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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]

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]

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]

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]

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]

As an aerial base station, unmanned aerial vehicle (UAV) has been considered as a promising technology to assist future wireless communications due to its flexible, swift and low cost features, where resource allocation is the basis for ensuring energy-efficient UAV-assisted networks. This paper formulates a joint optimization problem of user association, UAV trajectory design and power control to maximize the channel capacity among all ground users at a limited power level in a downlink transmission. To tackle the mixed-integer non-linear programming problem, this paper proposes a clustering-aided reinforcement learning approach consisting of three consecutive stages. Firstly, modified expectation-maximization unsupervised learning algorithm is investigated to cluster the ground users, which reduces the dimensions and hence, the association complexity is reduced as well. Then, Kuhn-Munkres algorithm is incorporated for user association, which associates a UAV with the ground users via matching to the cluster, and assigns the UAVs to the centroid of the matching cluster for pre-placement, with the aim of speeding up the convergence of the following deep reinforcement learning algorithm. Finally, a multi-agent twin delayed deep deterministic (MATD3) policy gradient is proposed to solve the non-convex sub-problem, which determines the transmit power and designs the fine-tuned trajectory of UAVs. By incorporating low-bias value estimation technique, the reward of the proposed MATD3 algorithm is improved. Simulation results have demonstrated that our proposed approach achieves higher reward as well as converging faster than existing reinforcement algorithms. Besides, the clustering-aided reinforcement learning has lower computational complexity than the benchmark schemes. [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]

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]

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]

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]

This study is motivated by the fact that localization in millimeter wave(mmWave) Vehicle-to-Vehicle communication becomes a more critical problem because both the terminals of the communication link are in motion. The positional awareness merely based on GPS or local sensors has an error margin of around 10 meters, which can worsen in uncertain real-time conditions such as road topology and highway traffic. The paper analyses the relation between vehicle dynamics, beamforming, and beam alignment for highly directive antennas. When the subsystem models presented in this paper are taken into consideration, the joint vehicle dynamics-beamforming approach will optimize the link connectivity and SNR. The vehicle dynamics model is designed to be more realistic considering the non-linear acceleration based on the throttle-brake jerks due to induced noises. The Unscented Kalman Filter based prediction subsystem predicts the alignment values even during non-linear acceleration and jerks. The feasibility of antenna design for the beamforming strategies is supported by the requirements of phase shifters and the number of elements. [ABSTRACT FROM AUTHOR]

This paper presents a non-orthogonal multiple access (NOMA)-assisted full-duplex (FD) cooperative spectrum sharing protocol to achieve full-rate at a cell-edge primary user, where the secondary receiver helps the primary network relay the information to the cell-edge primary user. Unlike other FD cooperative schemes, the FD relay in the proposed system is not transmitting while receiving symbols from the primary transmitter. Hence, the proposed scheme can achieve full-rate at a primary user without degrading its outage performance. Further, it guarantees every alternate time-slots to the secondary network for accessing the licensed spectrum by employing a secondary receiver as a cooperative relay. In this paper, we derive the analytical expression for the outage probability and ergodic capacity. Moreover, we validate the derived expression through simulation and asymptotic results. The numerical and simulation results substantiate that the proposed scheme significantly outperforms other existing protocols. [ABSTRACT FROM AUTHOR]

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]

A new dual-mechanical-port (DMP) electric machine for hybrid electric vehicle applications, particularly in the power-split continuously variable transmission systems, is proposed in this paper. In order to comprehensively and quantitatively evaluate the pros and cons of the proposed machine, a comparative study of four DMP electric machines with different topologies is conducted. These four investigated DMP electric machines include a conventional DMP machine, a DMP machine with spoke-type permanent magnets, a DMP machine with reluctance rotor, and a DMP machine with open slots which is the proposed machine in this paper. Even though these four machines have similar topologies, they have different operating principles, which are demonstrated in detail. The comparison results indicate that the DMP machine with open slots outperforms the others in terms of torque/power density, efficiency, magnet utilization, etc. Accordingly, the DMP machine with open slots is selected for further investigation and optimization. A large-scale multi-objective optimization is carried out for this machine, where the differential evolution algorithm serves as a global search engine to target optimal performance. Finally, an optimal design is prototyped, and the experimental results are performed to verify the effectiveness of the analysis and simulation results in this paper. [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]

