can better track and predict the current. A current error compensation module is added to reduce the error between the actual current and the predicted current. And the experimental results show that the proposed method has better performance such as smaller torque ripple and current THD both in steady and dynamic states. Finally, the strategy proposed in this paper was applied to the vehicle model through HIL test platform. The possibility of applying the strategy proposed in this paper to pure electric vehicles was verified under New European Driving Cycle (NEDC). [ABSTRACT FROM AUTHOR]

The intelligent radio is a novel emerging paradigm catching on in many practical applications, in order to provide context awareness techniques, able to learn by the environment conditions applying machine learning frameworks. The channel state information (CSI) prediction is one of the main challenges that an intelligent radio has to face with. This paper deals with the CSI prediction in the case of a buffer aided relaying communication scheme with adaptive link selection previously proposed in the literature where a direct source-destination link is not available and with both links affected by an independent fading. In particular, the CSI prediction has been reduced here to a time series prediction problem, performed by involving the usage of the recurrent neural networks. Finally, the validation of the effectiveness of the proposed intelligent radio buffer-aided relaying scheme with adaptive link selection approach has been pursued by presenting performance comparisons with the previously proposed scheme under the assumption of an ideal and actual CSI estimations, respectively. [ABSTRACT FROM AUTHOR]

The article presents the discussion on predictive energy management strategy of plug-in hybrid electric vehicles by real-time optimization and data-driven calibration. Topics include relating to road vehicles, the electrification of railway vehicles, air and water vehicles requiring fast development in combating the global climate change; and criteria for fuel cell operation finding the best trade-off between hydrogen consumption and number of start-ups.

This paper proposes a traction unit that combines an interior permanent magnet motor and a PM-assisted synchronous reluctance motor for electric vehicles to improve traction performance, enhance energy efficiency, and reduce cost. The characteristics of three traction units are analyzed and compared, including (a) rear-wheel-drive configuration using one IPM motor; (b) all-wheel-drive configuration using two interior permanent magnet motors; and (c) all-wheel-drive with a combination of one interior permanent magnet motor and one PM-assisted synchronous reluctance as proposed in this paper. The operational characteristics of each motor to achieve a wide constant power speed range are first investigated. Then, the Urban Dynamometer Driving Schedule is utilized to compare and evaluate the operational characteristics, energy consumption, and most importantly, the efficiency of the traction units. Simulation results reveal that the overall efficiency of the proposed traction unit can be improved compared to the other configurations. The advantages and drawbacks of the proposed traction unit are also investigated. Finally, experiments are conducted to validate the analysis. [ABSTRACT FROM AUTHOR]

In the controller development process, it is required to verify whether control functions operate normally, without fault or anomaly. Many control functions with complex system structures require various actual tests and test data analysis for verification. This paper presents an anomaly detection algorithm to verify control functions and to more efficiently analyze test data of a hybrid control unit (HCU) of a hybrid electric vehicle (HEV). The anomaly detection algorithm automatically detects anomaly of control functions from the test data, instead of manual labor by engineers. The examined target HCU control functions in this paper are the engine clutch engagement/disengagement control function and the engine start cooperative control function, which are crucial functions of transmission mounted electric drive (TMED) HEVs. A data-driven approach using a one-class support vector machine (SVM) is used to make it easy to apply various control functions. Actual vehicle test data is examined by the algorithm to verify the control functions at an actual vehicle level. The developed anomaly detection algorithm demonstrates feasibility and effectiveness of the proposed algorithm in detecting not only prior-known anomalies but also prior-unknown anomalies. Because other vehicle control functions have similar characteristics with the target functions of this paper, this algorithm is expected to be successfully applied to other HCU control functions. [ABSTRACT FROM AUTHOR]

In concurrent spectrum sharing scenarios between radars and wireless communication systems, one strict approach to protect the radar from undesired interference is locating the base stations outside a guard (or exclusion) zone of the radar. As a result, users inside that zone experience bad communication coverage. This paper uses Reconfigurable Intelligent Surfaces (RISs) to improve the base station coverage of users in the radar exclusion zone. The paper shows, through analysis and simulation, that joint optimization of base station and RIS locations improves the probability of coverage in the exclusion zone over the shared spectrum without disturbing the radar operation. [ABSTRACT FROM AUTHOR]

This paper proposes a model predictive current control (MPCC) method based on current augmenter prediction model (CAPM) appropriate for interior permanent magnet synchronous motor (IPMSM) under high-speed condition. The accuracy of CAPM is improved by considering rotor movement during one control period especially under high-speed condition. Meanwhile, the use of the permanent magnetic flux linkage is eliminated. In order to further improve the robustness of the MPCC method, an adaptive observer based on CAPM is designed to estimate the model error. The proposed adaptive observer also eliminates the use of the permanent magnetic flux linkage, so the whole implementation process of the MPCC method with adaptive observer is independent of the permanent magnetic flux linkage. Moreover, since the adaptive observer with fixed adaptive integral gain cannot achieve satisfactory performance under different operating conditions of the motor, this paper designs a calculation method for the variable adaptive integral gains by considering the current estimation error, the amplitude of current and the angular velocity of the IPMSM. Finally, the proposed method is validated by means of the experimental results on a 20-kW test platform. [ABSTRACT FROM AUTHOR]

In this paper, we develop a novel visual-inertial navigation system with motion constraint (VINS-Motion), which extends the visual-inertial navigation system (VINS) to incorporate vehicle motion information for improving the autonomous vehicles localization accuracy. We introduce the vehicle stop detection module in the pre-processing measurement procedure. During the back-end processing, if the vehicle is detected to be in a stopping state, we perform optimization based on the stop constraint to help eliminate the abnormal jitter of the estimated pose, thus ensuring the reasonability of the trajectory. Otherwise, besides the prior information, IMU measurement residual, and visual measurement residual utilized in VINS, vehicle orientation/velocity constraint built by Ackerman steering model is first exploited to constitute residuals. We minimize the sum of priors and Mahalanobis norms of three kinds of residuals to obtain a maximum posterior estimation, thus increasing system consistency and accuracy. The proposed approach is validated on public datasets and compared with state-of-the-art algorithms, which demonstrates that the motion constraint proposed in this paper improves the standard VINS performance and achieves significantly higher positioning accuracy. [ABSTRACT FROM AUTHOR]