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

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

Efficient spectrum management has always been an important issue due to the scarcity of satellite spectral resource, especially with the ever-increasing broadband demand. This paper proposes a market-driven technique to improve spectrum efficiency. In practice, spectrum resources are typically allocated in bulk to terrestrial agents which in turn will resell the bandwidth to end users of satellite communications. This paper explores the important role of terrestrial agents which serve as spectrum sales agents in satellite communications systems. The proposed approach aims to provide an incentive scheme for the agents to participate in the spectrum optimization process so as to result in maximizing the benefit of the agents as well as the satellite systems. We propose a dynamically optimal cooperation scheme between terrestrial agents and satellite systems, which is based on a stochastic process and optimal contract principle. By taking into consideration the satellite system's marginal cost related to transmission capacity in the given bandwidth, terrestrial agent's effort and the impact of market volatility, we designed an optimal incentive model which allows the satellite systems to determine a threshold value for paying sales commission to terrestrial agents or triggering contract termination if spectrum utilization is inefficient. Numerical results are presented to evaluate the performances of satellite systems’ profits in changing spectrum market and agency cost undertaken by satellite systems. [ABSTRACT FROM AUTHOR]

An editorial is presented on Cybertwin supporting advanced vehicle-to-everything (V2X) applications in the future intelligent and securing vehicular 6G network. Topics include becoming the personal AI assistant and acting as a security agent through customizing the visibility of recorded data; and proposing scheme good security performances in terms of symbol error rate (SER) and secrecy outage probability.

As more and more people choose high-speed rail (HSR) as a means of transportation for short trips, there is ever growing demand of high quality of multimedia services. With its rich spectrum resources, millimeter wave (mm-wave) communications can satisfy the high network capacity requirements for HSR. Also, it is possible for receivers (RXs) to be equipped with antenna arrays in mm-wave communication systems due to its short wavelength. However, as HSRs run with high speed, the received signal power (RSP) varies rapidly over a cell and it is the lowest at the edge of the cell compared to other locations. Consequently, it is necessary to conduct research on RX beamforming for HSR in mm-wave band to improve the quality of the received signal. In this paper, we focus on RX beamforming for a mm-wave train-ground communication system. To improve the RSP, we propose an effective RX beamforming scheme based on deep reinforcement learning (DRL), and develop a deep Q-network (DQN) algorithm to train and determine the optimal RX beam direction with the purpose of maximizing average RSP. Through extensive simulations, we demonstrate that the proposed scheme has better performance than the four baseline schemes in terms of average RSP at most positions on the railway. [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]

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]

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]

Uncertain cut-in maneuver of vehicles from adjacent lanes makes it difficult for vehicle's automatic speed control strategy to make judgments and effective control decisions. In this paper, an intelligent speed control strategy for uncertain cut-in scenarios is established based on a basic autonomous driving system. This strategy judges cut-in maneuver from surrounding vehicles and outputs adaptive control action under current environment according to Q value of state-action pair based on a Q network. In addition, according to the analysis of cut-in scenarios, the Q network is trained based on a novel reinforcement learning method named as experience screening deep Q-learning network (ES-DQN). The proposed ES-DQN is an extension of double deep Q-learning network (DDQN) algorithm, and includes two parts: experience screening and policy learning. Based on the experience screened from the experience screening part, the proposed learning method can train an intelligent speed control strategy which has stronger adaptability and control effect in uncertain cut-in scenarios. According to simulation results, the proposed intelligent speed control strategy trained by ES-DQN has better performance under uncertain cut-in scenarios than DDQN method and traditional ACC strategy. Meanwhile, by adjusting weight value in reward function, the system can realize different control target. [ABSTRACT FROM AUTHOR]

Cooperative Intelligent Transportation Systems (C-ITS) will change the modes of road safety and traffic management, especially at intersections without traffic lights, namely unsignalized intersections. Existing researches focus on vehicle control within a small area around an unsignalized intersection. This paper expands the control domain to a large area with multiple intersections. In particular, a Multi-intersection Vehicular Cooperative Control (MiVeCC) is proposed to enable cooperation among vehicles in a large area with multiple unsignalized intersections. Firstly, a vehicular end-edge-cloud computing framework is proposed to facilitate end-edge-cloud vertical cooperation and horizontal cooperation among vehicles. Under the framework, a two-stage reinforcement learning is implemented to obtain the optimal policy for vehicle control. To support RL in the proposed framework, a multi-vehicle state representation method and a safety-oriented value representation method are designed. The former structures the collected vehicle information, and the latter evaluates the traffic condition. A multi-intersection simulation platform is developed to evaluate the proposed scheme. Simulation results show that the proposed MiVeCC can improve travel efficiency at multiple intersections by up to 4.59 times without collision compared with benchmark methods. [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]