This paper presents techniques that are expected to improve the bit error rate (BER) performance of Unmanned Aerial Vehicles (UAVs) communication systems. The decode-and-forward (DF)-based cooperative multi-hop Rician network using space shift keying (SSK) modulation is employed for this purpose. The SSK modulation and cooperative communication are merged and used together. This enhances the BER performance for multiple-input multiple-output (MIMO) channels, thus accentuating its significance as an important technique for consistently reliable communication methodology while, at the same time, eliminating wastage of energy. The new technique is named as high performance (HP) due to not only BER performance improvement but also increase of the diversity of the communication system. This new model is further used for multi-hop networks and the resulting BER characteristics are assessed for possible enhanced performance in Rician channels. The paper also showcases the generalized-n-hop BER equation for various multi-hop DF-based cooperative communication systems in diverse UAV models. The Monte Carlo simulations further confirm the theoretical results that already exist for transmit-receive antennas. This work also underlines the fact that the number of hops in a communication system has no bearing whatsoever on the BER performance diversity value. [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]

Security issue in underwater visible light communication (UVLC) arises mainly due to the scattering effect wherein numerous photons are statistically generated when a light beam strikes a water molecule. This paper considers an underwater communication scenario wherein a floating vehicle (FV) transmitter that is equipped with multiple light-emitting diodes (LEDs) communicates with the two legitimate near-end and far-end underwater vehicles (UVs) in presence of an eavesdropper. In particular, two non-orthogonal multiple access (NOMA) technology-based optimal LED selection (OLS) and suboptimal LED selection (SLS) schemes are proposed to select a LED that can transmit the information with the highest secrecy rate against active/passive eavesdropping attacks. Furthermore, the FVT transmits the information to both UVs with the selected LED only. Utilizing the successive interference cancellation (SIC) characteristic, this paper derives the closed-form secrecy outage probability expressions for both single-LED and multi-LED transmission strategies for both known and unknown CSI. The security performance of the proposed multi-LED NOMA-UVLC is compared with the conventional single-LED NOMA-UVLC under the effects of air bubbles for both fresh and salty water types. In addition, the validity of the numerical results is verified through Monte-carlo simulation analysis. [ABSTRACT FROM AUTHOR]

This paper explores the feasibility and scope of co-channel coexistence between a low data rate Orthogonal Frequency Division Multiplexing (OFDM) secondary network and a digital television (DTV) broadcast network operating in the same area. Since DTV receivers do not transmit, neither the presence nor the impact of interference from the secondary network on these DTV receivers can be known. Consequently, conventional spectrum sensing techniques cannot be employed effectively. This paper proposes to compensate the lack of feedback by combining the available information about population distribution in the region - ranging from coarse population density estimates to accurate geospatial maps - with better estimation of interference using a data-driven technique called Ordinary Kriging. Radio Environment Maps constructed using the Kriging method from field-reported observations from the secondary nodes are used to obtain better accuracy than the conventional statistical models. The estimates are then used to compute communication parameters for each secondary link in terms of power, number of active subcarriers, and active time slots, while keeping the average interference in the DTV network within threshold bounds. Thus, the viability of such co-channel license-exempt operation of secondary networks in the DTV black and grey spaces for low data rate applications in densely populated urban areas and higher data rate capabilities for sparsely populated rural areas is established. [ABSTRACT FROM AUTHOR]

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]

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]

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]

Fixed Satellite Services (FSS) used to be alone at the C-Band spectrum in most countries. Since the deployment of 5G in many countries (i.e., 3.3–3.6 GHz), FSS is not the exclusive system in the C-Band anymore. In order to minimize the detrimental interference for the FSS to allowable levels, regional exclusion zones of maximum radiated power in 5G base stations (BS) are proposed and evaluated. In this paper, a measurement campaign has been carried out, and an analysis of the interference has been studied. A filtering model, namely Filter to Remove Broadband Interference 5G (FIREBRING), is proposed and analyzed concerning the carrier-to-noise ratio (C/N). Moreover, this paper focuses on the evaluation of the 5G interference into the FSS. The proposed solution deployed an Low-Noise Block (LNB) with a band frequency of 3.7 to 4.2 GHz to test the satellite down-conversion signal at the receiver. The paper offered a complete analysis of the 5G signal, taking into account the implications of out-of-band emissions, potentially LNB saturation into FSS receiver, and the repercussions of the deployment of the 5G BS active antenna systems. With the LNB and down-converter in place, it can be found that the signal interference between 1.450 GHz and 1.550 GHz, is nearly 18 dB. [ABSTRACT FROM AUTHOR]

