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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 AUTHOR]

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]

Multiple-input-multiple-output (MIMO) systems are of interest for meeting the expected demand for higher data rates and lower delays in future wireless packet data systems. In such systems, it is optimal to simultaneously transmit to multiple users compared with a single user in a single-input-single-output system. In addition, multicarrier systems are of interest to combat frequency-selective fading that is experienced over the larger bandwidth that these future broadband systems will use. The use of dirty paper coding further complicates the matter, because the order in which the users are encoded will affect the rates that they can achieve. A well-designed cross-layer scheduling algorithm must take into account the multiple dimensions of this resource-allocation problem and other quality-of-service (QoS) parameters to fully exploit the communications channel. The scheduling problem is often expressed in terms of optimizing some utility function. Unfortunately, the search space for this optimization problem is extremely large, which prohibits an optimal exhaustive search. To this end, we investigate the use of genetic algorithms to reduce the complexity of the scheduling. This paper builds upon prior work that implements scheduling via genetic algorithms in the context of single-carrier systems using zero-forcing beamforming (ZFB). In this paper, we investigate how the genetic algorithm can be adapted to account for the effect of encoding order on the scheduling and how the scheduling can be extended to a multicarrier system. In particular, we investigate the maximum throughput and proportionally fair scheduling criteria. We demonstrate that the performance of the genetic algorithm is near optimal compared with an exhaustive search at a greatly reduced computational complexity. Furthermore, in the case of a multicarrier orthogonal frequency-division multiplexing (OFDM) system, an increase in capacity is shown relative to the single-carrier case. [ABSTRACT FROM AUTHOR]

This paper investigates an optimization of the conventional relay selection for multirelay environments. In contrast with previously reported selection schemes, where a selected relay accesses the channel in a dedicated cooperative slot, the proposed scheme recovers the bandwidth loss of the half-duplex constraint by allowing two relays to simultaneously access the channels. Based on an appropriate dirty-paper coding (DPC) technique among relays, the proposed scheme enables a relay to establish communication with the destination at the same time that another relay forwards the data from the source. It is proven that the interplay between relay selection and the superposition DPC weight factor provides a tradeoff between relaying and new data performance. Hence, an appropriate codesign of the superposition DPC parameter and opportunistic relay selection can achieve efficient communication for the new data without affecting the relaying performance. The proposed scheme is compared with conventional relaying approaches, and its enhancements are provided through theoretical studies and numerical results. [ABSTRACT FROM AUTHOR]

In this paper, we extend the interference channel with degraded message sets model by introducing multiple secondaries. The new model allows for multiple concurrent transmissions in a wireless network by employing dirty paper and superposition coding. We apply the proposed scheme to the communication between nodes in a wireless mesh network (WMN). The cases of different WMN. topologies are analyzed, and numerical results show that considerable capacity gains are achieved when using the proposed model with multiple secondaries. [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]

An equation in the Appendix of a previous paper (IEEE Trans. Veh. Technol ., vol. 58, no. 7, pp. 3247–3262, Sep. 2009) contains an error, which propagates to a few more equations in the Appendix. This error does not affect the main body of the text or results presented in that paper, apart from a change to one equation. In this correspondence, we correct the error and provide a revised Appendix. [ABSTRACT FROM PUBLISHER]

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]

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 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]

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]

The article discusses about the special section on cross-layer design for mobile ad hoc networks and wireless sensor networks published within the May 01, 2006 issue in the U.S. The first section, which consist of seven papers, focuses on the capacity analysis/enhancement and quality-of-service support. The following papers, from eight to twelve, address the routing issues. In addition, four paper after the 12th explores the idea of cross-layer design.