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]

Positioning technology is an important component of environmental perception and the basis of decision-making and motion control for intelligent vehicles. Current independent positioning methods inevitably have defects and poor environmental adaptability. To improve positioning accuracy and stability using low-cost sensors, a multi-sensor fusion positioning strategy for intelligent vehicles using global pose graph optimization is proposed in this paper. This strategy complements the advantages of multiple sensors to obtain global optimal positioning results, and it can cope with a variety of challenging working conditions. Considering the fast response of the inertial measurement unit (IMU) and the advantages of a camera without drift, this strategy realizes the tightly coupled fusion of vision and the IMU to obtain the optimal pose of visual-inertial odometry (VIO) by nonlinear optimization. Moreover, this strategy refines the modeling of the IMU, and proposes a keyframe selection strategy to deal with driving in a straight line without obvious feature changes, turning, and other driving conditions. Taking into account the versatility of the global positioning system (GPS) and the low cost of the network real-time kinematic (NRTK) positioning, the output of VIO and GPS/NRTK is adaptively fused through global pose graph optimization to maintain global consistency. Finally, the multi-sensor fusion positioning strategy is verified using the KITTI dataset on the ROS simulation platform. The results show that the multi-sensor fusion positioning strategy for intelligent vehicles has better accuracy and environmental adaptability at a lower cost than state-of-the-art strategies. [ABSTRACT FROM AUTHOR]

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

In this paper, an original method about heavy-vehicle rollover risk prediction is presented and validated experimentally. It is based on the calculation of the load transfer ratio (LTR), which depends on the estimated vertical forces using high-order sliding-mode (HOSM) observers. Previously, a tractor model is developed. The validation tests were carried out on an instrumented tractor rolling on the road at various speeds and lane-change maneuvers. Many scenarios have been experienced: driving tests in a straight line, a curve, and a zigzag line, and brake tests to emphasize the rollover phenomenon and its prediction to set off an alarm to the driver. In this paper, the vehicle dynamic parameters (masses, inertia, stiffness, etc.) and the static-force infrastructure characteristics (road profile, radius of curvature, longitudinal and lateral slopes, and skid resistance) are measured or calculated before the tests. [ABSTRACT FROM PUBLISHER]

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

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

The ongoing growth of Internet of Things (IoT) traffic increasesthe emergence of Multi-Access Edge Computing (MEC), which can serve IoT devices to process edge missions properly. The power supply for IoT devices is addressed by energy harvesting (EH) technology. However, the energy arrival model obeys the unknown energy arrival process. In addition, due to the dynamical changes of network state, it is crucial to study the access control in the MEC-based green IoT system. In this paper, we propose an MEC-based access control strategy to maximize the system utility for the green IoT system, considering the spectral efficiency and the successful access ratio from the perspective of operators and users respectively. Specifically, we model the access control problem as a Constrained Markov Decision Process (CMDP), and develop a novel centralized reward-based experience replay deep convolutional Q network algorithm (RCQN) to attain the optimal access control for EH IoT devices. Additionally, to enrich the prior knowledge of access control strategy, we design a Long Short-Term Memory (LSTM) based energy prediction module to predict the energy state of IoT devices by learning the historical energy arrival information, whose output is taken as the state information of RCQN. Simulation results demonstrate that the proposed LSTM-based RCQN access control algorithm significantly improves the system utility with a proper reward design and training mechanism. [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]

The heterogeneous fog radio access networks (Fog-RAN), the integration of fog computing, and traditional heterogeneous radio access networks can be implemented through the next-generation wireless communication networks. However, most of the solutions are limited to the spectrum efficiency optimization, and cross-tier interference existing in the fog access points (F-APs) could affect the network performance seriously. In this paper, the user association, resource allocation (including bandwidth and power), and caching deployment are investigated in the heterogeneous Fog-RAN to consider energy efficiency and cross-tier interference mitigation. Specifically, the user association, resource allocation, and caching strategy are formulated as a non-convex optimization problem and then transformed into a convex problem, which can be solved by a proposed algorithm based on the concept of the alternating direction method of multipliers (ADMM). Then an ADMM-based algorithm is proposed to enhance the energy efficiency of the Fog-RAN. Compared with the current solutions, simulation results illustrate the proposed algorithm’s convergence and effectiveness. [ABSTRACT FROM AUTHOR]