This paper presents a decomposed co-design optimization framework for an electric vehicle powertrain equipped with a Continuously Variable Transmission (CVT). We jointly design the sizes of the powertrain components and the energy management strategy, considering the design and the dynamics of the transmission. Specifically, we divide the resulting nonlinear optimization problem into separate hierarchical optimization subproblems leveraging the concept of Analytical Target Cascading (ATC). Furthermore, we compare the proposed decomposition-based strategy with a centralized approach, in which we solve the same problem, and show that the method presented in this paper can be a potential alternative to a centralized optimization formulation for solving the large-scale, complex integrated plant and control system design. Finally, we compare the resulting performance of using a CVT on the vehicle total-cost-of-ownership with that equipped with a Fixed-Gear Transmission (FGT), which is what is commonly implemented in current electric vehicles. Our results show that by jointly optimizing the design of the transmission along with the control strategy, the CVT yields a lower energy consumption and total-cost-of-ownership compared to that of the FGT. [ABSTRACT FROM AUTHOR]

Sparse code multiple access (SCMA), as a codebook-based non-orthogonal multiple access (NOMA) technique, has received research attention in recent years. The codebook design problem for SCMA has also been studied to some extent since codebook choices are highly related to the system’s error rate performance. In this paper, we approach the SCMA codebook design problem by formulating an optimization problem to maximize the minimum Euclidean distance (MED) of superimposed codewords under power constraints. While SCMA codebooks with a larger MED are expected to obtain a better BER performance, no optimal SCMA codebook in terms of MED maximization, to the authors’ best knowledge, has been reported in the SCMA literature yet. In this paper, a new iterative algorithm based on alternating maximization with exact penalty is proposed for the MED maximization problem. The proposed algorithm, when supplied with appropriate initial points and parameters, achieves a set of codebooks of all users whose MED is larger than any previously reported results. A Lagrange dual problem is derived which provides an upper bound of MED of any set of codebooks. Even though there is still a nonzero gap between the achieved MED and the upper bound given by the dual problem, simulation results demonstrate clear advantages in error rate performances of the proposed set of codebooks over all existing ones not only in AWGN channels but also in some downlink scenarios that fit in 5G/NR applications, making it a good codebook candidate thereof. The proposed set of SCMA codebooks, however, are not shown to outperform existing ones in uplink channels or in the case where non-consecutive OFDMA subcarriers are used. The correctness and accuracy of error curves in the simulation results are further confirmed by the coincidences with the theoretical upper bounds of error rates derived for any given set of codebooks. [ABSTRACT FROM AUTHOR]

With the development of new radio (NR) technologies, sidelink (SL) transmission plays a more vital role in supporting low latency, higher reliability vehicle-to-everything (V2X) communications. Compared with the Long-Term Evolution (LTE), NR V2X aims to support enhanced V2X use cases with more stringent latency and reliability requirements. 3GPP introduces NR V2X distributed resource allocation to fulfill the low latency requirement to reduce the delay caused by requesting transmission resources from gNB. In distributed mode, UEs autonomously select resources without the scheduling from gNB. NR V2X introduces the physical sidelink feedback channel (PSFCH) to enhance transmission reliability. This paper presents the resource allocation mechanism for NR V2X distributed mode. Since the SL feedback channel is a new feature different from LTE V2X, we discuss the potential enhancements on resource allocation utilizing SL feedback channel to improve the reliability. This paper proposes a Receiver Grant-based resource allocation, which allows the receiver to send the grant with the feedback channel. By allowing the transmitter to indicate the pre-selected resources before the end of the previous reservation, the receiver can inform the transmitter about the collided resource selection through the PSFCH. The analytical models for NR V2X distributed mode with the feedback channel are also provided for UE to estimate the collision ratio. The simulation results and the analytical model show that the proposed Receiver (Rx) Grant-based resource allocation improves transmission reliability in terms of collision ratio. For future advanced V2X use cases, the analytical models provide an evaluation method and help UE make decisions to meet the reliability requirements. [ABSTRACT FROM AUTHOR]

