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The present paper is dedicated to the investigation of a predictive Equivalent Consumption Minimization Strategy. The objective is to determine the torque split between the internal combustion engine and the electric machine of a hybrid vehicle. The energy management is formulated as a receding optimization problem. To avoid a complex prediction of the vehicle speed and acceleration over time, the slow dynamic of their distribution is exploited. A rational tuning of the algorithm parameters is proposed as well as some improved implementations. The number of individual operations (additions, multiplications, interpolations, etc) required per seconds is discussed. Finally, the energy management algorithm energy consumption are assessed over different driving cycles, including one with a \boldsymbol 15406  ${km}$ length obtained using GPS measurements. A comparison with an adaptive Equivalent Consumption Minimization Strategy is provided. The predictive Equivalent Consumption Minimization Strategy allows controlling the state of charge close to a (possibly time varying) set point while providing low fuel consumption. [ABSTRACT FROM AUTHOR]

This paper studies a novel design of user pairing and resource allocation in a simultaneous wireless information and power transfer (SWIPT)-enabled cooperative non-orthogonal multiple access (NOMA) system with coexisting power-splitting (PS) and time-switching (TS) users. In this system, near NOMA users (NUs) that are close to a source can assist the communications between the source and far NOMA users (FUs) using the energy harvested by either PS or TS strategy. The design aims to maximize the sum-rate of the system by optimizing user pairing and power allocation between NUs and FUs as well as performing PS and TS control at NUs, while guaranteeing the target data rates and the minimum harvested energy requirements of users. Since the joint design is formulated as a non-convex mixed-integer non-linear programming (MINLP) problem that is challenging to tackle, we propose a two-step user pairing and resource allocation algorithm. Specifically, we decouple the original problem into an inner resource allocation problem and an outer user pairing problem. For the inner problem, we propose successive convex approximation (SCA) and block coordinate descent (BCD)-based algorithms to perform power allocation as well as PS and TS control iteratively, with the algorithms’ convergence being theoretically proved. By exploiting the characteristics of the outer problem of user pairing, this problem is well tackled by the Hungarian algorithm. Simulation results demonstrate the proposed joint design yields better performance gains than the existing schemes. [ABSTRACT FROM AUTHOR]

This paper proposes a novel secure channel estimation technique to provide security against leakage of the channel estimates to any malicious user by utilizing artificial noise (AN) along with full-duplex (FD) transmissions. AN overcomes the drawback of FD transmission, where any strategically located eavesdropper can minimize the interference signal received from the FD receiver. The proposed secure channel estimation technique comprises three stages, where the first stage is responsible for the estimation of the residual self-interference (SI) channel. The second stage acquires rough channel estimates to design AN orthogonal to the channel between legitimate transmitter-receiver for the next training stage. In the third stage, both legitimate nodes transmit orthogonal AN signals along with the known training signals using FD transmissions. For power allocation, we have presented a novel local adaptive power allocation algorithm at each legitimate node to allocate the powers to the training signals, and AN signals while ensuring equivocation at the eavesdropper. We provide the mean square error (MSE) to indicate the performance achieved by the respective nodes. We have also provided the bit error rate (BER) simulation analysis to indicate the secure communication achieved by securing the channel estimation process. The presented simulation analysis indicates that the eavesdropper is unable to decode the transmitted information while the legitimate receiver has robustly decoded the transmitted information. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) or drone, is an evolving technology in today's market with an enormous number of applications. Mini UAVs are developed in order to compensate the performance constraints imposed by larger UAVs during emergency situations. Multiple mini autonomous UAVs require communication and coordination for ubiquitous coverage and relaying during deployment. Multi-UAV coordination or swarm optimization is required for reliable connectivity among UAVs, due to its high mobility and dynamic topology. In this paper, a Secured UAV (S-UAV) model is proposed which takes the location of the UAVs as inputs to form a Wireless Mesh Network (WMN) among multiple drones with the help of a centralized controller. After WMN formation, efficient communication takes place using A* search, an intelligent algorithm that finds the shortest communication path among UAVs. Further, the S-UAV model utilizes cryptographic techniques such as Advanced Encryption Standard (AES) and Blowfish to overcome the security attacks efficiently. Simulation results show that the S-UAV model offers higher throughput, reduced power consumption and guaranteed message transmission with reduced encryption and decryption time. [ABSTRACT FROM AUTHOR]

In this paper, we consider the downlink (DL) of a zero-forcing (ZF) precoded extra-large scale massive MIMO (XL-MIMO) system. The base-station (BS) operates with limited number of radio-frequency (RF) transceivers due to high cost, power consumption and interconnection bandwidth associated to the fully digital implementation. The BS, which is implemented with a subarray switching architecture, selects groups of active antennas inside each subarray to transmit the DL signal. This work proposes efficient resource allocation (RA) procedures to perform joint antenna selection (AS) and power allocation (PA) to maximize the DL spectral efficiency (SE) of an XL-MIMO system operating under different loading settings. Two metaheuristic RA procedures based on the genetic algorithm (GA) are assessed and compared in terms of performance, coordination data size and computational complexity. One algorithm is based on a quasi-distributed methodology while the other is based on the conventional centralized processing. Numerical results demonstrate that the quasi-distributed GA-based procedure results in a suitable trade-off between performance, complexity and exchanged coordination data. At the same time, it outperforms the centralized procedures with appropriate system operation settings. [ABSTRACT FROM AUTHOR]