Indoor drone or Unmanned Aerial Vehicle (UAV) operations, automated or with pilot control, are an upcoming and exciting subset of drone use cases. Automated indoor flights tighten the requirements of stability and localization accuracy in comparison with the classic outdoor use cases which rely primarily on (RTK) GNSS for localization. In this paper the effect of multiple sensors on 3D indoor position accuracy is investigated using the flexible sensor fusion platform OASE. This evaluation is based on real-life drone flights in an industrial lab with mm-accurate ground truth measurements provided by motion capture cameras, allowing the evaluation of the sensors based on their deviation from the ground truth in 2D and 3D. The sensors under consideration for this research are: IMU, sonar, SLAM camera, ArUco markers and Ultra-Wideband (UWB) positioning with up to 6 anchors. The paper demonstrates that using this setup, the achievable 2D (3D) indoor localization error varies between 4.4 cm and 21 cm (4.9 cm and 67.2 cm) depending on the selected set of sensors. Furthermore, cost/accuracy trade-offs are included to indicate the relative importance of different sensor combinations depending on the (engineering) budget and use case. These lab results were validated in a Proof of Concept deployment of an inventory scanning drone with more than 10 flight hours in a 65 000 m2 warehouse. By combining lab results and real-life deployment experiences, different subsets of sensors are presented as a minimal viable solution for three different indoor use cases considering accuracy and cost: a large drone with little weight- and cost restrictions, one or more medium sized drones, and a swarm of weight and cost restricted nano drones. [ABSTRACT FROM AUTHOR]

The critical nature of vehicular communications requires their extensive testing and evaluation. Analytical models can represent an attractive and cost-effective approach for such evaluation if they can adequately model all underlying effects that impact the performance of vehicular communications. Several analytical models have been proposed to date to model vehicular communications based on the IEEE 802.11p (or DSRC) standard. However, existing models normally model in detail the MAC (Medium Access Control), and generally simplify the propagation and interference effects. This reduces their value as an alternative to evaluate the performance of vehicular communications. This paper addresses this gap, and presents new analytical models that accurately model the performance of vehicle-to-vehicle communications based on the IEEE 802.11p standard. The models jointly account for a detailed modeling of the propagation and interference effects, as well as the impact of the hidden terminal problem. The model quantifies the PDR (Packet Delivery Ratio) as a function of the distance between transmitter and receiver. The paper also presents new analytical models to quantify the probability of the four different types of packet errors in IEEE 802.11p. In addition, the paper presents the first analytical model capable to accurately estimate the Channel Busy Ratio (CBR) metric even under high channel load levels. All the analytical models are validated by means of simulation for a wide range of parameters, including traffic densities, packet transmission frequencies, transmission power levels, data rates and packet sizes. An implementation of the models is provided openly to facilitate their use by the community. [ABSTRACT FROM AUTHOR]

Mobile edge computing has been an important supplement for traditional cloud computing architecture to offer low-delay computing services to mobile users. However, it is in general impossible for edge service providers to overdeploy so much edge resources to satisfy the rapidly increasing while diverse user demands. In this paper, we study a mobile edge computing system consisting of a service platform, cloudlets joining the system, and mobile users. In this study, we focus on a profit-driven perspective such that the service platform purchases computation resource from the resource-rich cloudlets and makes profit by processing tasks from user side. The design objective is to maximize the platform profit subject to budget constraint and stringent delay requirements for task processing. We formulate this problem as a mixed integer programming problem. Due to the NP-hardness of the problem, we design a logic based Benders decomposition algorithm as the offline solution. We further study the scenario where the task arrivals from user side and resource availability at the cloudlets are both stochastic and unknown in advance. We accordingly propose a Multi-Armed Bandit learning based resource purchasing and greedy task scheduling algorithm for the online scenario. Simulations results show the high performance of our proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper considers the $\mathscr{L}_{2}$ string stability analysis and controller synthesis for vehicle platooning under jamming attacks and interferences from other communication channels. Jamming attacks and interferences for vehicle platooning systems are modeled as periodic denial of service attacks. Further, the vehicle platooning with denial of service attacks is modeled as a distributed switching system where an average dwell time approach is adopted for the stability analysis. Under the distributed switching system framework, this paper presents a co-design paradigm for the vehicle platooning. The distributed event-triggered method (ETM) and controllers are presented to guarantee $\mathscr{L}_{2}$ string stability, which can ensure that the output cannot be amplified along the vehicle platooning. Sufficient conditions for $\mathscr{L}_{2}$ string stability are then provided to guide the co-design of the ETM and controllers, which can be efficiently obtained by solving LMIs. Simulation results are presented to demonstrate the efficacy of the proposed co-design approach in addressing the denial of service attacks for vehicle platooning systems. [ABSTRACT FROM AUTHOR]