A challenge for rescue teams when fighting against wildfire in remote areas is the lack of information, such as the size and images of fire areas. As such, live streaming from Unmanned Aerial Vehicles (UAVs), capturing videos of dynamic fire areas, is crucial for firefighter commanders in any location to monitor the fire situation with quick response. The 5G network is a promising wireless technology to support such scenarios. In this paper, we consider a UAV-to-UAV (U2U) communication scenario, where a UAV at a high altitude acts as a mobile base station (UAV-BS) to stream videos from other flying UAV-users (UAV-UEs) through the uplink. Due to the mobility of the UAV-BS and UAV-UEs, it is important to determine the optimal movements and transmission powers for UAV-BSs and UAV-UEs in real-time, so as to maximize the data rate of video transmission with smoothness and low latency, while mitigating the interference according to the dynamics in fire areas and wireless channel conditions. In this paper, we co-design the video resolution, the movement, and the power control of UAV-BS and UAV-UEs to maximize the Quality of Experience (QoE) of real-time video streaming. We applied the Deep Q-Network (DQN) and Actor-Critic (AC) to maximize the QoE of video transmission from all UAV-UEs to a single UAV-BS to learn the dynamic fire areas and communication environment. Simulation results show the effectiveness of our proposed algorithm in terms of the QoE, delay and video smoothness compared to the Greedy algorithm. [ABSTRACT FROM AUTHOR]

Mechanical elastic wheel (MEW) has the advantage of explosion-proof, which helps to guarantee the safety and maneuverability of the automobile. Aiming to improve the stability of vehicles matching with MEW, this paper proposes an integrated control strategy that combines the active front steering and the active suspension systems. The integrated controller consists of feed-forward and feedback controllers. Firstly, the differential flatness system of the vehicle is constructed concerning the coupling among yaw, lateral, and roll motion. And the flatness based feed-forward control inputs of the desired front wheel angle and active roll moment are derived. At the same time, the backstepping-adaptive feedback controllers for yaw rate and roll angle are designed. Then, the feed-forward and feedback controllers are coordinated and their weight coefficients are adjusted according to the sideslip angle. Furthermore, this paper also adopts a nonlinear disturbance observer to estimate the vertical load of MEW. The influence of the vertical load is fully considered when estimating the equivalent cornering stiffness of each MEW, so as to realize the online adjustment of the controller parameters. Finally, the maneuver tests of step input steering and double lane change are simulated, respectively. The results show that the proposed control strategy yields a better control effect than the fuzzy-PID controller, and the performance of the vehicle matching with MEW has been significantly improved. [ABSTRACT FROM AUTHOR]

Ultra-reliable low-latency communication (uRLLC) emerges as one of the three service categories in fifth-generation (5G) wireless networks, with a potential to support multiple advanced services for latency-sensitive communications. The inherent openness of the wireless environment makes security in 5G unprecedentedly urgent to be addressed. Accordingly, the physical layer security (PLS) technique exploits the randomness characteristics of the wireless channel to fulfill security and confidentiality of information transmission. Different from many current works, which ignored the significance of security in uRLLC, this paper studies secrecy improvement strategy design in uRLLC scenarios. First, we propose suitable performance metrics for evaluating PLS of uRLLC and make a compromise between reliability, latency, and security, while maximizing the network throughput. Next, we identify the key challenging issues for achieving PLS in the context of uRLLC, and discuss the role of cooperative communication and mobility in terms of possible solutions. To the best of our knowledge, this is the first paper to make a trade-off among reliability, security and network throughput. Simulations validate the theoretical analyses. [ABSTRACT FROM AUTHOR]

A zero forcing (ZF) assisted single layer beamforming is developed for spatial modulation (SM) and space shift keying (SSK) modulation multiple input multiple output (MIMO) systems, termed as SZF-SM/SZF-SSK, to achieve both transmit beamforming and receive diversity gain in this paper. Specifically, the proposed SZF-SM/SZF-SSK maps part of the bits to spatial domain, simultaneously forming a ZF precoding assisted single layer data both in multiple input single output (MISO) and MIMO systems, that differs greatly from conventional ZF beamforming assisted MIMO systems. With MISO configuration, the ZF precoder of SZF-SM/SZF-SSK converges to the maximum ratio transmission (MRT), achieving only the transmit beamforming gain. With MIMO configuration, SZF-SM/SZF-SSK tranceiver could be further modified along with the MIMO channel to achieve single layer transmission and joint detection, attaining both the transmit beamforming and receive diversity gain in SM-MIMO system. The theoretical analysis and the simulation results of the proposed SZF-SM/SZF-SSK with MISO and MIMO configurations are presented in this paper respectively, which show an attractive performance gain over the conventional SM/SSK. Compared with the conventional ZF beamforming assisted MIMO systems and some other CSI assisted SM-MIMO schemes, the proposed scheme exhibits better performance, especially under highly correlated MIMO channels, rendering a new and practical way to achieve low rank MIMO transmission with good performance and high data rate. [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]

