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This paper investigates distributed MIMO precoding design to maximize the weighted sum rate in a heterogeneous network where the multiple input multiple output full-duplex (MIMO-FD) small cells reuse the downlink spectrum of the macro base-station (BS) to exchange backhaul information. The multi-antenna BS transmits signals to the cellular users using the dirty paper coding technique and the MIMO-FD small cells apply FD precoding structures to effectively balance the received signal, the self-interference (SI), and the co-channel interference (CCI). Since the optimization problem is shown to be nonconvex, obtaining the global optimum is challenging. A low-complexity solution with distributed implementation is introduced with proved convergence. By applying the successive convex approximation technique and the duality between the broadcast channel and the multiple access channel, the original nonconvex problem is decomposed into a sequence of convex subproblems, which can be solved analytically and separately at each small cell and macro BS with limited channel state information exchange. Simulation results confirm the convergence and demonstrate the benefits of the introduced algorithm. It is shown that the SI and CCI can be suppressed effectively with sufficient cancellation power and number of transmit antennas at the FD small cells.

Visual 3D perception is a key interface between vehicles and the environment which provides rich perception information for autonomous vehicles. In vehicular visual 3D perception, camera position uncertainty can often degrade the perception and localization performance, and the information coupling and propagation among 3D points and cameras exist ubiquitously. However, these factors do not receive the deserving concern in many previous algorithms which mainly focus on the consideration of efficiency. In this paper, we develop a statistical framework for visual 3D perception in vehicular networks from the perspective of information gains. Specifically, we first derive the influence of camera position uncertainty on perception and localization quality with geometric interpretations given by the information ellipsoid, and also present the perception loss caused by uncertainty in the camera deployment application. Then we determine the information coupling and propagation among 3D points and cameras in the vehicular network, propose the visual information graph to characterize the coupling, and derive the hierarchical structure in propagation. Besides, we also propose the equivalent circuit for the quasi-tree network to interpret the hierarchical structure and derive the blocking and the end- link effects for network reduction. The results of this paper provide guidelines for the development of efficient uncertainty- and coupling-aware visual 3D perception techniques in vehicular networks.

Radar-based human activity recognition (HAR) finds various applications like assisted living and driver behavior monitoring. As radar data are heavily environment-dependent, it is becoming increasingly important to develop a transfer learning mechanism that enables a radar-based HAR system with desirable cross-environment adaptation feasibility. This paper concerns the issue of how radar-based HAR system can adapt to a new environment without source data. To this end, we devote to using the source hypothesis transfer learning architecture to build such an environment adaptation mechanism towards cross-environment radar-based HAR. In doing this, it is a challenging task to develop a reliable self-supervised labeling strategy for generating pseudo labels associated with the unlabeled target data, which is crucial to facilitate the learning of a target-specific feature extractor being responsible for environment adaptation. This paper presents the neighbor-aggregating-based labeling method and incorporates it with the existing clustering-based labeling method to perform the self-supervised labeling task. The logic behind our approach is that the above two labeling methods are complementary to each other in terms of making use of both local and global structures of adaptation data to supervise the labeling task. The coordination of both labeling methods is motivated to be implemented in the weighted combination form, which contributes to improving the reliability of generated labels. Experimental results on a public HAR dataset based on the frequency modulated continuous wave (FMCW) radar demonstrate the effectiveness of our approach.

This paper proposes a path tracking controller to assist the electronic-four-wheel drive (e-4WD) vehicle in following the desired path profile. It utilizes only readily available in-vehicle sensors and a standalone global positioning system (GPS). Noteworthy, both the lateral distance error and the heading angle error between the desired path profile and the vehicle are calculated using only a standalone GPS. Therefore, the proposed algorithm can be easily applied to mass-produced e-4WD vehicles. A model predictive control (MPC) is selected as the controller type, which derives optimal control input considering both state and input constraints of in-wheel motor (IWM) in the e-4WD vehicle. Due to the advantage of this MPC predicting the vehicle's future dynamic behavior in advance, it is possible to output a more preemptive and stable yaw moment for vehicle path tracking. Finally, a weighted least square (WLS) allocation that is suitable for redundant actuator configuration is utilized to distribute the optimal IWM torques to the front left and right wheels of the e-4WD vehicle. The major contributions of the proposed control algorithm are as follows. 1) It is the first time in this paper that the MPC is applied to path tracking of e-4WD vehicles. 2) The condensed constraint matrix of the proposed MPC has a structure that can be easily applied to the linear programming. Using the CarSim simulation and the real-car based experiment, the effectiveness of the proposed MPC algorithm is verified by a comparative analysis. Thus, the high path tracking accuracy of the proposed algorithm is confirmed.

Rail transportation, especially, high-speed rails (HSR), is an important infrastructure for the development of national economy and the promotion of passenger experience. Due to the large bandwidth, millimeter wave (mmWave) communication is regarded as a promising technology to meet the demand of high data rates. However, since mmWave communication has the characteristic of high attenuation, mobile relay (MR) is considered in this paper. Also, full-duplex (FD) communications have been proposed to improve the spectral efficiency. However, because of the high speed, as well as the problem of penetration loss, passengers on the train have a poor quality of service. Consequently, an effective user association scheme for HSR in mmWave band is necessary. In this paper, we investigate the user association optimization problem in mmWave mobilerelay systems where the MRs operate in the FD mode. To maximize the system capacity, we propose a cooperative user association approach based on coalition formation game, and develop a coalition formation algorithm to solve the challenging NP-hard problem. We also prove the convergence and Nashstable property of the proposed algorithm. Extensive simulations are done to show the system performance of the proposed scheme under various network settings. It is demonstrated that the proposed distributed low complexity scheme achieves a nearoptimal performance and outperforms two baseline schemes in terms of average system throughput., Comment: 11 pages, 11 figures