In this paper, we study the average throughput performance of energy beamforming in a multi-antenna wireless-powered communication network (WPCN). The considered network consists of one hybrid access point (AP) with multiple antennas and a single-antenna user. The user has no constant power supply and, thus, needs to harvest energy from the signals broadcast by the AP in the downlink (DL) before sending its data back to the AP with the harvested energy in the uplink (UL). We derive closed-form expressions of the average throughput and their asymptotic expressions at a high SNR for both delay-limited and delay-tolerant transmission modes. The optimal DL energy harvesting time, which maximizes the system throughput, is then obtained for high SNR. All analytical expressions are validated by numerical simulations. The impact of various parameters, such as the AP transmit power, the energy harvesting time, and the number of antennas, on the system throughput is also investigated. [ABSTRACT FROM AUTHOR]

A two-user Gaussian broadcast channel (GBC) with finite input constellations is addressed in this paper. The constellation-constrained capacity region for the two-user GBC by recently proposed bit-division multiplexing (BDM) is derived. BDM can be seen as a practical implementation of constellation-constrained nonlinear superposition coding (SC). For comparison, the constellation-constrained capacity region for the two-user GBC by traditional constellation-constrained linear SC is also computed. With similar implementation complexity, BDM outperforms traditional constellation-constrained linear SC with lower shaping loss in the capacity defined in this paper. Analytical results show that the upper boundary of the capacity region for the two-user GBC can be well approached by BDM with amplitude phase-shift keying modulation, which could be considered as the preliminary demonstration on the applicability of BDM in practical systems. [ABSTRACT FROM PUBLISHER]

The growing interest in concepts such as smart cities and smart mobility is giving more and more importance to place of interest (POI) information, which proves to be crucial in providing efficient and tailored location-based services (LBSs). Though plenty of solutions exist for recognizing indoor places, the literature lacks of approaches aimed at recognizing big outdoor places without the GPS employment. Even if GPS-based solutions assure great accuracy, they have a strong request in terms of energy necessary to achieve such result. As a consequence, if LBSs are thought on the move (e.g., mobile devices such as smartphones are used) energy consumption is a key constraint. This paper proposes a POI recognition algorithm called enhanced location recognition algorithm for automatic check-in applications (E-LRACI). It is an evolution of LRACI (Location Recognition Algorithm for automatic Check-In applications, originally reported in [I. Bisio, F. Lavagetto, M. Marchese, and A. Sciarrone, ''GPS/HPS-andwifi fingerprint-based location recognition for check-in applications over smartphones in cloud-based LBSS,'' IEEE Transactions on Multimedia, vol. 15, no. 4, pp. 858–869, Jun. 2013]) which aims at recognizing big outdoor places by only exploiting radio beacons emitted by WiFi access points. In terms of contributions, this paper first, proposes a novel fingerprint algorithm; second, solves the problem of big outdoor POI recognition without using GPS by leveraging the concept of spot; and third, compares the results obtained by E-LRACI and other reference works both in terms of recognition accuracy and computational complexity. The obtained numerical results, carried out on real data (acquired with Android-based smartphones), prove that E-LRACI provides the best results since it is able to guarantee the highest accuracy (95% versus, at most, 89%) at a lowest computational complexity with respect to the existing POI recognition algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a novel emulation system for emulating the power system in the fuel cell electric vehicle. The major purpose of the proposed emulation system is to test the dc–dc converter between the fuel cell and high-voltage powertrain. The bidirectional buck–boost type dc–dc converter is applied in the emulation system as the basic elements. A modularized approach is presented for easily scaling up and down the power level of the emulation system. Meanwhile, an energy loop structure is implemented in the proposed emulation system for power capability boosting purpose. Because of the proposed energy loop configuration, the emulation system offers long-term running capability with high power rating without the grid connection. A neutral current control mechanism is applied in this paper to further enhance the long-term testing capability as well as extending the lifespan for the battery packages. The simulation studies are proposed in this paper to validate the control mechanisms of the emulation system. A 10 kW hardware prototype has been built and the experimental tests are presented for validating the functionalities of the emulation system. [ABSTRACT FROM AUTHOR]

