<![CDATA[ IEEE Transactions on Vehicular Technology - new TOC ]]>
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TOC Alert for Publication# 25 2018February 22<![CDATA[Table of contents]]>672C1C459<![CDATA[IEEE Vehicular Technology Society Information]]>672C2C2175<![CDATA[Editorial]]>67285185127<![CDATA[Adaptive Leader–Follower Formation Control of Underactuated Surface Vessels Under Asymmetric Range and Bearing Constraints]]>6728528651443<![CDATA[Development of Impact Indices for Performing Charging of a Large EV Population]]>6728668801867<![CDATA[An Optimization Model for Electric Vehicle Battery Charging at a Battery Swapping Station]]>6728818951231<![CDATA[Delay-Based Traffic Signal Control for Throughput Optimality and Fairness at an Isolated Intersection]]>6728969091140<![CDATA[Fault-Tolerant Direct Torque Control of Five-Phase FTFSCW-IPM Motor Based on Analogous Three-Phase SVPWM for Electric Vehicle Applications]]>6729109193585<![CDATA[Systematic Comparison of Active Balancing: A Model-Based Quantitative Analysis]]>6729209341169<![CDATA[Intermediate DC-Link Capacitor Reduction in a Two-Stage Cascaded AC/DC Converter for More Electric Aircrafts]]>6729359471673<![CDATA[Analysis and Design of Zero-Current Switching Switched-Capacitor Cell Balancing Circuit for Series-Connected Battery/Supercapacitor]]>6729489551133<![CDATA[Hybrid Lithium Iron Phosphate Battery and Lithium Titanate Battery Systems for Electric Buses]]>4Ti_{5}O_{12} (LTO) material anode is proposed. The configuration and control of the HBS are first studied, and a LFP battery degradation model is built. Simulation result indicates that the HBS can help us to mitigate LFP battery degradation. Then, the HBS is optimally sized for electric buses to achieve minimum cost. The daily bus operation and charging patterns as well as LFP battery degradation are considered. The optimal HBS has 10.7% and 19.3% lower total cost than the single LTO-battery and LFP-battery configurations, and has higher range flexibility than the single LTO-battery configuration.]]>6729569651934<![CDATA[An Integrated Dual-Output Isolated Converter for Plug-in Electric Vehicles]]>6729669761885<![CDATA[Predictive Brake Control for Electric Vehicles]]>6729779901612<![CDATA[Model Reference Adaptive Control for Hybrid Electric Vehicle With Dual Clutch Transmission Configurations]]>6729919991079<![CDATA[Experimental Validation of a Novel Architecture Based on a Dual-Stage Converter for Off-Board Fast Battery Chargers of Electric Vehicles]]>672100010112826<![CDATA[A Novel Approach for Model-Based Control of Smooth and Lossless Gear Shifts]]>672101210261083<![CDATA[Multiobjective Optimal Sizing of Hybrid Energy Storage System for Electric Vehicles]]>672102710351242<![CDATA[Development of a Bidirectional DC/DC Converter With Dual-Battery Energy Storage for Hybrid Electric Vehicle System]]>672103610523345<![CDATA[Design and Implementation of LTE RRM With Switched LWA Policies]]>672105310621456<![CDATA[Online Vehicle Front–Rear Distance Estimation With Urban Context-Aware Trajectories]]>front–rear distance information between vehicles is of great interest to drivers as such information can be utilized to improve driving safety. Acquiring such information based on systems such as the global positioning system (GPS) in urban settings is very challenging due to the high complexity of urban environments. In this paper, we propose a scheme, called relative urban positioning system (RUPS), to tackle the relative distance fixing problem. We first investigate the pervasive global system for mobile communication (GSM) signals and find that the received signal strength indicator measures of multiple GSM channels collected over a distance has ideal temporal–spatial characteristics for temporary fingerprinting. With this key insight, an RUPS-enabled vehicle first perceives the information of its GSM-aware trajectory while moving. Then, by exchanging and comparing its own trajectory with that of a neighboring vehicle, the vehicle can identify common locations overlapped between trajectories of itself and this neighbor. Finally, the relative distance between this pair of vehicles can be perceived by further comparing their geographical trajectories based on an identified common location. As a result, RUPS is a fully distributed and lightweight scheme, requiring only a minimum hardware deployment, and does not need synchronization between vehicles or any preconstructed signal maps. We implement a prototype system to validate the feasibility of the RUPS design. Extensive trace-driven simulation results show that RUPS can work stably under complex urban environments and overwhelm the performance of GPS by 2.7 times on average.]]