The energy detection technology is recommended in the licensed assisted access (LAA) scheme by 3GPP because of its simplicity and low cost. However, due to its inherent limitation, there may exist imperfect channel detection, which can lead to the decrease of the channel utilization efficiency and the deterioration of fairness. The imperfect detection can generally be represented by the detection probability and false alarm probability, which depend on detection time, signal to noise ration (SNR), sampling rate and energy threshold. However, among the parameters, only the energy threshold can be dominated by LAA small base stations (SBSs) in the LAA scheme. Therefore, the energy threshold should be dynamically adjusted in the changeable channel environment such that the detection accuracy can improved as high as possible. Consider the fact that the optimization theory cannot be used to optimize the energy threshold since the expressions of performance indexes about the energy threshold are extremely complex, a Q-learning based energy threshold optimization algorithm (QLET) is thus proposed in the paper, where LAA SBSs act as the agent, the energy threshold is defined as the agent action, the different combinations of fairness and throughput are defined as the agent states, and the fairness and the reward function are also redefined. In order to ensure the smooth implementation of the proposed QLET algorithm, the information exchange mechanism, where the sending-confirmation mechanism and 1- persistent CSMA are used, is also proposed. Based on the proposed QL framework, the agent can learn the optimal energy threshold by repeatedly interacting with the environment, which enables the coexistence system to obtain the best coexistence performance. A large number of simulation results show that the proposed QLET is superior to the traditional fixed energy threshold scheme (FET) in terms of the fairness, WiFi collision probability and transmission delay, and that QLET is almost the same as FET in term of throughput. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have been widely applied for classification tasks in wireless communications such as link scheduling, user association and base station (BS) muting. When training a DNN using datasets with highly-skewed class distribution where most data belong to a few majority classes, learning performance for minority classes will degrade since the imbalanced data forces the training process to be biased towards the majority classes. In many wireless problems, multiple decisions for classification are made jointly, say deciding which BSs in a network should be muted to avoid interference. In this paper, we employ DNN to learn an optimal predictive interference coordination policy, and strive to avoid biased learning in such a multi-label multi-class classification problem. The major contribution is proposing a training method to encourage fairness among classes by minimizing the maximal cost of decisions among classes, which is converted into a problem to optimize the weighting factors on the training cost of each class. We provide a gradient descent algorithm to optimize the weighting factors and the model parameters of the DNN by alternative updates. Simulation results show that the training method can improve the learning performance for the minority class and can achieve higher network utility than existing training methods. [ABSTRACT FROM AUTHOR]

Modern vehicular applications are resource-intensive and have different Quality of Service (QoS) requirements. In this direction, Vehicular Cloud Computing (VCC) has emerged as a new paradigm to host such applications in the cloud. In this paper, we extend the VCC paradigm to include federation, wherein the data centers from multiple cloud providers are pooled in to support applications that can not be supported in a single provider. We address the problem of dynamic resource allocation in a federated vehicular cloud. We propose a dynamic resource pricing model for sharing resources between the cloud providers, based on which we formulate resource allocation as an optimization problem. The objective is to minimize the cost of the vehicular service provider while meeting the delay bounds of different vehicular applications, for which a cost and delay-aware, polynomial-time algorithm is proposed. We evaluate the proposed algorithm under different scenarios to show that exploiting federation brings great benefits for the service providers and end-users alike. The proposed algorithm is seen to meet the delay requirements with a significant cost reduction to the provider up to 50% compared to baseline methods. We also show that the proposed algorithm can serve more than 90% of requests with lower cost and fewer VM migrations. It reduces the number of VM migrations by 72%. [ABSTRACT FROM AUTHOR]

In this paper, we study the beam synthesis and management methods for the phased antenna array-based wireless communications systems, e.g., 5 G mmWave communications. First, we propose a novel on-off beam synthesis method that can synthesize any given on-off beam pattern. The proposed method maximizes the beam power towards a given set of on-directions while suppressing the power towards all off-directions. Second, by using a set of beams synthesized by the proposed on-off beam synthesis method, we design a non-interactive hierarchical beam management algorithm that can greatly reduce the radio resource usage, feedback overhead, and the latency of the beam discovery, compared to the exhaustive beam search algorithms. Since the proposed beam management algorithm is non-interactive, it can be applied to the initial access as well as the beam tracking, and is especially useful for supporting a massive number of devices. Finally, we show the validness of the proposed beam synthesis and management methods by conducting experiments on a phased antenna array experimental testbed. [ABSTRACT FROM AUTHOR]

This paper investigates the estimation of low-rank multiple-input multiple-output (MIMO) channels in millimeter wave (mmWave) communications. Hybrid MIMO transceivers equipped with uniform linear arrays (ULAs) and phase shifter networks are considered. We propose a novel three-stage channel estimator by exploiting the low-rankness of the channel matrix and knowledge of the array response: We first obtain a low-rank estimate of the channel matrix using inductive matrix completion (IMC); then estimate the angle of arrival (AoA) and angle of departure (AoD) for the propagation paths by solving two one-dimensional spectrum estimation problems using Toeplitz rectification and the root multiple signal classification (MUSIC) algorithm; and finally pair the AoAs and AoDs and estimate the channel gains by solving a sparse recovery problem. Each stage is implemented at a low complexity and thus the overall complexity is kept low. A priori knowledge about the channel is exploited progressively to enhance the performance. The simulation results suggest significant gains in the channel estimation performance along with sparse representations of the estimated channel. [ABSTRACT FROM AUTHOR]

In this paper, we investigate power allocation algorithms in a downlink millimeter wave non-orthogonal multiple access (mmWave-NOMA) system, which consists of one base station (BS) and a group of two-user clusters. Specifically, two optimization problems are formulated to maximize the achievable sum rate (ASR) and energy efficiency (EE), respectively, while satisfying the individual quality of service (QoS) constraints. To ensure the stability of successive interference cancellation (SIC-stability), we specially add the power order constraints, which are often neglected in existing works. By dividing each formulated problem into more tractable inter-cluster and intra-cluster sub-problems, and deriving the corresponding solutions, we propose the ASR maximization based power allocation (ASRMax-PA) and EE maximization based power allocation (EEMax-PA) algorithms. Numerical results show that the proposed ASRMax-PA (EEMax-PA) algorithm is much better than the state-of-the-art schemes in term of ASR (EE), while yields quite good EE (ASR) performance simultaneously. Moreover, both the two proposed algorithms can ensure SIC-stability, which is shown to have a significant impact on the NOMA system performance. [ABSTRACT FROM AUTHOR]