In this paper, for an overlay cognitive radio system in which the primary users’ information exchange is assisted by a secondary multiple-input multiple-output two-way relay network, we propose two spectrum sharing schemes that achieve the full diversity gain in both the primary and secondary networks. In the spectrum sharing problem that we formulate, there are two degrees of freedom to be optimized: relay selection and spectrum allocation. We jointly optimize these two degrees of freedom such that the achievable rate in the secondary network is maximized under the constraint that a minimum quality of service requirement in terms of the achievable rate is satisfied for the primary network. We analyze the performance of the proposed schemes in terms of the outage probability (that reflects the rate of unsuccessful spectrum sharing), the asymptotic behavior and the diversity order over Rayleigh fading channel. The analysis of the paper shows that in both of the proposed schemes, the full diversity of order $N M^2$ is achieved by the primary users, where $N$ is the number of the secondary users and $M$ is the number of antennas per terminal. Moreover, in both of the schemes, the full selection diversity of order $N M^2$ is guaranteed for the secondary network. We also provide extensive simulation results to confirm the theoretical analysis of this paper. [ABSTRACT FROM AUTHOR]

This correspondence paper studies the impact of nonidentically distributed channel estimation error on performance of a scheduled space-time block coding with mean-value power allocation in cognitive-radio multiple-input multiple-output system. Using the derived probability density function, the exact closed-form expressions of the proposed system for outage probability, ergodic capacity, and symbol-error rate (SER) with both $\rm M$ -ary quadrature amplitude modulation and phase shift keying modulation are derived. Using asymptotic analysis, this paper quantifies the diversity order for both outage probability and SER in the presence of nonidentically distributed error. From the analysis results, this paper shows that the diversity order is enhanced by the multiuser diversity as the number of user terminals increases regardless of outage probability and SER, while the spatial diversity is eliminated. [ABSTRACT FROM AUTHOR]

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

Lithium-ion batteries (LIBs) charging at low temperatures will easily accelerate the aging of LIBs and reduce the useful life. This paper applies advanced multi-factors coupling aging model and bi-objective particle swarm optimization (PSO) algorithm to derive suitable charging patterns for LIBs at low temperatures. Based on the results of orthogonal experiments, which consider the effects of charging rate, charging temperature and charging cut-off voltage on battery aging, the low-temperature capacity degradation model was established. Furthermore, two important yet competing charging objectives, including battery health and charging time, are taken into account simultaneously. These two conflicting charging objectives are traded off by solving a bi-objective optimization problem based on battery electrothermal and aging behavior. Combined with PSO algorithm, the optimal low-temperature charging strategy is obtained. As a result, the three-stage constant current and constant voltage (CC-CV) charging strategy is optimized to balance various combinations of charging objectives. Different tradeoffs are compared and analyzed based on the Pareto frontiers. The verification results show that the optimized three-stage CC-CV charging strategy can effectively offer feasible health-conscious charging with desirable tradeoffs among charging speed and battery health under low-temperature charging conditions. [ABSTRACT FROM AUTHOR]

Aiming at the low-frequency oscillation (LFO) phenomenon in high-speed railways, this paper proposes a feedback linearized virtual inertia control (VIC) strategy of the traction converter based on a sliding mode observer (SMO) to improve the robustness of the system and suppress the occurrence of LFO. First, to optimize the total harmonic distortion of the traction grid-side current, the inner loop controller is designed as the feedback linearization control. Then, the voltage outer loop is designed as a combination of the VIC designed in this paper and SMO, which enhances the system inertia and reduces the impact of external interference on the system, while reducing the controller's demand for electrical power on the DC-side. Furthermore, a small-signal model is established to analyze the stability of the single-phase vehicle-grid system under the proposed control. The influence of the control parameters on stability is discussed in detail. Finally, the comparison of software simulation and hardware-in-the-loop experimental results between traditional PI control, feedback linearization control, feedback+SMO, feedback+VIC and the proposed control method shows that the proposed control has better dynamic and static characteristics, and can effectively inhibit the occurrence of LFO. [ABSTRACT FROM AUTHOR]