>672106310741998<![CDATA[Exact Solution for Elliptic Localization in Distributed MIMO Radar Systems]]>67210751086862<![CDATA[Artificial Noise Assisted Secure Interference Networks With Wireless Power Transfer]]>67210871098987<![CDATA[A Robust and Efficient Algorithm for Coprime Array Adaptive Beamforming]]>67210991112784<![CDATA[Spatial Interpolation of Cyclostationary Test Statistics in Cognitive Radio Networks: Methods and Field Measurements]]>672111311291882<![CDATA[Downlink Spectral Efficiency of Distributed Massive MIMO Systems With Linear Beamforming Under Pilot Contamination]]>672113011451048<![CDATA[Real-Time Adaptively Regularized Compressive Sensing in Cognitive Radio Networks]]>672114611571014<![CDATA[A ZF-Based Precoding Scheme With Phase Noise Suppression for Massive MIMO Downlink Systems]]>672115811731198<![CDATA[Power Allocation for Massive MIMO Cognitive Radio Networks With Pilot Sharing Under SINR Requirements of Primary Users]]>67211741186995<![CDATA[Resource Optimization for Device-to-Device and Small Cell Uplink Communications Underlaying Cellular Networks]]>672118712011279<![CDATA[Cluster-Based Cooperative MIMO-OFDMA Cellular Networks: Scheduling and Resource Allocation]]>67212021216944<![CDATA[Low-Complexity MIMO Signal Detection Employing Multistream Constrained Search]]>a posteriori probability (MAP) estimation, MSCS applies discrete optimization to some streams and continuous optimization to others. First, the discrete optimization constrains some streams to a certain set of modulation symbols. The continuous optimization is then used to identify the optimal continuous values of the other streams under this constrained condition and quantizes the results. A signal candidate comprises the quantized optimal values and constrained streams. Multiple constraint patterns result in multiple signal candidates, and sphere decoding (SD) identifies the detected signal as the one that maximizes the likelihood function. Limiting the number of constrained streams and applying SD can reduce computational complexity. In addition, MSCS selects constrained streams that have large variances of Gaussian distributions obtained from the MAP estimation. As a result of this stream selection method, MSCS can maintain an excellent BER performance even when the constrained streams are few in number. Computer simulations in 8-by-8 MIMO channels with modulation schemes of 16- and 64-QAM demonstrate that MSCS suffers degradation of merely 0.2 dB in average BER performance compared to the maximum likelihood detection (MLD). We also show that MSCS can achieve a similar average BER performance as that of the QR decomposition and M algorithm (QRM-MLD) while requiring less computational complexity. Under uncorrelated channels, the complexity of MSCS is less than 1/2 and 1/4 that of QRM-MLD in the case of 16- and 64-QAM, respectively. By contrast, simulations under channels with transmit-side spatial correlation show that when 16-QAM is used and average <-
nline-formula>$E_b/N_0$ is equal to 18 dB, the complexity of MSCS ranges from 3/10 to 2/5 that of QRM-MLD. We also show that the complexity of MSCS ranges from 1/10 to 1/6 that of QRM-MLD when 64-QAM is used and the average $E_b/N_0$ is 23 dB.]]>672121712302087<![CDATA[Adaptive Pilot Patterns for CA-OFDM Systems in Nonstationary Wireless Channels]]>672123112441757<![CDATA[Generalized Buffer-State-Based Relay Selection With Collaborative Beamforming]]>672124512571460<![CDATA[Design and Analysis of Relay-Selection Strategies for Two-Way Relay Network-Coded DCSK Systems]]>672125812711015<![CDATA[Impact of CSI Uncertainty on MCIK-OFDM: Tight Closed-Form Symbol Error Probability Analysis]]>$M$ -ary modulation size is large or even outperforms the ML under certain CSI conditions. Finally, the theoretical and asymptotic analysis are verified via simulation results, obtaining the high accuracy of the derived SEP.]]>672127212791527<![CDATA[A Joint Update Parallel MCMC-Method-Based Sparse Code Multiple Access Decoder]]>$%$ computation complexity compared to the existing decoding method with a codebook size 16, which only has 0.5-dB performance loss compared to the maximum-likelihood-like decoding algorithm.]]