The sensitivity to blockages is a key challenge for the high-frequency (5G millimeter wave and 6G sub-terahertz) wireless networks. Since these networks mainly rely on line-of-sight (LOS) links, sudden link blockages highly threaten the reliability of the networks. Further, when the LOS link is blocked, the network typically needs to hand off the user to another LOS basestation, which may incur critical time latency, especially if a search over a large codebook of narrow beams is needed. A promising way to tackle the reliability and latency challenges lies in enabling proaction in wireless networks. Proaction basically allows the network to anticipate blockages, especially dynamic blockages, and initiate user hand-off beforehand. This paper presents a complete machine learning framework for enabling proaction in wireless networks relying on visual data captured, for example, by RGB cameras deployed at the base stations. In particular, the paper proposes a vision-aided wireless communication solution that utilizes bimodal machine learning to perform proactive blockage prediction and user hand-off. The bedrock of this solution is a deep learning algorithm that learns from visual and wireless data how to predict incoming blockages. The predictions of this algorithm are used by the wireless network to proactively initiate hand-off decisions and avoid any unnecessary latency. The algorithm is developed on a vision-wireless dataset generated using the ViWi data-generation framework. Experimental results on two basestations with different cameras indicate that the algorithm is capable of accurately detecting incoming blockages more than $\sim 90\%$ of the time. Such blockage prediction ability is directly reflected in the accuracy of proactive hand-off, which also approaches $87\%$. This highlights a promising direction for enabling high reliability and low latency in future wireless networks., Comment: Submitted to IEEE, 30 pages, 11 figures. The dataset will be available soon on the ViWi website https://viwi-dataset.net/

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.

Safety-critical systems in various industries such as transportation or nuclear energy have been paid more attention with the development of societies due to increased attachment of importance to the life of human, their property, and nature. While developing such systems, detailed availability and safety characteristics are to be taken into account in parallel with architectural design decisions such as synchronization between different computing units or real-time task management. For fulfilling top-level requirements in international standards, ambitious quantitative targets like 0.012 FIT for HW units are to be reached where the industry has difficulties to achieve it. In this paper, this problem is handled by proposing an augmented Markov model for diverse architectures that is superior to the formulas provided in the main safety standard IEC 61508 and previous studies. With the proposed method it is possible to cover all safety-relevant states, which leads to more accuracy and lower hazard rates helping to reach these ambitious quantitative targets. Besides, the reliability parameters are investigated and optimized to increase safety performance. Consequently, the proposed novel model including enhanced reliability parameters is used for an industry application, namely safety-critical computer used for unmanned metro and high-speed rail transportation. The result obtained by the proposed model is compared with the results obtained using state of art models in literature and using the formulas in IEC 61508. As domain independent references IEC 61508 and Markovian approach are used in the paper, this study is applicable to other safety critical areas such as automotive or avionic industry.

As one of the three main application scenarios of 5 G cellular system, massive Machine-Type Communications (mMTC) has been regarded as the key solution to facilitate the IoT paradigm. One major bottleneck for accommodating mMTC is the severe congestion at the cellular random access channel when plenty of Machine-Type Devices (MTDs) send access requests concurrently while the preamble resources are limited. To remedy this issue, limiting the number of retransmissions and dropping access requests after the limit is reached can be an effective approach. Yet, the effect of the preamble retransmission limit $K$ on the optimal access performance of mMTC in cellular networks remains largely unexploited, which motivates the study in this paper. Specifically, in this paper, we start by characterizing the network steady-state points based on the limiting probability of successful transmission of access requests. We then obtain explicit expressions of the access throughput and the mean access delay of successfully-transmitted access requests as functions of $K$ and the number of preambles $M$ . The maximum access throughput and the corresponding optimal backoff window size are further derived. It is shown that the maximum access throughput is independent of $K$ , while the mean access delay can be significantly reduced with a small $K$ , yet, at the expense of increased request dropping ratio. In addition, to improve both the throughput and delay performance, the analysis shows that more preambles should be allocated but the performance gain becomes marginal when $M$ is large. Therewith, an algorithm is proposed for determining the least number of preambles $M^\ast$ that maximizes the access throughput and the preamble resource utilization ratio. Numerical results show that a smaller preamble retransmission limit $K$ can further reduce $M^\ast$ .

While the vehicular network enables geographically distributed cooperative computation, its mature implementation has long been constrained due to the lack of an effective management platform. In this paper, employing the security and privacy attributes of blockchain, we propose a novel Blockchain-enabled Large-scale Parked Vehicular Computing (BLPVC) architecture to utilize the potential solar energy and vehicular computational resources in the outdoor parking lot. However, the uneven green power supply and random arrival time of electric vehicles compose the highly complex environment. Accordingly, in this paper, to handle the efficient utilization of the distributed resources by blockchain technology, we propose an integrated optimization framework which leverages the green energy utilization and service latency limit among the processes of block generation, task computing, and communication, whereas such a design leads to the mixed-timescale stochastic optimization problem. To this end, corresponding to the dynamic solar energy arrival, we propose a shaped deep deterministic policy gradient (DDPG) algorithm to accelerate the learning rate of computational frequency control in the short-term stage; while in the long-term stage, for the mixed-integer programming (MIP) of task offloading and blockchain parameters adjustment, a series of transformation is employed to preserve convexity. Finally, experiments are carried out on Python demonstrating that the proposed scheme achieves a balanced performance between service latency and distributed resources, while the battery depreciation cost is heavily reduced.