Multiple signal classification (MUSIC) has been widely applied in multiple-input multiple-output (MIMO) receivers for direction-of-arrival (DOA) estimation. To reduce the cost of radio frequency (RF) chains at millimeter-wave bands, hybrid analog-digital structure has been adopted in massive MIMO transceivers. In this situation, received signals at the antennas are unavailable to the digital receiver, and as a consequence, the spatial covariance matrix (SCM), which is essential in MUSIC algorithm, cannot be obtained using traditional sample average approach. Based on our previous work, we propose a novel algorithm for SCM reconstruction in hybrid massive MIMO systems with multiple RF chains in this paper. By switching the analog beamformers to a group of predetermined DOAs, SCM can be reconstructed through the solutions of a set of linear equations. Furthermore, a low-complexity algorithm, as well as a careful selection of the predetermined DOAs, will be also presented in this paper. Simulation results show that the proposed algorithms can reconstruct the SCM accurately so that MUSIC algorithm can be well used in hybrid massive MIMO systems with multiple RF chains. [ABSTRACT FROM AUTHOR]

Even if massive multiple-input multiple-output (MIMO) can theoretically bring huge benefits, it incurs substantial hardware complexity and expensive hardware costs. To address these issues while maintaining the system performance simultaneously, we develop an asymmetrical transceiver architecture for massive MIMO systems in this paper by releasing the shackles on radio frequency (RF) chains. Specifically, we first develop the architecture for the asymmetrical transceiver where the number of receive RF chains is different from that of the transmit RF chains. Due to this unequal number of RF chains, channel inconsistency appears. To overcome the channel inconsistency and thus fully harness the system's downlink data transmission capability, we propose two uplink-to-downlink channel transfer algorithms. The cost and power consumption models for the asymmetrical transceiver are also developed and the uplink signal-to-noise loss due to the channel inconsistency is investigated. Through analyzing system spectral, cost, and energy efficiency, we demonstrate that the proposed asymmetrical transceiver-based massive MIMO system can achieve excellent downlink spectral efficiency while maintaining a reasonable energy efficiency. [ABSTRACT FROM AUTHOR]

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

In underwater acoustic (UWA) adaptive communication system, due to time-varying channel, the transmitter often has outdated channel state information (CSI), which results in low efficiency. UWA channels are much more difficult to estimate and predict than terrestrial wireless channels, given the more severe multipath environments with varying propagation speeds in different locations, non-linear propagation paths, several-order higher propagation latency, mobile transceiver and obstacles in the sea, etc. To handle the complexity, this paper proposes an efficient online CSI prediction model for UWA CSI prediction considering the complicated correlationship of UWA channels in both the time and frequency domains. This paper designs a learning model called CsiPreNet, which is an integration of a one-dimensional convolutional neural network (CNN) and a long short term memory (LSTM) network. The performance is compared with the widely used recursive least squares (RLS) predictor, the approximate linear dependency recursive kernel least-squares (ALD-KRLS), and two common conventional deep neural networks (DNN) predictors, i.e., back propagation neural network (BPNN) and LSTM network using the measured data recorded in the South China Sea. To validate the efficacy of prediction, we investigate the prediction of CSI in simulated downlink UWA orthogonal frequency division multiple access (OFDMA) systems. Specifically, the measured UWA channel is used in the OFDMA system. A joint subcarrier-bit-power adaptive allocation scheme is used for resource allocation. To further improve the performance, we develop an offline-online prediction scheme, enabling the prediction results to be more stable. Simulation and experimental results show that the CsiPreNet has superior performance than the existing solutions, thanks to its capability in capturing both the temporal and frequency correlation of the UWA CSIs. [ABSTRACT FROM AUTHOR]

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

This paper studies the allocation of shared resources between vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) links in vehicle-to-everything (V2X) communications. In existing algorithms, dynamic vehicular environments and quantization of continuous power become the bottlenecks for providing an effective and timely resource allocation policy. In this paper, we develop two algorithms to deal with these difficulties. First, we propose a deep reinforcement learning (DRL)-based resource allocation algorithm to improve the performance of both V2I and V2V links. Specifically, the algorithm uses deep Q-network (DQN) to solve the sub-band assignment and deep deterministic policy-gradient (DDPG) to solve the continuous power allocation problem. Second, we propose a meta-based DRL algorithm to enhance the fast adaptability of the resource allocation policy in the dynamic environment. Numerical results demonstrate that the proposed DRL-based algorithm can significantly improve the performance compared to the DQN-based algorithm that quantizes continuous power. In addition, the proposed meta-based DRL algorithm can achieve the required fast adaptation in the new environment with limited experiences. [ABSTRACT FROM AUTHOR]