In this paper, a new adaptive inverse control (AIC) method is presented to solve vehicle handling inverse dynamics problem and achieve path tracking control for emergency avoidance. The designed AIC system includes two crucial modules: the model identifier and the inverse model controller which are both the nonlinear adaptive filters constituted by neural networks. Firstly, they are both trained offline and connected with the vehicle to be the initial states of AIC system. Then, the back-propagation (BP) algorithm is used to adjust the weight parameters of the model identifier to identify the nonlinear characteristics of vehicle in real time. Simultaneously, the inverse model controller is used to be the controller of AIC system and the controller's weight parameters are tuned by the back-propagation through model (BPTM) algorithm online. A novel adaptive learning rate is introduced into the BP algorithm and the BPTM algorithm to guarantee the stability of neural networks. Finally, the inverse model controller can be regarded as the inverse of vehicle and used to solve required steering wheel angle according to the desired path to realize path tracking. The double lane change (DLC) road is used to be the desired path for emergency avoidance. The simulation results illustrate that the AIC system could realize accurate path tracking although there exist external disturbances and the AIC is an effective method to deal with the handling inverse dynamics problem for emergence avoidance. [ABSTRACT FROM AUTHOR]

The Internet of vehicles (IoV) is a large information interaction network that collects information on vehicles, roads and pedestrians. One of the important uses of vehicle networks is to meet the entertainment needs of driving users through communication between vehicles and roadside units (RSUs). Due to the limited storage space of RSUs, determining the content cached in each RSU is a key challenge. With the development of 5G and video editing technology, short video systems have become increasingly popular. Current widely used cache update methods, such as partial file precaching and content popularity- and user interest-based determination, are inefficient for such systems. To solve this problem, this paper proposes a QoE-driven edge caching method for the IoV based on deep reinforcement learning. First, a class-based user interest model is established. Compared with the traditional file popularity- and user interest distribution-based cache update methods, the proposed method is more suitable for systems with a large number of small files. Second, a quality of experience (QoE)-driven RSU cache model is established based on the proposed class-based user interest model. Third, a deep reinforcement learning method is designed to address the QoE-driven RSU cache update issue effectively. The experimental results verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

With the prosperity of vehicular networks and intelligent transport systems, vast amount of data can be easily collected by vehicular devices from their users and widely spread in the vehicular networks for the purpose of solving large-scale machine learning problems. Hence how to preserve the data privacy of users during the learning process has become a public concern. To address this concern, under the celebrated framework of differential privacy (DP), we present in this paper a decentralized parallel stochastic gradient descent (D-PSGD) algorithm, called DP2-SGD, which can offer protection for privacy of users in vehicular networks. With thorough analysis we show that DP2-SGD satisfies (ε, δ)-DP while the learning efficiency is the same as D-PSGD without privacy preservation. We also propose a refined algorithm called EC-SGD by introducing an error-compensate strategy. Extensive experiments show that EC-SGD can further improve the convergence efficiency over DP2-SGD in reality. [ABSTRACT FROM AUTHOR]

With the rise of data-hungry applications and the rapid development of the Internet-of-Things (IoT), 5G and device-to-device (D2D) communications come into being. While such technologies are designed to support massive data access to meet the ever-increasing demands, the complexity and heterogeneity of the network structure bring bigger challenges for interference management. As such, downlink/uplink decoupling (DUDe) has become an attractive approach, as it offloads user equipments (UEs) to small-cells (SCells) and creates a more benign environment for D2D communication. In this paper, we investigate the application of DUDe in D2D-underlay heterogeneous networks, and propose an efficient joint cell-association, subchannel allocation, and power control scheme for network sum-rate maximization. The proposed scheme includes a minimum path-loss based DUDe to decide the uplink (UL) serving base-station, and a greedy coloring scheme with modified Munkres algorithm to cluster the UEs, and allocate subchannels. Furthermore, the difference of convex functions based programming algorithm for power control is utilized to maximize the network sum-rate while satisfying the UE transmit power and data rate constraints. The results presented show that the proposed scheme can achieve superior performance to the coupled benchmarks. [ABSTRACT FROM AUTHOR]

Direction of arrival (DOA) estimation of underwater multipath signals has become an indispensable part of many military and civil applications in the oceanic field. However, DOAs of different paths of the multipath signals may be the same or too close to be separated in space, which brings a great challenge to the DOA estimation of multipath signals. Besides, the number of multipath is generally unknown and time-varying in passive scenarios which generally lead to an underdetermined case. Therefore, it is essential to study an efficient algorithm to realize the underdetermined DOA estimation of underwater multipath signals in passive scenarios. This paper proposes a passive DOA estimation algorithm of underwater multipath signals via spatial time-frequency distributions (STFDs). Firstly, we establish a multipath signals model and utilize the quadratic time-frequency (TF) analysis method to reduce cross-terms while keeping a good resolution. Then, we estimate the number of multipath and introduce a novel multi-ridge detection technique to extract the TF trajectories of multipath signals, which adopts the idea of smoothing forward and backward, and the edge TF point detection strategy. Lastly, we estimate the DOA of each path of the multipath signals separately and realize source association based on the clustering-based method. Simulation results and analysis demonstrate that this algorithm can effectively improve the estimation accuracy of instantaneous frequency (IF) and DOA estimation of underwater multipath signals in underdetermined cases and passive situations. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive nonlinear differential limiter (ANDL) is proposed to efficiently alleviate the impact of impulsive noise (IN) in a communication system. Unlike existing nonlinear methods, the ANDL is implemented in the analog domain where the broader acquisition bandwidth makes outliers more detectable and consequently it is easier to remove them. While the proposed ANDL behaves like a linear filter when there is no outlier, it exhibits intermittent nonlinearity in response to IN. Therefore, the structure of the matched filter in the receiver is modified to compensate the filtering effect of the ANDL in the linear regime. In this paper, we quantify the performance of the ANDL by deriving a closed-form analytical bound for the average signal-to-noise ratio at the output of the filter. The calculation is based on the idea that the ANDL can be perceived as a time-variant linear filter whose bandwidth is modified based on the intensity of the IN. In addition, by linearizing the filter time parameter variations, we treat the ANDL as a set of linear filters where the exact operating filter at a given time depends upon the magnitude of the outliers. The theoretical average bit error rate is validated through simulations and the performance gains relative to classical methods such as blanking and clipping are quantified. [ABSTRACT FROM AUTHOR]