>672128012911638<![CDATA[Reducing the Impact of Handovers in Ground-to-Train Free Space Optical Communications]]>67212921301801<![CDATA[A Decentralized Approach for Self-Coexistence Among Heterogeneous Networks in TVWS]]>67213021312612<![CDATA[Robust Transceiver Design in Full-Duplex MIMO Cognitive Radios]]>67213131330956<![CDATA[Wireless Powered Cooperative Jamming for Secure OFDM System]]>a priori known) jamming signals, respectively. For both types of receivers, we maximize the secrecy rate at the destination by jointly optimizing the transmit power allocation at the source and the jammer over sub-carriers, as well as the time allocation between the two time slots. First, we present the globally optimal solution to this problem via the Lagrange dual method, which, however, is of high implementation complexity. Next, to balance the tradeoff between the algorithm complexity and performance, we propose alternative low-complexity solutions based on the minorization maximization and heuristic successive optimization, respectively. Simulation results show that the proposed approaches significantly improve the secrecy rate, as compared to benchmark schemes without joint power and time allocation.]]>67213311346892<![CDATA[Interference Modeling in an Urban Microcell With Full-Dimensional MIMO]]>67213471362865<![CDATA[Channel Matrix Sparsity With Imperfect Channel State Information in Cloud Radio Access Networks]]>67213631374848<![CDATA[Transmitter-Precoding-Aided Spatial Modulation Achieving Both Transmit and Receive Diversity]]>ratio-threshold-test-assisted maximum likelihood detector. We address the principles, characteristics, complexity, and performance of these detection algorithms. Furthermore, we analyze the average bit error probability of the TZF- and TMMSE-assisted TRD-PSM systems employing, respectively, the JMLD and the simplified JMLD and at both small and large scales. Finally, numerical and simulation results are provided to demonstrate and compare the achievable performance of TRD-PSM systems employing various precoding and detection algorithms, as well as to validate the formulas derived.]]>672137513881131<![CDATA[Atomic Norm Denoising-Based Joint Channel Estimation and Faulty Antenna Detection for Massive MIMO]]>a priori knowledge on the indices of faulty antennas. This motivates us to propose the approach for simultaneous channel estimation and faulty antenna detection. By exploiting the fact that the degrees of freedom of the physical channel matrix are smaller than the number of free parameters, the channel estimation and faulty antenna detection can be formulated as an extended atomic norm denoising problem and solved efficiently via the alternating direction method of multipliers. Furthermore, we improve the computational efficiency by proposing a fast algorithm and show that it is a good approximation of the corresponding extended atomic norm minimization method. Numerical simulations are provided to compare the performances of the proposed algorithms with several existing approaches and demonstrate the performance gains of detecting the indices of faulty antennas.]]>67213891403741<![CDATA[Forward Collision Vehicular Radar With IEEE 802.11: Feasibility Demonstration Through Measurements]]>$20,text{MHz}$). This indicates significant potential for industrial devices with joint vehicular communications and radar capabilities.]]>672140414161391<![CDATA[Opportunistic Beamforming Using Dumb Basis Patterns in Cognitive Multiple Access Channels]]>$K$ interference channels and $N$ secondary users, it is shown that the secondary network sum capacity scales like $log ({(K^{2}+K)}/{mathcal{W}({K e^{K}}/{N})})$ , where $mathcal{W}(.)$ is the Lambert-W function. Thus, LoS interference hinders the achievable multiuser diversity gain experienced in Rayleigh interference channels, where the sum capacity grows like $log (N)$. To overcome this problem, we propose the usage of a single radio Electronically Steerable Parasitic Array Radiator antenna at each of the secondary mobile terminals. These antennas will be used to induce artificial fluctuations in the interference channels to restore the $log (N)$ growth rate by assigning random weights to orthogonal basis patterns. This technique will be referred to as random aerial beamforming (RAB). It is also shown that by using RAB, one can actually exploit LoS interference to improve multiuser interference diversity by boosting the effective number of users with minimal hardware complexity.]]