Indoor location-based services have become more and more important due to their potential applications in a wide range of personalized services in recent years. The accuracy of smartphone based 3D indoor localization is subjected to the poor performance of low-cost sensors and limited coverage of location sources. In order to solve these problems, this paper proposes a precise 3D indoor localization and trajectory optimization framework that uses the combination of sparse Wi-Fi Fine Time Measurement (FTM) anchors and built-in sensors (3D-LOWS). The inertial navigation system (INS) mechanization, multi-level constraints and observed values are integrated by the adaptive unscented Kalman filter to eliminate effects of cumulative error, indoor magnetic interference, and diversity of handheld modes. The Wi-Fi based ranging and landmark detection information is used to provide an accurate absolute reference to the built-in sensors based method. In addition, this paper proposes and evaluates two different trajectory optimization algorithms and compares the improved localization performance. The comprehensive experiments indicate that the proposed 3D-LOWS is proved to achieve accurate and stable 3D indoor positioning and trajectory optimization performance under complex indoor environments using sparse wireless stations. [ABSTRACT FROM AUTHOR]

Robust and adaptive vehicle state estimation and tracking algorithms have become a very important part within the autonomous driving field. The family of interacting multiple model (IMM) filters has shown to provide very effective and accurate state estimation in systems whose behavior patterns change significantly over time. The main reason for the improved performance of IMM filters compared to single model approaches is the mode mixing, which constantly aligns the different models. This paper proposes an innovative way for the mode mixing, when the state-vectors of the models are of different size. The proposed mixing approach consists of two parts: firstly mixing the common states and secondly weighting between original and mixed states based on the model probabilities. Results from artificial simulations and real world measurement setups are shown to demonstrate the validity of the approach. Compared to previously suggested solutions, the proposed approach is more general and the overall complexity of the mode mixing step is reduced, which is the main contribution of the presented paper. [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]

The pantograph-catenary system is used in modern electrified railways to power electric trains. Common sense is that the increase of train speed leads to a deterioration of the interaction performance. But recent studies indicate that a higher speed may result in a better performance in some local speed regions. This paper is the first attempt to provide a theoretical explanation for this phenomenon. A validated pantograph-catenary model is built to reproduce this phenomenon. Referring the definition of critical speed in moving load problems, the catenary's critical speed is defined. The wavelength components can be extracted through the Fourier transformation to the contact wire's vertical mode shape. The speed that satisfies the resonance condition can be regarded as the potential critical speed. The corresponding contact wire amplitude is further evaluated to determine the critical speeds at a given mode. The analysis indicates that a good interaction performance happens when the speed falls in the transition procedure between the critical two speeds. Based on this idea, a simple indicator that describes the overall contribution of all modes subjected to a moving pantograph at a given speed is proposed to facilitate the determination of the local optimal speed. The variation of the proposed indicator with respect to the speed shows good consistency with the trend of the simulation results. The main finding in this paper can be used in the design phase to determine the optimal speed of the pantograph-catenary system in a local speed range. [ABSTRACT FROM AUTHOR]