Multi-hop device-to-device (D2D) communications play an important role in expanding D2D coverage. In this paper, we study a relay-based and network-coding-assisted (in particular, XOR coding) multi-hop D2D communication system. In the system, toward jointly considering the impact of interference and network traffic conditions on the quality of D2D communications, various channel fading models and traffic models are investigated, and the packet loss probability of D2D links is meticulously computed using these models. With the packet loss probability of D2D links, the general closed-form expressions of end-to-end packet loss probability (E2EPLP) of the system with the presence (or absence) of XOR coding are subsequently derived. Our experiments reveal that both the number of relays and the mechanism of XOR coding can affect the system performance. Specifically, the increase in the number of relays will lower the overall system performance (e.g., an increase in the E2EPLP and end-to-end completion time, and a decrease of the end-to-end rate may follow as a result). On the other hand, although the presence of XOR coding unfortunately raises the system E2EPLP, it can effectively improve the end-to-end completion time and end-to-end rate. It is our belief that the analytical approach proposed in this paper and the results found in our work can be useful to peer studies in the context of applying network coding in multi-hop D2D networks. [ABSTRACT FROM AUTHOR]

This paper is concerned with the network selection for heterogeneous networks. Although many network selection approaches have been developed for heterogeneous networks, few of them explore the network selection problem for integrated cellular and drone-cell networks. Furthermore, most of the existing designs may be unsuitable for the integrated cellular and drone-cell networks owing to the high dynamic drone-cell-user association and the relatively limited drone-cell network capacity. In this paper, we investigate a network selection problem for the integrated cellular and drone-cell networks with a goal of maximizing a proportional fairness function of time average utilities across users under coarse correlated equilibrium constraints and minimum time average utility constraints. To mitigate this challenging problem, we first convert it into a non-linear integer programming (NLIP) problem based on our derived theoretical results. Next, we propose a repeated-stochastic-game-based efficient and fair network selection (RSG-EF) algorithm to alleviate the NLIP problem by leveraging a linear approximation mechanism. Simulation results show that the RSG-EF algorithm can achieve the highest total utility and a high level of fairness across users compared with three benchmark algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a novel autocorrelation-based algorithm that uses statistical knowledge to detect orthogonal frequency division multiplexing signals in channels where frequency offset is present. The algorithm may be viewed as a significant improvement over other types of autocorrelation algorithms that appear in literature that lead to false alarm due to the hardware impairment of frequency offset. The algorithm works by making an unbiased estimate of the square of an autocorrelation coefficient and from that deduces an appropriate probability density function for the phase angle of the complex test statistic and thereby palliating the effect of phase distortion introduced by the frequency offset. It is shown that the algorithm presented in this paper can be implemented on a testbed, as well as overcome simulations that have been specifically designed to have worst case frequency offset phase distortion conditions. [ABSTRACT FROM AUTHOR]

This paper deals with the problems of the Plug-in Electric Vehicles charging costs in housing sector. A review of the optimal strategies is proposed and as such six algorithms are presented: three smart unidirectional and three smart bidirectional charging algorithms where the Vehicle-to-Grid (V2G) and the Vehicle-to-Home (V2H) concepts were exploited. In addition an innovative V2G algorithm named Optimal Logical Control (V2G-OLC) is introduced in this paper. This latter is dedicated to the French energy billing system within the peak/base hour's contract. All the algorithms are tested over a set of 1000 data composed of real elements with four different daily energy price profiles. The results show a great efficiency of the V2G-OLC algorithm compared to the traditional optimal charging strategies. Indeed, the results show a reduction of 47.94% of the average charging cost with a unitary selling/buying price ratio in comparison to the simple charging at 230V-32A. [ABSTRACT FROM AUTHOR]

Autonomous driving technologies can provide greater safety, comfort, and efficiency for future transportation systems. Until now, much of the research effort has been devoted to developing different sensing and control algorithms. However, there has been limited research on how to handle sensor errors efficiently. A simple error in the sensor may lead to an unexpected failure in the whole autonomous driving function. In those cases, the vehicle is then recommended to be sent back to the manufacturer for repair, which costs time and money. This paper introduces an efficient automatic online sensor correction method. The method includes four major functions: sensor error detection, human teaching, vehicle learning, and vehicle self-evaluation. The first function is assumed to be ready and the major contribution of this paper is the human-vehicle teaching and learning framework, which utilizes human-vehicle interaction to collaboratively adjust the parameter in the control model in order to compensate for the errors of the sensors. The self-evaluation function is also briefly introduced. The applications of this method to radar and vision sensors to recover adaptive cruise control and lane keeping functions are introduced in detail. Experimental results acquired from high-fidelity 1/10-scale autonomous driving vehicles illustrate the effectiveness and advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

Torque safety is one of the key elements of functional safety for future automobile development. For drive-by-wire systems that are commonly used in modern vehicles, pedal signals are critical for the vehicle supervisory controller to generate appropriate torque request to the powertrain, in order to keep the vehicle speed as desired. Problems with pedals or pedal sensors will result in incorrect torque request sent to the drivetrain, possibly resulting in torque safety problems, such as sudden unintended acceleration. This paper proposes a fault detection and isolation strategy based on structural analysis as well as fault mitigation methods for torque safety of drive-by-wire systems, with focus on pedal mechanical stiction fault and pedal sensor faults. Before the diagnostic strategy is designed, fault modeling for these two types of faults are introduced to assist the design of diagnostic tests. In addition, for pedal mechanical stiction fault, this paper introduces an effective approach to calibrate the thresholds in the fault diagnostic system in order to achieve the desired tradeoff between false alarm rate and detection delay. The detection and mitigation strategies are calibrated and validated through model-in-the-loop and hardware-in-the-loop simulations using realistic pedal profiles from the testing results of the EcoCAR2 prototype vehicle developed at the authors’ institution. [ABSTRACT FROM AUTHOR]