>67214171427846<![CDATA[Preamble Sequence Design for Spectral Compactness and Initial Synchronization in OFDM]]>672142814431240<![CDATA[A Comparative Study of PMI/RI Selection Schemes for LTE/LTEA Systems]]>672144414532052<![CDATA[Hybrid-Domain Parallel Decision Feedback Equalization for Single-Carrier Block Transmission]]>672145414691574<![CDATA[Distributed Uplink Reception in Cloud Radio Access Networks: A Linear Coding Approach]]>$L$ antenna terminals (ATs) are connected to a central process (CP) via digital error-free links of a finite-capacity $R_0$, and serves $K$ user terminals. In this network, novel low-complexity detection methods at the CP are proposed by incorporating a lattice-quantize-and-forward (LQF) framework, which converts the C-RAN into an equivalent finite-field multiple-input-multiple-output (FF-MIMO) channel. In particular, under this equivalent FF single-input-multiple-output channel, an optimal receive combining method is presented by using a simple repetition code. In addition, using linear block codes, a low-complexity detection method is presented for the equivalent FF-MIMO channel. Finally, by simulations, it is demonstrated that the proposed detection method combined with the LQF framework provides high achievable sum rates for uplink C-RANs especially when a lot of low-cost ATs are deployed.]]>67214701481711<![CDATA[Cognitive Tropospheric Scatter Communication]]>67214821491831<![CDATA[Performance of Adaptive Link Selection With Buffer-Aided Relays in Underlay Cognitive Networks]]>672149215091796<![CDATA[Virtual Multiantenna Array for Estimating the Direction of a Transmitter: System, Bounds, and Experimental Results]]>a priori; and 2) the local oscillator (LO) frequency offset between the transmitter and the receiver adds a phase offset to the signal received by each antenna of the virtual array, which must be estimated and compensated. The first problem is solved by using an inertial measurement unit, which can provide the relative position of the receiver for short time durations. The second problem is solved by estimating the LO frequency offset jointly with the direction of the transmitter by extending the MUSIC algorithm for multidimensional estimation. We investigate the Cramér–Rao lower bound of the proposed estimator, which provides some insights in the design of our system. We implement our system on a software-defined radio testbed and present some measurement results obtained in a controlled environment.]]>672151015202326<![CDATA[DPSAF: Forward Prediction Based Dynamic Packet Scheduling and Adjusting With Feedback for Multipath TCP in Lossy Heterogeneous Networks]]>672152115342069<![CDATA[Mobile Crowdsensing Games in Vehicular Networks]]>672153515451210<![CDATA[Capacity of Cooperative Vehicular Networks With Infrastructure Support: Multiuser Case]]>672154615601478<![CDATA[Approximate and Sublinear Spatial Queries for Large-Scale Vehicle Networks]]>672156115691356<![CDATA[Cluster-Oriented Device-to-Device Multimedia Communications: Joint Power, Bandwidth, and Link Selection Optimization]]>67215701581701<![CDATA[Road-to-Vehicle Communications With Time-Dependent Anonymity: A Lightweight Construction and Its Experimental Results]]>et al. (“Balanced trustworthiness, safety, and privacy in vehicle-to-vehicle communications,” IEEE Trans. Veh. Technol., vol. 59, 2010), we can show an attack against the scheme where anyone can forge a valid group signature without using a secret key. In contrast, our GS-TDL scheme is provably secure. In addition to the time-dependent linking property, our GS-TDL scheme supports verifier-local revocation, where a signer (vehicle) is not involved in the revocation procedure. It is particularly worth noting that no secret key or certificate of a signer (vehicle) must be updated, whereas the security cre-
ential management system must update certificates frequently for vehicle privacy. Moreover, our technique maintains constant signing and verification costs by using the linkable part of signatures. This might be of independent interest.]]>67215821597929<![CDATA[A Sensor-Assisted Emergency Guiding System: Sensor-Centric or User-Centric?]]>sensor-centric guiding problem and the user-centric guiding problem. The former is to find a guiding direction for each single sensor for evacuating people nearby, whereas the latter is to find a personalized guiding direction for each individual user. In other words, the number of guiding directions provided by each single sensor in the sensor-centric guiding problem is limited, whereas this constraint is relaxed in the user-centric guiding problem. This paper proves that the sensor-centric guiding problem is NP-hard. When the constraint is relaxed, a user-centric guiding system is more efficient. Then, this paper designs a localized user-centric guiding protocol, which allows each sensor to provide more than one guiding direction for personalized guiding. The key idea of the proposed protocol is to estimate the evacuation time in a distributed manner. Extensive simulation results show that the proposed solution can significantly reduce the evacuation time by $15%$ compared to typical sensor-centric approaches. Finally, the implemented prototype indicates that proposed protocol is lightweight and can work with real sensor platforms.]]