The densification of wireless infrastructure to meet ever-increasing quality of service (QoS) demands, and the ever-growing number of wireless devices may lead to higher levels of electromagnetic field (EMF) exposure in the environment, in the 5G era. The possible long term health effects related to the EMF radiation are still an open debate and requires attention. Therefore, in this paper, we propose a novel EMF-aware resource allocation scheme based on the power domain non-orthogonal multiple access (PD-NOMA) and machine learning (ML) technologies for reducing the EMF exposure in the uplink of cellular systems. More specifically, we use the K-means approach (an unsupervised ML approach) to create clusters of users to be allocated together and to then strategically group and assign them on the subcarriers, based on their associated channel properties. Finding the best number of clusters in the PD-NOMA environment is a key challenge, and in this paper, we have used the elbow method in conjunction with the F-test method to effectively control the maximum number of users to be allocated at the same time per subcarrier. We have also derived an EMF-aware power allocation by formulating and solving a convex optimization problem. Based on the simulation results, our proposed ML-based strategy effectively reduces the EMF exposure, in comparison with the state-of-the-art techniques.

In this paper, an antenna synthesis process is investigated for Intelligent Transportation System (ITS) application. The wireless communication between Road Side Units (RSUs) and On Board Units (OBUs) located in the vehicles enables smart mobility. However, to support high speed vehicles and high data rates, a wider coverage area by the RSU antennas projected on the highway is required. This is defined by the radiation patterns of the RSU antennas. On a multi-lane highway, interference from the adjacent lanes is a critical issue which determines the communication reliability in terms of Single-to-Interference plus Noise Ratio (SINR). Hence, in this paper, an interference-aware antenna synthesis process is proposed and a planar array is optimized based on the objective function of obtaining a wide coverage area in terms of SINR distribution for a given modulation scheme. The proposed antenna array enhances the reliable communication area and provides improved SINR distribution across the lane for misaligned vehicles from the center of the lane. The results indicate that the proposed antenna array performs better than the existing designs in terms of communication reliability.

This paper presents a new soft switching high voltage gain interleaved dc-dc converter with a three-windings coupled inductor for a fuel cell vehicle. Two interleaved and intercoupled boost cells are connected by the input parallel and output series (IPOS) configuration, realizing low input current ripple and high voltage gain simultaneously. All power switches can operate under soft switching conditions by only adding a simple auxiliary circuit composed of one auxiliary inductor and one power switch. Besides, the auxiliary inductor is coupled with the main inductors, which results in a rapid zero convergence of the auxiliary current and a small conduction loss of the auxiliary circuit. The operating principle, steady state characteristic, comparison with other converters, and parameter design are analyzed in this paper. Finally, the main characteristics of the proposed converter are verified by a 1-kW laboratory prototype.

This paper proposes a cloud-assisted joint charging scheduling and energy management framework for unmanned aerial vehicle (UAV) networks. For charging the UAVs those are extremely power hungry, charging towers are considered for plug-and-play charging during run-time operations. The charging towers should be cost-effective, thus it is equipped with photovoltaic power generation and energy storage systems functionalities. Furthermore, the towers should be cooperative for more cost-effectiveness by intelligent energy sharing. Based on the needs and setting, this paper proposes 1) charging scheduling between UAVs and towers and 2) cooperative energy managements among towers. For charging scheduling, the UAVs and towers should be scheduled for maximizing charging energy amounts and the scheduled pairs should determine charging energy allocation amounts. Here, two decisions are correlated, i.e. , it is a non-convex problem. We re-formulate the non-convex to convex for guaranteeing optimal solutions. Lastly, the cooperative energy sharing among towers is designed and implemented with multi-agent deep reinforcement learning and then intelligent energy sharing can be realized. We can observe that the two methods are related and it should be managed, coordinated, and harmonized by a centralized orchestration manager under the consideration of fairness, energy-efficiency, and cost-effectiveness. Our data-intensive performance evaluation verifies that our proposed framework achieves desired performance.

Recently, numerous electronic components are installed in vehicles, providing drivers and passengers with increased safety and convenience. The electronic components construct an in-vehicle network that internally shares relevant status information about the vehicle. As modern vehicles become more computerized, the potential for automotive cyber-security threats also increases a fact that has been illustrated clearly by various car-hacking demonstrations. Using the controller area network (CAN), the de facto standard protocol in the automotive industry that facilitates in-vehicle network communication, car-hacking demonstrations inject critical CAN messages to control vehicular functions. In efforts to address this security issue, car manufacturers, in turn, have made confidential the CAN database (i.e., DBC format file), where signal information assigned in the CAN data payload is specified. However, it has since become known that this policy does not hermetically seal a vehicular network against cyber attacks. On the contrary, in-depth automotive security research has been hindered significantly because of the limited information accessible by researchers. For example, automotive intrusion detection systems (IDS) identify and alert when there is a vehicular break-in, and this technology is a major area of study in automotive cyber security research. For the automotive IDS that analyzes CAN traffic, information in the DBC format file greatly improves detection veracity. However, most IDS technologies to date have been independently developed without the confidential CAN DB information and, as a result, do not mitigate threats to a satisfactory standard. In this paper, we propose an enhanced method that identifies signal boundaries in a CAN data payload, which is specified in the DBC format file. Unlike an existing method that is designed based on total bit-flip rates, our method analyzes bit-flip time series not total bit-flip rates so that signal boundaries can be more clearly identified. In this paper, we use a publicly available DBC format file called OpenDBC as a reference, and show that our method outperforms the existing method.