In this paper, we design receivers for filter bank multicarrier-based (FBMC-based) massive MIMO considering practical aspects such as channel estimation and equalization. In particular, we propose a spectrally efficient pilot structure and a channel estimation technique in the uplink to jointly estimate all the users’ channel impulse responses. We mathematically analyze our proposed channel estimator and find the statistics of the channel estimation errors. These statistics are incorporated into our proposed equalizers to deal with the imperfect channel state information (CSI) effect. We revisit the channel equalization problem for FBMC-based massive MIMO, address the shortcomings of the existing equalizers in the literature, and make them more applicable to practical scenarios. The proposed receiver in this paper consists of two stages. In the first stage, a linear combining of the received signals at the base station (BS) antennas provides a coarse channel equalization and removes any multiuser interference. In the second stage, a per subcarrier fractionally spaced equalizer (FSE) takes care of any residual distortion of the channel for the user of interest. We propose an FSE design based on the equivalent channel at the linear combiner output. This enables the applicability of our proposed technique to small and/or distributed antenna setups such as cell-free massive MIMO. Finally, the efficacy of the proposed techniques is corroborated through numerical analysis. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have attracted enormous attention in recent years due to their potentials in mitigating energy crisis and air pollutions. However, the long battery charging time and lack of sufficient charging infrastructure highly restrict the popularization of EVs. In this context, it is promising to establish battery charging and swapping systems (BCSSs) based on the concept of battery swapping services. To optimally achieve the combined operation of BCSSs, this paper proposes a hybrid swapped battery charging and logistics dispatch model in continuous time domain. Identifying the special structure of the mathematical models of the two problems, this paper innovatively formulated the swapped battery charging strategy as the rectangle packing problem and the battery logistics model as the vehicle routing problem. The two models are closely linked by the delivery time of transporting the well-charged batteries from battery charging stations to battery swapping stations. A hybrid optimal operation model of BCSSs is further formulated as a mixed-integer linear programming model by incorporating the interaction between the battery charging and battery logistics. Finally, case studies are conducted on several BCSSs and numerical results validate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

In this study, we investigate the operation of a user-number threshold-based base station (BS) on/off control, in which the BS turns off when the number of users is less than a specific threshold value. This paper presents a space-based analysis of the BS on/off control system to which a stochastic geometric approach is applied. In particular, we derive the approximated closed-form expression of the coverage probability of a homogeneous network (HomNet) with the user-number threshold-based BS on/off control. Moreover, the optimal user-number threshold for maximizing coverage probability is analytically derived. In addition to HomNet, we also derive the approximated closed-form expression of the coverage probability and the optimal user-number thresholds for a heterogeneous network (HetNet). The results show that HetNet, the analysis of which seems intractable, can be analyzed in the form of a linear combination of HomNets with weighted densities. In addition, the optimal user-number threshold of each tier is obtained independently from other tiers. The modeling and analysis presented in this paper are not only limited to the case of user-number threshold-based on/off control, but also applicable to other novel on/off controls with minor modifications. Finally, by comparing the analytical results with the simulated results, the theoretical contributions of this study are validated. [ABSTRACT FROM AUTHOR]

The performance of the recently proposed orientational beamforming (OBF) system for ultra-wideband (UWB) signals degrades seriously in multiuser situation. Although various techniques have been proposed in the literature to suppress multiuser interference in the time-hopping UWB system, there has been no research on interference suppression in the OBF UWB system. In this paper, we propose a nonlinear OBF system via a modified radial basis function (RBF) neural network, named the RBF-OBF system, to reject orientational UWB interferences caused by multiuser transmission in the OBF system. The RBF neural network is selected mainly because of its simple structure, convenient training process, and fast convergence speed. However, the conventional Euclidean-distance-based RBF neural network is not suitable for the training process used in this work, as the training samples are noiseless orientational steering vectors in frequency domain. Therefore, a modified RBF neural network is proposed in this paper, which evaluates the similarity between an input vector and the center vector of a hidden layer neuron by their signed complex correlation coefficient. Numerous simulations demonstrate that compared to the conventional OBF system, the proposed RBF-OBF system can significantly reduce the bit error rates (BERs) under different noise and interference situations. The BER reduction rate of the proposed RBF-OBF system is higher than 92% in additive white Gaussian noise channel and higher than 89% in line-of-sight multipath channel. [ABSTRACT FROM AUTHOR]

This paper investigates the fundamentals of motion planning for minimizing motion sickness in transportation systems of higher automation levels. The optimum velocity profile is sought for a predefined road path from a specific starting point to a final one within specific and given boundaries and constraints in order to minimize the motion sickness and the journey time. An empirical approach based on British standard is used to evaluate motion sickness. The trade-off between minimizing motion sickness and journey time is investigated through multi-objective optimization by altering the weighting factors. The correlation between sickness and journey time is represented as a Pareto front because of their conflicting relation. The compromise between the two components is quantified along the curve, while the severity of the sickness is determined using frequency analysis. In addition, three case studies are developed to investigate the effect of driving style, vehicle speed, and road width, which can be considered among the main factors affecting motion sickness. According to the results, the driving style has higher impact on both motion sickness and journey time compared to the vehicle speed and the road width. The benefit of higher vehicle speed gives shorter journey time while maintaining relatively lower illness rating compared with lower vehicle speed. The effect of the road width is negligible on both sickness and journey time when travelling on a longer road. The results pave the path for the development of vehicular technologies to implement for real-world driving from the outcomes of this paper. [ABSTRACT FROM AUTHOR]