>672159816111674<![CDATA[Adaptive Cell Zooming and Sleeping for Green Heterogeneous Ultradense Networks]]>67216121621942<![CDATA[Equilibriums in the Mobile-Virtual-Network-Operator-Oriented Data Offloading]]>672162216341305<![CDATA[Formation of Cognitive Personal Area Networks (CPANs) Using Probabilistic Rendezvous]]>672163516481395<![CDATA[NASH: Navigation-Assisted Seamless Handover Scheme for Smart Car in Ultradense Networks]]>672164916591383<![CDATA[Resource Allocation in Public Safety Broadband Networks With Rapid-Deployment Access Points]]>67216601671814<![CDATA[AC-POCA: Anticoordination Game Based Partially Overlapping Channels Assignment in Combined UAV and D2D-Based Networks]]>67216721683834<![CDATA[Resource Allocation for Green Cloud Radio Access Networks With Hybrid Energy Supplies]]>672168416971439<![CDATA[Efficiency of Power Ramping During Random Access in LTE]]>67216981712774<![CDATA[Finite-State Markov Channel Based Modeling of RF Energy Harvesting Systems]]>67217131725923<![CDATA[Non-interactive Identity-Based Underwater Data Transmission With Anonymity and Zero Knowledge]]>67217261739483<![CDATA[Empirical Distribution of Nearest-Transmitter Distance in Wireless Networks Modeled by Matérn Hard Core Point Processes]]>67217401749918<![CDATA[Joint Listening, Probing, and Transmission Strategies for the Frame-Based Equipment in Unlicensed Spectrum]]>nominal throughput optimal transmission that takes into account transmission costs such as power consumption. We find that the nominal throughput optimal rule is a pure threshold policy: The FBE should stop listening and transmit once the channel quality exceeds an optimized threshold. The optimal threshold can be found by solving a fixed point equation, but the fixed point equation in general does not admit a closed-form solution. We then derive a lower bound and an upper bound on the optimal threshold. We further devise an iterative algorithm with convergence analysis to compute the optimal threshold. Our results shed further light on LBT strategies for radio equipment operating in unlicensed spectrum.]]>67217501764805<![CDATA[Dual-Side Optimization for Cost-Delay Tradeoff in Mobile Edge Computing]]>672176517811157<![CDATA[Two-Phase Task Scheduling in Data Relay Satellite Systems]]>672178217931824<![CDATA[Virtual Resource Allocation for Heterogeneous Services in Full Duplex-Enabled SCNs With Mobile Edge Computing and Caching]]>672179418081322<![CDATA[A Noncoherent Multiuser Large-Scale SIMO System Relying on M-Ary DPSK and BICM-ID]]>67218091814680<![CDATA[ASER Analysis of Hexagonal and Rectangular QAM Schemes in Multiple-Relay Networks]]>$m$ fading channels with an integer-valued fading parameter using a well-known cumulative-distribution-function-based approach. Furthermore, ASER expressions for 32-cross QAM, differentially encoded quadriphase shift keying (QPSK), and $pi /4$-QPSK modulation schemes are derived for the considered systems. The asymptotic ASER expression is also derived for the RQAM scheme, which is useful to examine system's diversity order. Numerically evaluated results are verified by Monte Carlo simulation.]]>67218151819386<![CDATA[Interference Management for In-Band Full-Duplex Vehicular Access Networks]]>67218201824371<![CDATA[A Preamble Collision Resolution Scheme via Tagged Preambles for Cellular IoT/M2M Communications]]>67218251829614<![CDATA[Comparison Study Between PD-NOMA and SCMA]]>67218301834372<![CDATA[Reduced Latency ML Polar Decoding via Multiple Sphere-Decoding Tree Searches]]>67218351839801<![CDATA[Receiver Energy Efficiency and Resolution Profile Design for Massive MIMO Uplink With Mixed ADC]]>67218401844432<![CDATA[Wireless Energy Transfer Enabled D2D in Underlaying Cellular Networks]]>67218451849522<![CDATA[Become a published author in 4 to 6 weeks]]>67218501850893<![CDATA[Introducing IEEE Collabratec]]>672185118511860<![CDATA[Introducing IEEE Collabratec]]>672185218523151<![CDATA[IEEE Vehicular Technology Society]]>672C3C3156