In the 5G ultra-dense wireless heterogeneous network system, it is a crucial issue to implement an effective network selection strategy to satisfy the demands of massive edge users and novel 5G services. In this paper, we model the network selection problem of edge users requesting different services as a bipartite graph, and propose a network selection algorithm based on weighted bipartite graph matching for 5G ultra-dense heterogeneous networks, named BGMNS. The proposed algorithm combines Analytic Hierarchy Process (AHP) and Grey Relation Analysis (GRA) to analyze the preferences of multiple services for different network attributes and obtain the Quality of Experience (QoE) of different edge users for each network. Moreover, in order to realize the fair allocation of network resources, we comprehensively consider the importance of the requested services and the obtained QoE by edge users to construct system fairness index. The BGMNS algorithm can optimize the overall QoE of users under the premise of ensuring the system fairness. Simulation results show that compared to the existing network selection algorithms, the proposed BGMNS algorithm can not only provide stable access to users when network status fluctuates randomly, but also effectively reduce user blocking probability as well as total packet loss rate, and significantly improve user average energy efficiency. [ABSTRACT FROM AUTHOR]

In general, a two-dimensional (2D) moving trajectory can be reconstructed using data collected from a 6-axis IMU (inertial measurement unit), which consists of a 3-axis gyroscope and a 3-axis accelerometer. Since the power consumption of a gyroscope is significantly larger than that of an accelerometer, to be energy-efficient, it is more preferable to use two gyro-free IMUs as alternatives. The existing gyro-free trajectory reconstruction approach is an angular velocity (i.e. ω) -based method. The design of an ω-free gyro-free approach remains as a research gap, which motivates this research work. In this paper, an ω-free accelerometer pair (OFAP) method is proposed, which can yield a lower accumulated error, has a lower power consumption, is more scalable, and can deliver the same trajectory reconstruction performance as the existing approach. [ABSTRACT FROM AUTHOR]

Both Device-to-device (D2D) and full-duplex (FD) have been widely recognized as spectrum efficient techniques in the fifth-generation (5G) networks. By combining them, the FD-D2D aided underlaying networks (FN) has exhibited considerable technical advantages in terms of both spectral efficiency (SE) and energy efficiency (EE). Considering the fact that the performance of FN may be severely affected by users’ workload, the workload-driven FN (WFN) must be investigated. In this paper, a deep learning based transmit power allocation (TPA) method is proposed for automatically determining the optimal transmit powers of co-spectrum cellular users (CUs) and D2D users (DUs) relying on a deep neural network. Unlike the conventional transmit-power-control schemes, in which complex optimization problems must be addressed in an iterative manner (it usually requires a relative longer computational time), the proposed scheme enables each DU to determine its transmit power with a relatively shorter time. Furthermore, an improved iterative subspace-pursuit algorithm, as the performance benchmark, is formulated for WFN. In addition, to reflect the influence imposed by the workload, the penalty-based statistical sum-date-rate (PSS) can be employed as the performance metric of WFN. Numerical results show that the proposed scheme is capable of achieving a PSS comparable with that of the traditional iterative-based algorithms even under heavy-workload scenarios, but the computational complexity of the former can be significantly reduced. [ABSTRACT FROM AUTHOR]

Wireless network communication has developed rapidly in recent years, especially in the field of Internet of vehicles (IoV). However, due to the limitations of traditional network architecture, resource scheduling in wireless network environment is still facing great challenges. We focus on the urgent need of users for bandwidth resources in the IoV scenario under virtual network environment. This paper proposes a bandwidth aware multi domain virtual network embedding (BA-VNE) algorithm. The algorithm is mainly aimed at the problem that users need a lot of bandwidth in wireless communication mode, and solves the problem of bandwidth resource allocation from the perspective of virtual network embedding (VNE). In order to improve the performance of the algorithm, we introduce particle swarm optimization (PSO) algorithm to optimize the performance of the algorithm. In order to verify the effectiveness of the algorithm, we have carried out simulation experiments from link bandwidth, mapping cost and virtual network request (VNR) acceptance rate. The final results show that the proposed algorithm is better than other representative algorithms in the above indicators. [ABSTRACT FROM AUTHOR]

In this paper, we propose transmission point selection algorithms to provide lower service time between an application server to end users by selecting the transmission point used by a newly identified metric, i.e., service latency. In particular, the proposed ordered service latency-based transmission point selection algorithm is beneficial in a sense that it does not increase average service time but decrease the maximum service time although it brings additional computational complexity slightly. We also provide the performance bounds with proven approximation ratios and verify that the proposed algorithm achieves lower maximum service time without degrading the average service time as well as waiting time through simulations in practical environments. [ABSTRACT FROM AUTHOR]

In this paper, a resource allocation problem is formulated to maximize the throughput of vehicular user equipments (VUEs) in both licensed, and unlicensed frequency bands under constraints of reliability, and latency for vehicular communications as well as the Quality of Service (QoS) for WiFi network based on a network system with coexisting VUEs, and WiFi user equipments (WUEs). In the system, the VUEs are able to access to the unlicensed frequency bands, and the interference among the VUEs, and WUEs are mitigated by the listen-before-talk (LBT) scheme or the duty cycle scheme. Due to the mixed integer nonlinear programming (MINLP) objective in the optimization problem, the problem is hard to be solved directly. Instead, we solve the problem by two steps. The number of VUEs offloaded to unlicensed frequency bands as well as the time factor for duty cycle scheme is firstly determined, and then, the optimization problem is converted into a convex optimization problem, which is solved by the proposed Lagrange Duality Method (LDM). Numerical results show the efficiency of our proposed application scenario compared to case with only LTE system or only WiFi system. Moreover, compared to traditional Greedy algorithm, the proposed algorithm performs better in terms of throughput with a guaranteed QoS of WUEs. [ABSTRACT FROM AUTHOR]

This paper investigates subchannel allocation based on clustered interference alignment in dense small cell networks when all small cell user equipments (SUEs) have differentiated requirements for data streams. By imposing the condition that each cluster has a size not exceeding the maximum value achieved when each SUE needs only one data stream, we maximize the number of SUEs with guaranteed requirements for data streams, which is NP-hard. Hence, we propose a two-phase efficient solution with much lower complexity and reduced feedback overhead. First, similarity clustering is performed by graph partition, and then, subchannel allocation is done through a coloring algorithm. Numerical results show that the proposed solution offers a performance better than the related schemes and close to the approximate optimal solution. [ABSTRACT FROM AUTHOR]