In this paper, we propose a multi-channel medium access control (MMAC) protocol for the exploitation of retro-directive array antennas in Flying Ad Hoc Networks (named FA-MMAC). This FA-MMAC employs multiple channels and retro-directive array antennas to adjacent unmanned aerial vehicles (UAVs) to transmit simultaneously over the same channel without causing interference. The channel utilization characteristic of FA-MMAC increases the spatial reuse of channel and thus improve the system performance. The key idea is that UAVs first exchange control packets in control window to select data channels and to align beams before exchanging data packets in the data window. In this way, the hidden-terminal and deafness problems are reduced. UAVs can reserve channels and align beams through a circular directional transmission of control frames so that the protocol can take advantage of the benefits of retro-directive array antennas of spatial reuse and increased coverage without prior knowledge of the locations. We develop both antenna model and channel model for FA-MMAC protocol. Analysis and simulation results show that our proposed FA-MMAC can offer a significant performance gain for both static and mobile scenarios than the existing MMAC and 802.11-based protocols. Our findings reported in this paper provide some insights into the performance of multi-channel Flying Ad Hoc Networks which might help network researchers to contribute in the development of next generation UAVs.

This paper addresses the congestion problem in cellular networks through the assistance of parcel delivery drones. Drones have the primary task of parcel delivery and a secondary task of acting as relay nodes (hop) to add extra capacity in a cellular network. Base transceiver stations in the cells can admit ground user equipment to communicate directly with the drone (device-to-device communications) through in-band signalling between them. This paper proposes a novel algorithm, called CARLO, which optimizes trajectory and altitude of the drones in order to deliver physical parcels, maximize network capacity and reduce network interference. Our simulation studies show that our solution improves the network throughput while the energy inefficiency of taking detour path to assist congested cells is kept to a minimum. Moreover, it is guaranteed that parcels are delivered before deadline.

In this paper, an algorithm for optimizing the transmission protocol which considers the weights of transmission nodes in a wireless body area network (WBAN) is studied. The source broadcasts the radio frequency (RF) signal to transmit wireless energy to all other nodes in the system, and then the relay and sensors collect energy. Using the energy harvested, each sensor transmits information data back to the source in turn by time division multiple access (TDMA). Since the importance of sensors in the transfer network is different, in this process, the weighted throughput proposed in this paper can better reflect the system's information transmission efficiency. Motivated by this, a joint weights optimizing time slot allocation protocol (JWTA) is proposed. The specific weight of each sensor is based on the analytic hierarchy process algorithm (B-AHP). When dynamically selecting several sensors to form a certain communicating network, we can quantitatively calculate the physiological weight value of each node in current network through this algorithm. That weight value can directly reflect the importance of physiological signals collected by the detecting sensor to human health. With this foundation, this paper reallocates transmission time slots combining with the obtained weights, which also optimizes the traditional time switching (TS) protocol. The simulation result shows that the weighted throughput of the JWTA protocol is significantly better than that of the classic algorithm (that is, optimizing time slots individually) and equally assigned transmitting time slot algorithm. The influence of various system parameters is also analyzed.

A swarm of Autonomous Underwater Vehicles (AUVs) can provide richer spatial-temporal information than the traditional single-robot system, which can be used for underwater mapping, exploration, target tracking, among others. However, the limitation of AUVs’ battery cannot support persistent services, which restricts AUVs’ operating range and mission duration. In this paper, a mobile underwater charging solution is developed that can continuously recharge a swarm of AUVs by using a wireless mobile charger. Magnetic induction-based communication and wireless energy transfer are employed. This paper first shows that by using tri-axis coils, reliable wireless communication and wireless energy transfer without coil orientation losses can be obtained. After that, wireless communication, motion control, and wireless energy transfer are jointly designed for a swarm of mission-driven AUVs. In particular, optimal continuous motion controllers are developed for AUVs to avoid intra-swarm collisions and networking protocols are designed to optimally allocate resources for wireless communication and wireless energy transfer. The proposed approach can guide AUVs to their destinations, while maintaining wireless network integrity and maximizing wireless energy transfer efficiency, upon which the constraints on AUVs’ batteries can be eliminated and cheaper and smaller AUVs can be employed for various underwater applications.

A vehicular-fog (VF) system as an emerging platform consists of electric vehicles with computing resources that are mostly under-utilized. This paper considers a two-tier federated Edge and Vehicular-Fog (EVF) system, where edge systems may partially offload user traffic to nearby VFs for potential cost reduction. Offloading configuration is to determine the ratios and targets of offloading traffic for maximal cost reduction, which is formulated as a mixed integer programming problem in this paper. We first present a decentralized offloading configuration protocol (DOCP) for an individual edge system to set up its own offloading configuration. We then propose a matching protocol among multiple edge systems to resolve resource contention when they simultaneously request resources from the same VF. Simulation results show that the proposed approach can leverage the heterogeneity of cost and capacity between edge systems and VFs. The proposed protocol outperforms greedy approaches by at most 40% and is comparable to a centralized off-line approach that is based on Particle Swarm Optimization.

Intelligent mobile vehicles asks for the ability to learn to comprehend immediate surroundings similar to human cognition and autonomously navigate in an unknown scene. Although simultaneous localization and mapping (SLAM) can construct a geometrically surrounding map, the map contains little semantic information. This paper presents a novel stereo visual-inertial system with loop closure detection based on a semantic-topological map framework. In our system, a hybrid 3D point cloud semantic-topological mapping framework is used to realize autonomous navigation simply by providing a map for path planning and meanwhile for storing semantic information under a dynamic environment. A stereo visual-inertial system can obtain more accurate visual-inertial odometry than a monocular system, and a visual-inertial system is capable of real-time map correction when the localization module detects a loop closure. This paper adopts state-of-the-art Mask R-CNN components to obtain 2D semantic information and project it to a semi-dense 3D semantic-topological map built by stereo visual-inertial SLAM. We perform experimental evaluation of our proposed system on the EuRoC MAV datasets and we also compare it with other competitive methods. Our results demonstrate that the proposed method is more effective than the other competitive methods.