In this paper, variable measurement interval is proposed for channel-adaptive random access with discontinuous channel measurement. After each channel measurement, the next measurement interval is adaptively determined based on previous channel measurement. Results show that receiver power consumption for channel measurement can be reduced with variable measurement interval under the same average transmission delay constraint compared with fixed measurement interval. [ABSTRACT FROM AUTHOR]

Many autonomous vehicles and advanced driver-assistance systems are equipped with front-facing cameras that detect and track objects using deep-learning-based algorithms. However, the localization capability of monocular cameras is often overlooked. In this paper, a novel method for estimating the pixel-wise error in a detected object's location versus its ground truth is proposed. As the object moves away from the camera, the pixel errors are shown to be normally distributed with unique spreads along the image's vertical axis (y-pixel). The pixel error appears to be smaller as objects get farther away, while at the same distance range, objects have similar error distribution across the camera's horizontal view. The horizontal axis (x-pixel) error appears to be smaller while the distance moves further away. However, the x-pixel location along a constant y-pixel row has no impact on the error distribution. The estimated x and y-pixel error distributions can in turn be used to form a spatial error distribution for finding the location of a detected object within a certain confidence interval. The spatial errors are then projected onto the world coordinate system using a camera transformation matrix to give a more realistic sense of what this error means. The results show that location estimation using monocular cameras generates an elliptical error distribution around the object with a larger error in the y-pixel direction compared to the x-pixel direction. This error distribution can be important to fuse information from multiple range-detecting sensors as well as multi-vehicle and multi-object tracking. The uncertainty characterization for position measurement, as demonstrated in this paper is an essential element of tracking and, is sensor and algorithm dependent. [ABSTRACT FROM AUTHOR]

Recently, research on path planning for the autonomous underwater vehicles (AUVs) has developed rapidly. Heuristic algorithms have been widely used to plan a path for AUV, but most traditional heuristic algorithms are facing two problems, one is slow convergence speed, the other is premature convergence. To solve the above problems, this paper proposes a new heuristic algorithms fusion, which improves the genetic algorithm with the ant colony optimization algorithm and the simulated annealing algorithm. In addition, to accelerate convergence and expand the search space of the algorithm, some algorithms like trying to cross, path self-smoothing and probability of genetic operation adjust adaptively are proposed. The advantages of the proposed algorithm are reflected through simulated comparative experiments. Besides, this paper proposes an ocean current model and a kinematics model to solve the problem of AUV path planning under the influence of ocean currents. [ABSTRACT FROM AUTHOR]

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

The use of unmanned aircraft systems (UAS) is expanding rapidly. For safe and reliable integration of UAS into the National Airspace System (NAS), control and nonpayload communication (CNPC) system requirements are being developed. The air-to-ground (AG) channel characteristics essentially determine CNPC link performance. The National Aeronautics and Space Administration (NASA) Glenn Research Center has sponsored a comprehensive AG channel measurement campaign in 2013 in both the L- and C-bands recently allocated for UAS. These measurements covered nearly all typical ground site (GS) local environments, including over water, hilly, mountainous, suburb, and near urban. As a continuation of results reported for the over-water scenarios, a description of the measurement campaign, measured results, and complete AG channel models for hilly and mountainous terrains are provided in this paper. The path loss is modeled by a modified log-distance path loss model with a correction for flight direction; path loss results are close to those of free space. A strong ground reflection was observed only for some small portions of some of the flight paths. The small-scale fading is well modeled by the Ricean distribution with a $K$-factor of 29.4 dB in the C-band and 12.8 dB in the L-band. The interband signals are uncorrelated. The spatial correlation for the two intraband channels with an aircraft antenna separation of 1.8 m is greater than 0.85. The C-band root-mean-square delay spread (RMS-DS) is 10 ns most of the time, with a maximum of 1 \mu\text{s} in hilly terrain and 180 ns in mountainous terrain. Tapped delay line (TDL) models accounting for the line of sight (LOS) and up to seven intermittent multipath components (MPCs) were developed, including the MPCs' probability of occurrence, relative power, phase, duration, and excess delay. [ABSTRACT FROM PUBLISHER]