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

In multi-user downlink small cell networks, cooperative resource allocation (RA) within a small cell cluster is a key technique to enhance network capacity. However, capacity-maximizing RA in frequency-selective fading channels requires global channel state information (CSI) of users within a small cell cluster, which makes it infeasible in practical networks with limited direct link capacity. To circumvent this global CSI assumption, most of the existing studies on RA have been based on several CSI assumptions such as local CSI and local CSI at the transmitters (CSIT). Nevertheless, cost functions with local CSI or local CSIT in the literature rely on heuristic formulations, because the sum-rate cannot be computed if without global CSI. In this paper, we propose a deep reinforcement learning-based RA algorithm to maximize the sum-rate for any given limited information on instantaneous CSI or sum-rate at the previous period. The proposed scheme is not restricted to certain CSI assumptions, but attempts to find the best RA for any given information such as quantized local CSI and quantized local CSIT; thus, it is applicable to any given direct link capacity. The proposed algorithm is self-adaptive in time-varying channels, since it is not divided into training and test phases. We modify the target neural network (TNN) scheme to enhance the sum-rate and the convergence speed. Numerical simulations confirm that: i) the proposed algorithm outperforms the conventional algorithms even under the same CSI assumption such as local CSI and local CSIT; ii) a flexible trade-off between the amount of CSI and the sum-rate is realizable in practical systems. [ABSTRACT FROM AUTHOR]

In this paper, we study the network of unmanned aerial vehicles (UAVs) which acts as a relay between a base station, and remotely deployed Internet of Things devices. UAVs have received much attention for connecting remotely located networks/objects, but there is a lack of study on multi-hop relay transmission schemes over UAV networks. It is particularly challenging because of the their high mobility, and limited resources, to name a few. In this regard, we propose an energy-efficient, cooperative multi-hop relay scheme for UAVs. We formulate the optimal multi-hop transmission scheduling problem to minimizes the power consumption of UAVs without violating the given time constraint. We convert the initial formulation into a low-complex iterative algorithm, making it suitable for an online scheduling. Also, we propose a buffer prediction, and correction scheme to initiate the multi-hop transmission scheduling without having the perfect buffered data information. Evaluation results show that the proposed scheme can energy-efficiently schedule the multi-hop transmissions over dynamic UAVs without violating the time constraints. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency and comfort of autonomous vehicles while ensuring safety, the decision algorithm needs to interact with human drivers, infer the most probable behavior and then makes advantageous decision. This paper proposes a Nash-Q learning based motion decision algorithm to consider the interaction. First, the local trajectory of surrounding vehicle is predicted by kinematic constraints, which can reflect the short-term motion trend. Then, the future action space is built based the predicted local trajectory that consists of five basis actions. With that, the Nash-Q learning process can be implemented by the game between these basis actions. By elimination of strictly dominated actions and the Lemke-Howson method, the autonomous vehicle can decide the optimal action and infer the behavior of surrounding vehicle. Finally, the lane merging scenario is built to test the performance contrast to the existing methods. The driver in loop experiment is further designed to verify the interaction performance in multi-vehicle traffic. The results show that the Nash-Q learning based algorithm can improve the efficiency and comfort by 15.75% and 20.71% to the Stackelberg game and the no-interaction method respectively while the safety is ensured. It can also make real-time interaction with human drivers in multi-vehicle traffic. [ABSTRACT FROM AUTHOR]

The multi-UAV network is promising to extend conventional networks by providing broader coverage, and better reliability. Nevertheless, the broadcast nature of wireless signals, and the broader coverage expose multi-UAV communications to the threats of passive eavesdroppers. Recent studies mainly focus on securing a single legitimate link, or communications between a UAV, and multiple ground users in one/two-UAV-aided networks, while the physical layer secrecy analysis for hierarchical multi-UAV networks is underexplored. In this paper, we investigate a general two-tier UAV network consisting of multiple UAV transmitters (UTs), and multiple UAV receivers (URs) in the presence of multiple UAV eavesdroppers (UEs). To protect all legitimate UT-UR links against UEs at the physical layer, we design a two-stage framework consisting of a UT-UR association stage, and a cooperative transmission stage. Specifically, we formulate the secure transmission problem into a many-to-one matching game followed by an overlapping coalition formation (OCF) game, taking into account the limited capabilities, and the throughput requirements of URs, as well as the transmission power constraints of UTs. A matching algorithm, and an OCF algorithm are proposed to solve these two sequential games whose convergences, and stabilities are guaranteed theoretically. Simulation results show the superiority of our algorithms, and the effectiveness of our two-stage game framework in the terms of secrecy performance. [ABSTRACT FROM AUTHOR]

While the Poisson point process (PPP) has been widely employed to model the user distribution in many network design problems, an existing challenge is that it often reveals inaccuracy in small-cell networks. In this paper, instead of employing PPP, we capture the randomness of user equipment (UE) by collecting many their realizations. Specifically, we focus on the millimeter-wave (mmWave) base station (BS) deployment problem in an urban geometry, based on the application of a scenario sampling approach, previously introduced for large-scale optimization, to quantitatively sample a portion of the UE realizations. Motivated by the scenario sampling, a reduced-scale mmWave BS deployment problem is formulated, whose optimal solution is attained by the proposed low-complexity iterative search algorithm. A required number of samples that guarantee a specified majority of the link quality constraints is analyzed. Simulation results verify the scenario sampling theory and the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Connected vehicles will change the modes of future transportation management and organization, especially at an intersection without traffic light. Centralized coordination methods globally coordinate vehicles approaching the intersection from all sections by considering their states altogether. However, they need substantial computation resources since they own a centralized controller to optimize the trajectories for all approaching vehicles in real-time. In this paper, we propose a centralized coordination scheme of automated vehicles at an intersection without traffic signals using reinforcement learning (RL) to address low computation efficiency suffered by current centralized coordination methods. We first propose an RL training algorithm, model accelerated proximal policy optimization (MA-PPO), which incorporates a prior model into proximal policy optimization (PPO) algorithm to accelerate the learning process in terms of sample efficiency. Then we present the design of state, action and reward to formulate centralized coordination as an RL problem. Finally, we train a coordinate policy in a simulation setting and compare computing time and traffic efficiency with a coordination scheme based on model predictive control (MPC) method. Results show that our method spends only 1/400 of the computing time of MPC and increase the efficiency of the intersection by 4.5 times. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of detector design in the frequency-selective fading channels for unique-word-based single-carrier spatial modulation (UWSC-SM) systems. We propose to precede the conventional M-algorithm with an appropriately designed frequency-domain prefilter to condition the equivalent channel so that the number of the survivor paths needed for a satisfactory performance can be significantly reduced. By exploiting the block circulant channel induced by the UWSC-SM block format, the prefilter can be computed, and carried out efficiently using the fast Fourier transforms. Simulation results demonstrate that, compared to the conventional M-algorithm, the proposed algorithm achieves essentially the same error-rate performance with a considerably lower overall complexity in systems where the number of receive antennas is no smaller than that of transmit antennas. [ABSTRACT FROM AUTHOR]