In this paper, we investigate device-to-device (D2D) communication networks which are one of the key technologies for next-generation mobile communication networks and many other applications such as unmanned aerial vehicles (UAVs), vehicle-to-vehicle (V2V), and Internet of things (IoT). The overlay D2D communication networks that are considered in our study use dedicated radio resources separate from what cellular networks use and there exists co-channel interference in D2D networks without cross-channel interference between two networks. We propose a new transmission scheme for overlay D2D networks that uses a dueling deep reinforcement learning (DRL) architecture. The DRL is especially effective in environments where actions do not affect subsequent states as in wireless communication networks. The main contribution of this paper is that the proposed architecture is designed to utilize only information that each D2D devices can easily obtain by measuring channels. The proposed scheme thus enables D2D devices to train their own neural networks and to decide autonomously whether to transmit data without any intervention from infrastructures. The performance of the proposed scheme is analyzed in terms of average sum-rates and is compared to three baseline schemes. Simulation results show that the proposed scheme can achieve almost optimal sum-rates with low signal-to-noise (SNR) values without any intervention from infrastructure.

In this paper, we investigate a hybrid multicast/unicast scheme for a multiple-input single-output cache-aided non-orthogonal multiple access (NOMA) vehicular scenario in the face of rapidly fluctuating vehicular wireless channels. Considering a more practical situation, imperfect channel state information is taking into account. In this paper, we formulate an optimization problem to maximize the unicast sum rate under the constraints of the peak power, the peak backhaul, the minimum unicast rate, and the maximum multicast outage probability. To solve the formulated non-convex problem, a lower bound relaxation method is proposed, which enables a division of the original problem into two convex sub-problems. Computer simulations show that the proposed caching-aided NOMA is superior to the orthogonal multiple access counterpart.

This paper describes the calibration of the Volcano ray-tracing engine against channel measurements collected in an urban environment with a state-of-the-art 28-GHz directional channel sounder. A discrete set of rays, representing planar wavefronts propagating between the transmitter and receiver, were extracted from the measurements and characterized in path gain, delay, and 3D angle-of-arrival through super-resolution techniques, with average errors of only 1.2 dB, 0.55 ns, and 2.05° respectively. The extracted rays were then tracked over space as the receiver, mounted on a mobile rover equipped with military-grade GPS, traversed 66 m while amassing a total of 488 channel acquisitions. The tracked rays were then mapped to rays predicted from ray-tracing, originating through specular reflection or diffuse scattering from ambient objects. The mapping enabled object-specific calibration, namely calibrating distinct diffuse-scattering models for buildings, vehicles, and foliage. To our knowledge, this is the first effort to calibrate ray-tracing with object-specific diffuse scattering models against rays individually mapped in the path gain, delay, angle, and space domains. Results, in terms of calibrated ray-tracing parameters, fit-error statistics, and lessons learned, are included. Our chief finding was that, while most papers on millimeter-wave ray-tracing do not even consider diffuse scattering, it accounted for 20% of the total received power, whereas diffraction accounted for less than 1%.

The technical challenges, e.g. the mobility management and the offloading process, hinder the conventional cellular systems to meet the huge data traffic requirements of the next generation mobile communications. The traditional system (e.g dual-connectivity (DC)) has been proposed to improve the mobility management, however, it will inherit the big trade-off in the offloading process between the energy consumption for the small cell (SC) discovery (SCD) process and the efficiency of utilizing the system resources (e.g. frequency and signaling). In this paper, we present a framework to model the potential offloading opportunities as well as the offloading loss when a typical user equipment (UE) performs the inter-frequency (IRF) scan periodically. The proposed framework also studies the impact of the SCD on the energy efficiency. To improve the system performance and reduce the power consumption at the UEs, a new scheme, trio-connectivity (TC), is proposed in this paper to tackle the aforementioned challenges. The TC includes three planes: control-plane (C-plane), user-plane (U-plane) and indication-plane (I-plane). The I-plane works as an indicator to help the UE to identify and discover the SCs in the system prior to offloading. The role of the I-plane is to keep the SCD on one frequency channel regardless of the number of frequency channels in the system. In the proposed offloading mechanism, some of the energy consumption is transferred from the UE to the network. By using the proposed framework, UE energy efficiency and system energy efficiency as well as the total energy consumption are derived as performance metrics to compare between the TC and the DC. The results show that the TC can outperform the DC in dense cellular systems.

The performance evaluation of wireless systems is crucial for the development of future systems with more connected devices. It is essential to have an easy and relevant method for ensuring the wireless communication performance of the devices. We have recently developed a new random line-of-sight (random-LOS) measurement system for evaluating the communication performance of wireless devices, e.g., transceivers and antennas installed on a vehicle. In the measurement system, a plane wave is generated in the test zone emulating the far-field wave transmitted from a radio base station. In this paper we present both numerical simulations and actual experimental results of the random-LOS over-the-air (OTA) measurement setup operating in the 1.5–3 GHz band. The measurement accuracy is determined by the field variations within the test zone, where a smaller variation gives better measurement accuracy. In this paper the achieved accuracy expressed in terms of standard deviation (STD) was evaluated to be approximately 1 dB of the power within a cylindrical test zone of height 0.4 m and diameter 2 m. The active multiple-input multiple-output (MIMO) performance of antenna systems installed on an actual vehicle was measured and evaluated using the presented setup. A comparison to a theoretical zero forcing (ZF) receiver is also presented.