For several years, the completion time and the decoding delay problems in instantly decodable network coding (IDNC) were considered separately and were thought to act completely against each other. Recently, some works have aimed to balance the effects of these two important IDNC metrics, but none of them studied a further optimization of one by controlling the other. This paper investigates the effect of controlling the decoding delay to reduce the completion time below its currently best known solution in both perfect and imperfect feedback with persistent erasure channels. To solve the problem, the decoding-delay-dependent expressions of the users' and overall completion times are derived in the complete feedback scenario. Although using such expressions to find the optimal overall completion time is NP-hard, this paper proposes two novel heuristics that minimize the probability of increasing the maximum of these decoding-delay-dependent completion time expressions after each transmission through a layered control of their decoding delays. Afterward, this paper extends the study to the imperfect feedback scenario, in which uncertainties at the sender affect its ability to anticipate accurately the decoding delay increase at each user. This paper formulates the problem in such an environment and derives the expression of the minimum increase in the completion time. Simulation results show the performance of the proposed solutions and suggest that both heuristics achieve a lower mean completion time, as compared with the best known heuristics for completion time reduction in perfect and imperfect feedback. The gap in performance becomes more significant as the erasure of the channel increases. [ABSTRACT FROM PUBLISHER]

This paper presents an offline state-of-health (SoH) estimation based on charge transfer resistance for high-power lithium-ion (Li-ion) batteries, such as lithium iron phosphate (LFP) batteries. As shown in the experimental results, the charge transfer resistance has a great aging change with battery degradation and good abilities against state-of-charge (SoC) drift and external resistance variation in the impedance parameter set of a single-time-constant equivalent circuit model (ECM), including ohmic resistance, charge transfer resistance, double-layer capacitance, and time constant, for SoH estimation. A fast and efficient three-point (TP) impedance extraction method is also proposed in this paper for accurately extracting the charge transfer resistance in offline SoH estimation. The results of long-term cycling test demonstrate that the TP impedance extraction method can successfully indicate the SoH of LFP batteries with low estimation error of 6.1%. [ABSTRACT FROM PUBLISHER]

This paper considers the problem of user association to small-cell base stations (SBSs) in a heterogeneous and small-cell network (HetSNet). Two optimization problems are investigated, namely, maximizing the set of associated users to the SBSs (the unweighted problem) and maximizing the set of weighted associated users to the SBSs (the weighted problem), under signal-to-interference-plus-noise ratio constraints. Both problems are formulated as linear integer programs. The weighted problem is known to be NP-hard, and in this paper, the unweighted problem is proved to be NP-hard as well. Therefore, this paper develops two heuristic polynomial-time algorithms to solve both problems. The computational complexity of the proposed algorithms is evaluated and is shown to be far more efficient than the complexity of the optimal brute-force (BF) algorithm. Moreover, this paper benchmarks the performance of the proposed algorithms against the BF algorithm, the branch-and-bound CPLEX-based algorithm, and state-of-the-art algorithms, through numerical simulations. The results demonstrate the close-to-optimal performance of the proposed algorithms. They also show that the weighted problem can be solved to provide solutions that are fair between users or to balance the load among SBSs. [ABSTRACT FROM PUBLISHER]

This paper analyzes the performance of the recently proposed quadrature spatial modulation (QSM) multiple-input–multiple-output (MIMO) system over Nakagami- $m$ fading channel. In the analysis, the general distribution of the Nakagami- m$ channel phase is considered. In the literature, performance analysis of spatial modulation (SM) over Nakagami- m$ channel with uniform phase is conducted. However, apart from the very special case of , where Nakagami- $m$ fading corresponds to Rayleigh fading, the phase of the Nakagami- $m$ distribution is not uniformly distributed. It is shown in this paper that the phase of the channel has major impact on the performance of spatial multiplexing MIMO systems such as SM and QSM systems. A general upper bound expression for the average bit error ratio (ABER) performance of QSM is derived, and the impact of different channel parameters is studied. Monte Carlo simulation results are provided to corroborate the exactness of the derived analysis. [ABSTRACT FROM PUBLISHER]

Source localization based on time-difference-of-arrival (TDOA) measurements from spatially separated sensors is an important problem in sensor networks. In this paper, we study the optimal geometries of a team of networked sensors for the TDOA-based localization problem, taking into account the a priori uncertainty of the source location. Two types of TDOA sensor pairing methods, namely, centralized and decentralized , are considered. Analytic solutions to the optimal sensor geometries in the ideal case are derived in both static and movable source cases. In the movable source case, sensor geometry design is investigated under an extended Kalman filter (EKF) framework. Furthermore, under sensor motion and communication constraints, we extend this paper to an optimal sensor path planning problem. Simulations demonstrate its enhancement to the source localization and tracking performance. [ABSTRACT FROM AUTHOR]