Energy management is an enabling technique to guarantee the reliability and economy of hybrid electric systems. This paper proposes a novel machine learning-based energy management strategy for a hybrid electric bus (HEB), with an emphasized consciousness of both thermal safety and degradation of the onboard lithium-ion battery (LIB) system. Firstly, the deep deterministic policy gradient (DDPG) algorithm is combined with an expert-assistance system, for the first time, to enhance the “cold start” performance and optimize the power allocation of HEB. Secondly, in the framework of the proposed algorithm, the penalties to over-temperature and LIB degradation are embedded to improve the management quality in terms of the thermal safety enforcement and overall driving cost reduction. The proposed strategy is tested under different road missions to validate its superiority over state-of-the-art techniques in terms of training efficiency and optimization performance. [ABSTRACT FROM AUTHOR]

The direct position determination (DPD) algorithms have been shown to outperform conventional two-step methods in localization accuracy under poor observation conditions (e.g., low signal-to-noise ratios or short signal observation durations). In this paper, we consider the distributed DPD of a static source in large-scale sensor networks. Different from the centralized DPD (CDPD) algorithms where all the raw signal samples are transmitted globally to a fusion center to solve for the source position, in our method the signal samples are only exchanged locally between neighboring sensors, and the source position is estimated using the proposed DPD algorithm distributively. The problem-specific cost function, which is different from the cost function of CDPD, is first derived. To obtain the cost function at each sensor, we propose to transmit the low-dimensional sufficient statistics across the sensor networks instead of the original high-dimensional raw signal samples, and to reconstruct the cost function via simple interpolations. Simplification of cost function evaluation is also developed to further reduce complexity. Finally, source position is estimated at each sensor efficiently via the importance sampling algorithm. Simulation results demonstrate the superior performance of the proposed method over existing adaptive distributed DPD as well as the two-step algorithms under poor observation conditions, and its computational efficiency is significantly improved compared with the CDPD. [ABSTRACT FROM AUTHOR]

In this paper, we study an intelligent omni-surface (IOS)-assisted downlink communication system, where the link quality of a mobile user (MU) can be improved with a proper IOS phase shift design. Unlike the intelligent reflecting surface (IRS) in most existing works that only forwards the signals in a reflective way, the IOS is capable to forward the received signals to the MU in either a reflective or a transmissive manner, thereby enhancing the wireless coverage. We formulate an IOS phase shift optimization problem to maximize the downlink spectral efficiency (SE) of the MU. The optimal phase shift of the IOS is analysed, and a branch-and-bound based algorithm is proposed to design the IOS phase shift in a finite set. Simulation results show that the IOS-assisted system can extend the coverage significantly when compared to the IRS-assisted system with only reflective signals. [ABSTRACT FROM AUTHOR]

A hybrid method for the prediction of the remaining useful life (RUL) of Lithium-ion batteries considering error-correction is proposed in respect of capacity diving phenomenon in later capacity degradation. First, an improved empirical capacity degradation model is proposed based on our previous work, with the analytic expression further revised in this paper to enhance its fitting accuracy, parameter identifiability, and applicability to tracking algorithm. Unscented particle filter (UPF) algorithm is then implemented to obtain prognostic results with original error series. Next, to enhance the quality of original error data by reducing the local uncertainty, complete ensemble empirical mode decomposition (CEEMD) algorithm is utilized to reconstruct error series. The fundamental error evolution information is retained by selecting relatively highly correlated intrinsic mode functions (IMFs) of the decomposition results of original error series. Finally, after employing Gaussian process regression (GPR) algorithm, the prognostic error to correct the UPF-based prognostic result is obtained from the reconstructed error series. RUL prediction experiments for batteries with different working conditions have been conducted to verify the improved performance of the proposed model and the hybrid method, with mean absolute percentage errors of battery capacity degradation predicted less than 0.4%. [ABSTRACT FROM AUTHOR]

Combining intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA) is an effective solution to enhance communication coverage and energy efficiency. In this paper, we focus on an IRS-assisted NOMA network and propose an energy-efficient algorithm to yield a good tradeoff between the sum-rate maximization and total power consumption minimization. We aim to maximize the system energy efficiency by jointly optimizing the transmit beamforming at the BS and the reflecting beamforming at the IRS. Specifically, the transmit beamforming and the phases of the low-cost passive elements on the IRS are alternatively optimized until the convergence. Simulation results demonstrate that the proposed algorithm in IRS-NOMA can yield superior performance compared with the conventional OMA-IRS and NOMA with a random phase IRS. [ABSTRACT FROM AUTHOR]

In this paper, we aim to design an autonomous tracking system for a swarm of unmanned aerial vehicles (UAVs) to localize a radio frequency (RF) mobile target. In the system, UAVs equipped with omnidirectional received signal strength (RSS) sensors can cooperatively search the target with a specified tracking accuracy. To achieve fast localization and tracking in the highly dynamic channel environment (e.g., time-varying transmit power and intermittent signal), we formulate a flight decision problem as a constrained Markov decision process (CMDP) with the main objective of avoiding redundant UAV flight path. Then, we propose an enhanced multi-agent reinforcement learning to coordinate multiple UAVs performing real-time target tracking. The core of the proposed scheme is a feedback control system that takes into account the uncertainty of the channel estimate. We prove that the proposed algorithm can converge to the optimal decision. Our simulation results show that the proposed scheme outperforms standard Q-learning and multi-agent Q-learning algorithms in terms of searching time and successful localization probability. [ABSTRACT FROM AUTHOR]