This paper studies the uplink (UL) transmission of a generalized cell-free massive multiple-input multiple-output (massive MIMO) system in which multiple base stations (or access points), each equipped with a multiple-antenna array and connected to a central processing unit (CPU) over a backhaul network, simultaneously serve multiple users in a cell-free service area. The paper focuses on the non-orthogonal multiple access (NOMA) approach for sharing pilot sequences among users. Unlike the conventional cell-free massive MIMO-NOMA systems in which the UL signals from different access points are equally combined over the backhaul network, this paper first develops an optimal backhaul combining (OBC) method to maximize the UL signal-to-interference-plus-noise ratio (SINR). It is shown that, by using OBC, the correlated interference can be effectively mitigated if the number of users assigned to each pilot sequence is less than or equal to the number of base stations (BSs). As a result, the cell-free massive MIMO-NOMA system with OBC can enjoy unlimited performance when the number of antennas at each BS tends to infinity. A closed-form SINR expression is derived under Rayleigh fading and used to formulate a max-min quality-of-service (QoS) power control problem to further enhance the system performance. To deal with the NP-hardness of the concerned optimization problem, a successive inner approximation technique is applied to convert the original problem into a series of convex optimizations, which can be solved iteratively. In addition, a user grouping algorithm is also developed and shown to be better than random user grouping and a grouping method recently proposed in the literature. Numerical results are presented to corroborate the analysis and demonstrate the superiority of the proposed optimal backhaul combining over both equal-gain backhaul combining and zero-forcing backhaul combining.

This paper investigates a heterogeneous cellular network (HCN) topology, where multiple sub-6 GHz bands are available for access and device-to-device (D2D) links with the full-duplex (FD) mode, and an integrated millimeter-wave (mm-wave) band is added to the D2D links’ sub-channel resource options. D2D links share spectrum resources of cellular users or dedicated mm-wave sub-channel. Densely deployed small cells scenario provides an ideal platform to implement mm-wave, FD and D2D communications. With large bandwidths available, mm-wave has the potential to meet the expected extreme data rate demands. Leveraging highly directional beamforming, severe transmission loss and the blockage phenomenon of mm-wave can be alleviated. Theoretically, FD communications can double the spectral efficiency due to bi-directional communications with the same spectral and temporal resources. However, such benefit comes at the cost of residual self-interference (RSI). Multi-user interference (MUI) and RSI among network uplink, downlink and D2D links are the main challenges. In the paper, we formulate an optimization problem for FD access and D2D links sub-channel allocation underlaying HCNs combining sub-6 GHz and mm-wave bands to make the system transmission rate maximized. Then, a scheme based on the coalition formation game is proposed to deal with this challenging NP-complete optimization. Finally, performance evaluation investigates the suitable conditions for FD and HD operations. Under different network parameter settings, we quantify associated performance of proposed scheme.

Vehicular ad hoc networks (VANETs) are wireless self-organizing networks, whose communication is conducted through open wireless channels, thus making it vulnerable to various attacks. It is therefore necessary to employ encryption technology to ensure secure communication. In view of the foregoing, key agreement techniques are introduced to VANET communications. The group key agreement (GKA) protocol allows a group of participants to establish a public session key for a secure communication channel over an insecure network. The traditional GKA protocol is inefficient, however, and consumes considerable amounts of resources. With the aim of resolving problems related to information security and privacy preservation in VANETs, this paper proposes a full session key agreement scheme based on chaos mapping. In this paper, the first phase describes the key agreement between the fog server and group administrator; the second phase describes the group key agreement for vehicle nodes in the group where the vehicle can only communicate with vehicles having the same group key without the aid of a trusted key center or centralized key distributor. The proposed scheme is based on Chebyshev chaotic mapping algorithm, in which a chaotic map is utilized to provide a one-way hash; Chebyshev polynomial is also employed to establish a public multi-party key; it avoids the modular multiplication index or scalar multiplication on the elliptic curve. The proposed scheme does not only satisfy the security requirements in VANETs but also performs detailed analysis.

This paper makes the case that Switched Reluctance Motor (SRM) is an exceptionally attractive technology to respond to the increasing demand in propulsion applications for high-efficiency, high-performance, and low-cost motors with a more secure supply chain. The paper also presents methods to effectively reduce torque ripple and acoustic noise, which have been the major issues impeding the widespread use of SRMs. Finally, SRM designs for two propulsion motors, one for an e-bike and the other for a hybrid electric vehicle application, are presented which have been proposed to replace permanent magnet machines.

In this paper, we study the underlay relay-aided Device-to-Device (D2D) communications to improve the physical layer (PHY) security of the cellular network. We propose a cooperative scheme, whereby the D2D pair, in return of being allowed to share the spectrum band of the cellular network, serves as a friendly jammer, through its multiple-input multiple-output relay, to degrade the wiretapped signal at an eavesdropper. This paper aims to show that spectrum sharing is advantageous for both D2D communications and cellular networks with respect to reliability and robustness for the former, and the physical layer security enhancement for the latter. To assess the proposed cooperative system model, closed-form expressions for the D2D outage probability, the secrecy outage probability, and the probability of non-zero secrecy capacity are derived. More importantly, the benefits due to the cooperation scheme are verified through numerical and simulation results.

In the past several years, unmanned aerial vehicle (UAV) have been employed to provide enhanced coverage or relay service to mobile users in a scenario with limited or even no infrastructure since they can be deployed to almost everywhere and can be manipulated at anytime. This paper studies UAV as aerial base station (BS) enabled wireless communication system, where a UAV is dispatched to provide wireless communication service to a set of ground users with difference quality-of-experience (QoE) requirements. In real world, user requirements are randomly and unevenly distributed. In addition, UAV communication coverage and on-board energy are limited and system resources are also limited (e.g., transmission power, spectrum). In order to meet the QoE of all users with limited system resources and limited UAV energy, we jointly optimize user communication scheduling, UAV trajectory, transmit power and bandwidth allocation to maximize energy-efficiency and satisfy user QoE requirement. The formulated problem is mixes integer non-convex and non-concave so it is difficult to solve. In this paper, we solvevv the problem with two steps as follows. Firstly, we transform the objective function into a tractable form. Secondly, we divide the optimal problem into four sub-optimal problems, and then use a powerful iterative algorithm with the Dinkelbach and block coordinate descent to solve the optimal problem. That is to say, the user scheduling, UAV trajectory, transmission power and bandwidth allocation are alternately optimized in each iteration. Extensive simulation results present that our proposed method can obtain higher energy efficiency than that of baselines. Specifically, the energy efficiency obtained by our proposed method is 12.5% higher than the approach that only maximizes throughput.