This paper explores the near-field regional target localization problem with the reconfigurable intelligent surface (RIS) assisted system. Traditional near-field localization strategies typically estimate the position of the target through line-of-sight (LOS) signals transmitted from the anchor node. In practice, accurate location estimation is a challenging issue when the LOS link between the anchor node and target may be unavailable due to the obstacle. To this end, this paper investigates the possibility to confirm the position of the target node consisted in an area of interest (AOI) by retrieving information from the RIS reflection signals. Specifically, we establish a general framework for RIS-assisted regional localization, which consists of RIS phase design and position determination. By defining the average localization accuracy (ALA) of the AOI, we first present a discretization method to design the RIS phase. Then, a robust phase design problem is formulated via transforming the AOI into an uncertainty model of position parameters, and an efficient iterative entropy regularization (IER)-based algorithm is proposed to solve it. Using the designed RIS phase, we develop a near-field target localization algorithm and discuss the power optimization problem for the RIS-assisted localization system (RALS). Numerical results demonstrate the effectiveness of the proposed framework, in which both phase design strategies almost coincide with the optimal RIS phase, and the proposed localization method can attain the near-optimal localization performance by applying the designed RIS phase schemes. [ABSTRACT FROM AUTHOR]

This paper proposes a deep reinforcement learning-based video streaming scheme for mobility-aware vehicular networks, e.g., vehicles on the highway. We consider infrastructure-assisted and mmWave-based scenarios in which the macro base station (MBS) cannot directly provide the streaming service to vehicles due to the short range of mmWave beams so that small mmWave base stations (mBSs) along the road deliver the desired videos to users. Motivated by the fact that a video stream consists of sequential chunks and smooth streaming has to be provided while users pass through multiple mBSs at high speed, the MBS proactively pushes some video chunks of the desired contents to mBSs. This is done to support vehicles that are currently covered and/or will be by each mBS. We formulate the dynamic video delivery scheme that adaptively determines 1) which content, 2) what quality and 3) how many chunks to be proactively delivered from the MBS to mBSs using Markov decision process (MDP). Since it is difficult for the MBS to track all the channel conditions and the network states have extensive dimensions, we adopt the deep deterministic policy gradient (DDPG) algorithm for the DRL-based video delivery scheme. This paper finally shows that the DRL agentlearns a streaming policy that pursues high average quality while limiting packet drops, avoiding playback stalls, reducing quality fluctuations and saving backhaul usage. [ABSTRACT FROM AUTHOR]

In nonsaturated 802.11 distributed coordination function (DCF) networks, existing studies had observed a salient phenomenon: the maximum stable throughput may be far higher than the saturation throughput and last for a long duration (say, months). Such a maximum stable throughput is called the presaturation throughput peak. The long-duration peak suggests that 802.11 DCF networks may provide a far higher stable throughput than what we generally believe, without worrying about a sudden deterioration of quality of service (QoS). This paper is devoted to developing a general theoretical framework to predict the peak. In the framework, we define a backoff entropy to quantify the uncertainty of the inherent random backoff process of 802.11 DCF networks, and then innovatively introduce a BackoffEntropy-Peak linearity property (i.e., the peak is linearly proportional to the backoff entropy) for the peak prediction. This framework is applicable for a general configuration of contention windows (CWs). It enables us to reveal the most essential dependency of the peak on the initial CW size and the number of network nodes. Further, we design peak-based call admission control (CAC) schemes that can be performed quickly and easily, without requiring network performance measurements and complex calculations. We verify our framework and CAC schemes via widely adopted 802.11 DCF ns2 and ns3 simulators (mostly with typical 802.11 standard settings). Extensive simulations show that our framework and CAC schemes trade within 9% peak prediction errors for making 802.11 DCF networks provide high stable throughput with good QoS (such as short delay and low collision probability). This paper is the first to propose applying the information entropy to study the throughput-stability problem. We believe that this study makes a crucial breakthrough in thoroughly investigating nonsaturated performance and network stability of random access wireless networks. [ABSTRACT FROM AUTHOR]