This paper presents a framework for optimizing the power allocation between a battery and supercapacitor in an electric vehicle energy storage system. A convex optimal control formulation is proposed that minimizes total energy consumption whilst enforcing hard constraints on power output and total energy stored in the battery and supercapacitor. An alternating direction method of multipliers (ADMM) algorithm is proposed, for which computational and memory requirements scale linearly with the length of the prediction horizon (and can be reduced using parallel processing). The optimal controller is compared with a low-pass filter against an all-battery baseline in numerical simulations, where it is shown to provide significant improvement in battery degradation (inferred through reductions of 71.4% in peak battery power, 21.0% in root-mean-squared battery power, and 13.7% in battery throughput), and a reduction of 5.7% in energy consumption. It is also shown that the ADMM algorithm can solve the optimization problem in a fraction of a second for prediction horizons of more than 15 minutes, and is therefore a promising candidate for online receding-horizon control. [ABSTRACT FROM AUTHOR]

Trailer mass is one of the important trailer parameters that affects the stability of the tractor-trailer systems. In this paper, two different approaches are proposed to estimate trailer mass for arbitrary tractor-trailer configurations; dynamic system model-based and Machine Learning (ML) approaches. The stability of the dynamic system model-based estimation algorithm is analyzed, establishing the convergence of the estimation error to zero. In the proposed ML-based approach, a Deep Neural Network (DNN) is designed to estimate trailer mass. The inputs of the ML-based method have been selected based on the tractor-trailer dynamic model, and are considered to be normalized by the tractor mass, tire sizes, and geometry so that re-training of the network is not needed for different towing vehicles. The simulation and experimental results justify the accuracy of the trailer mass estimation in various cases and demonstrate that the trailer mass can be estimated with less than 10% error. [ABSTRACT FROM AUTHOR]

In the era of the fifth generation (5G) communication networks, massive multiple-input multiple-output (MIMO) systems demand even lower computation complexity and power consumption while catching up with good detection performance. In this paper, a low-complexity nonlinear detection algorithm is proposed for massive MIMO systems, which is based on partial tree search and successive interference cancellation (SIC). The proposed scheme allows us to expedite the detection process by coping with the transmit symbols group by group. As compared to vertical-Bell laboratories layered space time (V-BLAST), the major breakthrough of computation reduction lies in the fact that the partial tree search can assist the detection process to avoid the inversion of the detection matrix required in each recursion of the SIC process. Both computational complexity analysis and simulation results show that our proposed algorithm not only significantly reduces computational complexity, but also has better bit error rate (BER) performance. [ABSTRACT FROM AUTHOR]

Codeword position index based sparse code multiple access (CPI-SCMA) is a novel variant of SCMA that employs the idea of index modulation (IM), which has been proposed recently as an effective alternative to the conventional SCMA system. In contrast to the conventional SCMA that directly maps the information bits of each user to a predefined M-point codebook, the data in CPI-SCMA is not only conveyed by the M-point codebook as in SCMA, but also by the indexes of the codeword positions. In this paper, we first propose a simplified detector termed as message passing algorithm aided index reliability detection (MPA-IRD) that is more suitable for practical implementation. Moreover, we investigate the spectral efficiency (SE) in CPI-SCMA, and two important features are identified to facilitate the design of the system. A tight approximation of the average block error probability (ABLEP) under the maximum likelihood detector (MLD) is also derived from studying the error rate performance of CPI-SCMA. Finally, to further improve the SE of CPI-SCMA, an enhanced version of CPI-SCMA termed as hybrid CPI-SCMA (HCPI-SCMA) is proposed. It is shown via both simulation, and analytical results that the proposed scheme not only achieves better error rate performance in the high signal-to-noise ratio (SNR) region but also improves the SE, and the level of robustness under channel estimation error compares to SCMA. [ABSTRACT FROM AUTHOR]

In this paper, we study the handover (HO), and power allocation problem in a two-tier heterogeneous network (HetNet), which consists of a macro base station, and some millimeter-wave (mmWave) small base stations. We establish an HO management, and power allocation scheme to maximize the overall throughput while reducing the HO frequency. In particular, considering the interrelationship among decisions made by different user equipments (UEs), we first model the HO, and power allocation problem as a fully cooperative multi-agent task, in which all agents, i.e., UEs, have the same target. Then, to solve the multi-agent task, and get decentralized policies for each UE, we develop a multi-agent reinforcement learning (MARL) algorithm based on the proximal policy optimization (PPO) method, by introducing the centralized training with decentralized execution framework. That is, we use global information to train policies for each UE, and after the training is finished, each UE obtains a decentralized policy, which can be implemented only based on each UE's local observation. Specially, we introduce the counterfactual baseline to address the credit assignment problem in centralized learning. Due to the centralized training, the decentralized polices learned by multi-agent PPO (MAPPO) can work more cooperatively. Finally, the simulation results demonstrate that our method can achieve better performance comparing with other existing works. [ABSTRACT FROM AUTHOR]

In this paper, we present a civil aircrafts augmented space-air-ground integrated networks (CAA-SAGIN) architecture, where each mobile user can access multiple platforms simultaneously. With the target of ensuring fairness, we put forward the fair optimization strategies including resource allocation and resource auction. In the resource allocation, we formulate a service-oriented fair (SOF) resource allocation problem in order to minimize the difference between the allocated and required data rate. After introducing convex-relaxation and logarithmic approximation, the joint optimal solutions of subchannel and power allocation can be obtained by SOF iteration algorithm. Aiming at managing the terrestrial network overload, we present a user update mechanism. After applying SOF resource allocation for selected original users in sky access platforms, we propose a resource auction scheme so as to maximize the utility for both sides. First, we decompose the original problem into several subproblems and transform them into their lower bound by first-order approximation. Then, we can obtain the joint optimal solution with an alternating descent iterative method. From simulation results, we can find that CAA-SAGIN can bring great benefits to traditional terrestrial networks. In addition, the proposed resource allocation and auction schemes are significant in terms of telecommunication and economic parameters. [ABSTRACT FROM AUTHOR]