Automatic modulation classification (AMC) plays an important role in both civilian and military applications. Today, increasingly more researchers apply a deep learning framework in AMC. However, few papers take into account that a typical deep model is difficult to deploy on resource constrained devices. In this paper, we propose a new filter-level pruning technique based on activation maximization (AM) that omits the less important convolutional filter. Compared to other network pruning techniques, the convolutional neural network pruned via the AM method achieves equal or higher classification accuracy in the RadioML2016.10a dataset.

This paper proposes cloudlet-based intelligent agents for energy crowdsourcing from autonomous electric vehicles (AEVs). Existing energy crowdsourcing mechanisms focus on load shedding and cost savings, but lack incentive models for strategically behaving agents. In the proposed model, crowdsourcing agents residing on the edge network communicate with AEVs to stimulate them to participate in providing energy to the grid during peak time periods. The focus of this paper is on the development of incentive mechanisms that allow the agents to engage in an auctioning process according to their energy contribution. Through theoretical analysis and simulations, we evaluate the performance of the proposed model with respect to individual rationality, computational efficiency, and truthfulness. This work was supported by the Deanship of Scientific Research at King Saud University, Riyadh, Saudi Arabia, through the Vice Deanship of Scientific Research Chairs: Chair of Smart Cities Technology.

Channel emulation is widely used to control wireless channels and replay scenarios for repeatable experimentation and evaluation. Characterizing the propagation of massive Multiple-Input Multiple-Output (MIMO) systems, is challenging due to the rapid scaling of the number of channels needed to create representative scenarios as well as accurately modeling complex interdepedencies that exist within the channel matrix. Nonetheless, having the ability to emulate massive MIMO channels is critical for the development and testing of next-generation cellular protocols and algorithms. To achieve such scale for massive MIMO channel emulation solutions, in this paper, we study the tradeoff in channel emulation fidelity versus the hardware resources consumed using both analytical modeling and FPGA-based implementations. To reduce the memory footprint of our design, we optimize our channel emulation using an iterative structure to generate geometry-based channels at scale. In addition, the effects of varying the channel update rate and word length selection are evaluated in the paper and can significantly improve the implementation efficiency on an FPGA.

The hybrid energy storage system (HESS), which is composed of battery and ultracapacitor, is established to enhance the performance of an electric vehicle (EV). Moreover, several studies highlight the gains of split the traction power demand among electric motors (EMs) with different characteristics and drivetrain configurations, allowing these to operate at higher efficiency. In this paper, we unite the advantages of the multi EM drive train with the HESS power split and propose a novel two HESS system, in which each HESS is in charge of one of the drivetrain configurations (front and rear system). The EV is driven by two in-wheel EM at the front wheels and a single EM assembled with a differential transmission that moves the rear wheels. In this paper, a multiobjective optimization, based on a genetic algorithm (GA), is formulated to minimize the HESS sizing and maximize the driving range of the vehicle. Also employing the fuzzy control, this strategy is responsible to split the power between the front and rear wheels systems in a more reasonable way to satisfy the demands of better performance. The complete strategy has been developed under the FTP-75 (urban), HWFET (highway) and the US06 (high speed and required acceleration) driving cycles using the MATLAB/Simulink software environment. As compared to a similar EV with a single HESS system, the proposed dual-HESS configuration was able to improve the driving range in 145.15 km also decreasing 23.93% of the HESS mass.

Being an effective non-orthogonal multiple access (NOMA) technique, sparse code multiple access (SCMA) is promising for future wireless communication. Compared with orthogonal techniques, SCMA enjoys higher overloading tolerance and lower complexity because of its sparsity. In this paper, based on deterministic message passing algorithm (DMPA), algorithmic simplifications such as domain changing and probability approximation are applied for SCMA decoding. Early termination, adaptive decoding, and initial noise reduction are also employed for faster convergence and better performance. Numerical results show that the proposed optimizations benefit both decoding complexity and speed. Furthermore, efficient hardware architectures based on folding and retiming are proposed. VLSI implementation is also given in this paper. Comparison with the state-of-the-art have shown the proposed decoder's advantages in both latency and throughput (multi-Gbps).

To improve the maneuverability and stability of a vehicle and fully leverage the advantages of torque vectoring technology in vehicle dynamics control, a finite-time yaw rate and sideslip angle tracking controller is proposed by combining a second-order sliding mode (SOSM) controller with the backstepping method in this paper. However, existing research indicates that first-order sliding mode (FOSM) control suffers from the chattering problem, while the traditional SOSM controller requires knowing the bound of the uncertain term in advance to obtain the switching gain, which is difficult in practice. To address these problems, this paper proposes an adaptive second-order sliding mode (ASOSM) controller based on the backstepping method by adding the high-frequency switching term to the first derivative of the sliding mode variable, which implies that the actual control can be acquired after an integration process. The switching gain in the ASOSM controller is obtained by an adaptive algorithm without knowing any information of the uncertainty. The proposed algorithm is compared with FOSM and SOSM in different scenarios to demonstrate its applicability and robustness. Simulation results show that the bandwidth of the vehicle transient response can be improved by 21%. In addition, ASOSM and SOSM controllers are insensitive to vehicle mass and tire type, implying their robustness to such disturbances. Furthermore, ASOSM requires less control action because of the adaptive law when it performs similarly with SOSM and FOSM.

In this paper we propose a quality of experience (QoE) based joint subcarrier and power allocation (JSPA) technique for orthogonal frequency division multiple access (OFDMA) based downlink video transmission over doubly selective channels (DSCs), where the power allocation is made adaptive to the user velocity. Due to the doubly selective nature of wireless channels, OFDMA based wireless systems are vulnerable to inter-carrier interference (ICI). The proposed speed adaptive power allocation can combat ICI to a great extent compared to the non-adaptive power allocation schemes. Moreover, the proposed JSPA ensures a critical issue of fairness in QoE among users. By making use of space-alternating generalized expectation-maximization maximum a posteriori probability (SAGE-MAP) algorithm, this paper also studies the bit rate performance of the proposed resource allocation algorithm in the presence of perfect and imperfect channel state information (CSI).

High reliability and fault-tolerant operation capability are the application requirements of electric vehicles(EVs). In case of the drive system suffering from inter-turn short-circuit (ITSC) fault, it is necessary to avoid the aggravation of the fault and ensure the normal output performance, this paper proposed a torque ripple minimization control strategy for ITSC fault based on the open-winding (OW) five phase fault tolerant fractional slot concentrated winding interior permanent magnet (FTFSCW-IPM) motor drive system with common DC bus. Once the ITSC fault is detected, the fault phase is active open-circuit (OC) (null the current of the normal part in the fault phase) and connected to a voltage sensor. In this way, the torque ripple minimization control strategy can deal with the OC fault and ITSC fault. For the OC fault, the equal amplitude phase current modulation strategy is adopted based on the carrier-based pulse width modulation (CPWM). For the ITSC fault, this paper proposed a feedforward compensation approach to eliminate the influence of the short-circuit loop current. The novelty of the proposed method is that the compensation can be real-time estimated using the zero-sequence voltage (ZSV) and zero-sequence current (ZSC), even the ITSC level changes. Simulation and experimental results demonstrate the effectiveness and feasibility of the proposed method.

In this paper, a new bidirectional dc-dc converter that is based on the quadratic and switched-capacitor structures is proposed for Electric Vehicle applications. The proposed converter has high voltage conversion ratios, low voltage stress on the semiconductor devices, constant potential difference between the grounds of its low voltage and high voltage ports, and continuous current at its low voltage port. Synchronous rectification is utilized to enhance the efficiency of the converter. In addition, an extended version of the proposed converter is discussed in this paper. Finally, the experimental results obtained from a 2-kW/800-V scaled-down prototype validate the feasibility of the proposed topology and the correctness of its theoretical analysis.

This paper presents an DC/DC topology achieving high efficiency of over 97-% for electric vehicle (EV) fast chargers requiring a very wide output voltage range. The proposed topology consists of an unregulated resonant converter and a non-isolated buck converter. The unregulated resonant converter is responsible for the galvanic isolation for overall system, and the buck converter carries out the charging of the battery inside EV by constant-current or constant-voltage mode control. This structure provides a number of advantages such as easy realization of a very wide voltage range, no circulating-current, zero-voltage-switching operation on all the switches of the unregulated resonant converter running at high frequency and high voltage, and the achievement of high efficiency at every time over the entire battery charging profile. The availability of small passive components is a benefit because it enables the design of high power-density. In this paper, the feasibility of the proposed topology in EV fast charger applications has been verified by a prototype converter developed with the specification of 120-kHz or 50-kHz switching frequencies, maximum power rating of 20-kW, maximum charging current of 30-A, and battery voltage range of 50-650-Vdc.

Independently on the business case addressed, one of the main drawbacks of the railway use cases that need continuous Global Navigation Satellite Systems data is the lack of availability for the 100% of the time of the journey. Additionally, the integrity assessment of the position estimation given is also mandatory for safety critical applications. Thus, tunnels and multipath effects are one of the most challenging situations for the continuous positioning systems. In this context, an autonomous on-board Complementary Positioning System has been proposed to overcome the limitation of Global Navigation Satellite System based positioning systems. This paper proposes a positioning enhancement solution by means of fusing data from the satellite navigation system and inertial measurement units. That hybrid solution provides higher availability and accuracy to the positioning specially on known blocked scenarios, such as tunnels, or urban canyons, by means of a novel environment aware map aided software technique named Known Blocked Scenarios algorithm… This paper describes the Complementary Positioning System and the field test carried out in a challenging environment to validate the enhancement proposed by the authors, which demonstrate the benefits that this system has in known harsh environments for railways.

To overcome the limitations of DSRC with short range, non-supportability of high density networks, unreliable broadcast services, signal congestion and connectivity disruptions, cellular vehicle-to-everything (C-V2X) communication networks, standardized in 3rd Generation Partnership Project (3GPP) Release 14, have been recently introduced to cover broader vehicular communication scenarios including vehicle-to-vehicle (V2V), vehicle-to-pedestrian (V2P) and vehicle-to-infrastructure/network (V2I/N). In C-V2X, vehicles can directly communicate over PC5 based dedicated sidelinks called direct mode or V2V communication. However, high vehicle densities may require reuse of cellular spectrum for V2V. Moreover, infrastructure mode communication through V2I/N links can augment V2V communication by enhancing communication range and reliability for enhanced safety along with consistent performance under traffic congestions. Motivated by the stringent connection reliability, spectral efficiency, and coverage requirements in C-V2X, this paper presents the first comprehensive and tractable analytical framework for performance of C-V2X networks over shared V2V and cellular uplink channels, where the transmitting vehicles can deliver their information via infrastructure or direct mode, based on their distances, propagation environments and the bias factor. By practically modeling the vehicles on the roads using the doubly stochastic Cox process and the base-stations, we derive new association probabilities, new success probabilities of infrastructure and direct mode, and overall success probability of the C-V2X communication over shared channels, which are validated by the simulations results. Our results reveal the benefits of our proposed model (possibility of selecting both direct and infrastructure modes over shared channels) compared to V2V network in terms of success probability., Comment: Paper revised