<![CDATA[ IEEE Transactions on Vehicular Technology - new TOC ]]>
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TOC Alert for Publication# 25 2017May 22<![CDATA[Table of Contents]]>665C1C460<![CDATA[IEEE Vehicular Technology Society Information]]>665C2C2126<![CDATA[Determination of Cell Coverage Area and its Applications in High-Speed Railway Environments]]>66535153525937<![CDATA[In-Vehicle Channel Measurement, Characterization, and Spatial Consistency Comparison of $text{30}hbox{--}text{11 GHz}$ and $text{55}hbox{--}text{65 GHz}$ Frequency Bands]]>$text{3}hbox{--}text{11 GHz}$ and the $text{55}hbox{--}text{65 GHz}$ frequency bands under similar conditions. By spatially averaging channel impulse response realizations within a $10times 10$ grid, we obtain the power-delay profile (PDP). The data measured at $text{3}hbox{--}text{11 GHz}$ and $text{55}hbox{--}text{65 GHz}$ exhibit significant differences in terms of root mean square (RMS) delay spread, number of resolvable clusters, and variance of the maximal excess delay. Moreover, we evaluate the spatial stationarity via the Pearson correlation coefficient and via the PDP collinearity, depending on the distance in the grid. The measured and calculated results indicate that a strong reverberation inside the vehicle produces similar PDPs within the range of approximately ten wavelengths. We also provide a linear piecewise model of the PDP in logarithmic scale and a generalized extreme value model of a small-scale signal fading. Our channel model is validated utilizing the Kolmogorov–Smirnov test.]]>665352635373511<![CDATA[5-GHz V2V Channel Characteristics for Parking Garages]]>K-factor, and root-mean-square delay spread (RMS-DS) are quantified. Statistical tapped delay line models with Markov chains that describe the birth/death of multipath components are also provided. Our results provide channel impulse responses for in-garage driving tests, allowing the performance of V2V communication systems in parking garages to be evaluated or simulated based upon the channel models provided in this paper.]]>665353835471567<![CDATA[Higher Order Modes: A Solution for High Gain, Wide Band Patch Antennas for Different Vehicular Applications]]>66535483554706<![CDATA[Measurement and Modeling of Angular Spreads of Three-Dimensional Urban Street Radio Channels]]>Quasi-LOS path exists. The 2-D arrival profiles of the ray clusters have been observed, and their impacts on the angular spreads are analyzed in different propagation environments. By comparing multiple candidate fitting functions, the lognormal distribution models for ASA and ESA are proposed. In addition, the channel delay spread (DS) was also measured along the streets and positive correlations among ASA, ESA, and DS have been found. This work can help to est-
blish the 3-D spatial channel models for advanced MIMO technologies and is also valuable for future channel measurements.]]>665355535701524<![CDATA[A Real-Time Markov Chain Driver Model for Tracked Vehicles and Its Validation: Its Adaptability via Stochastic Dynamic Programming]]>a priori, stochastic methods, such as a Markov chain driver model (MCDM), must be employed. For tracked vehicles, the steering power, which is related to vehicle angular velocity, is a significant component of the driver demand. In this paper, a three-dimensional (3-D) MCDM incorporating angular velocity for a tracked vehicle is proposed. Based on the nearest-neighborhood method, an online transition probability matrix (TPM)-updating algorithm is implemented for the 3-D MCDM. Simulation results show that the TPM is able to update online and adapt to the changing driving conditions. Moreover, the adaptability of the online TPM updating algorithm to the change in driving is validated via a stochastic dynamic programming approach for a series hybrid tracked vehicle. Results show that the online updating for the MCDM's TPM is competent for adapting to the changing driving conditions.]]>665357135822169<![CDATA[A Framework of Vehicle Trajectory Replanning in Lane Exchanging With Considerations of Driver Characteristics]]>665358335961475<![CDATA[Design of Pedestrian Target Selection With Funnel Map for Pedestrian AEB System]]>665359736091259<![CDATA[Edge Position Detection of On-line Charged Vehicles With Segmental Wireless Power Supply]]>665361036211857<![CDATA[Fuzzy Load Modeling of Plug-in Electric Vehicles for Optimal Storage and DG Planning in Active Distribution Network]]>66536223631911<![CDATA[Nonlinear Model Predictive Control for Thermal Management in Plug-in Hybrid Electric Vehicles]]>$^circ$C. For one of the six cycles, an NMPC software-in-the loop (SIL) is presented, where the models inside the controller and for the controlled plant are the same. This simulation is compared with the finite-state machine-based strategy performed in the real vehicle. The results show that NMPC keeps the battery at healthier temperatures and reduces the cooling electrical consumption by more than 5%. In terms of the objective function, which is an accumulated and weighted sum of the two goals, this improvement amounts to 30%. Finally, the online SIL presented in this pap-
r suggests that the used optimizer is fast enough for a future implementation in the vehicle.]]>665363236441403<![CDATA[Voltage Control for Enhanced Power Electronic Efficiency in Series Hybrid Electric Vehicles]]>665364536584154<![CDATA[Measuring Vehicle Velocity in Real Time Using Modulated Motion Blur of Camera Image Data]]>665365936732822<![CDATA[Reinforcement Learning-Based Plug-in Electric Vehicle Charging With Forecasted Price]]>66536743684602<![CDATA[Active Steering Actuator Fault Detection for an Automatically-Steered Electric Ground Vehicle]]>$mathcal {H}{_}$ performance, and the $mathcal {H}_{infty }$ performance of the augmented system are all exploited. Based on the analysis results, the mixed $mathcal {H}{_}$/$mathcal {H}_{infty }$ fault detector design method is developed. An experimental test is used to show the performance of the designed fault detector.]]>66536853702997<![CDATA[Analysis, Prototyping, and Experimental Characterization of an Adaptive Hybrid Electromagnetic Damper for Automotive Suspension Systems]]>$cdot$s/m.]]>665370337131213<![CDATA[Active Vehicle Battery Equalization Scheme in the Condition of Constant-Voltage/Current Charging and Discharging]]>665371437231425<![CDATA[An Efficient Regenerative Braking System Based on Battery/Supercapacitor for Electric, Hybrid, and Plug-In Hybrid Electric Vehicles With BLDC Motor]]>665372437381504<![CDATA[Calibration of Nonlinear Crosstalk in MIMO Transmitter]]>665373937481260<![CDATA[Optimization of Scalable Broadcast for a Large Number of Antennas]]>665374937642308<![CDATA[Low-Complexity OSIC Equalization for OFDM-Based Vehicular Communications]]>665376537761401<![CDATA[On Joint Pareto Frontier in Multiple Access and Relay Rate Regions With Rayleigh Fading]]>66537773786511<![CDATA[Channel Correlation Modeling and its Application to Massive MIMO Channel Feedback Reduction]]>correlation factors, channel variance, and channel delay profile. The closed-form expression enables a principal component analysis (PCA)-based compression of channel state information (CSI), which allows the feedback overhead to be efficiently reduced. We also analyze the compression feedback error, bit-error-rate (BER) performance, and the spectral efficiency (SE) of the system using the PCA-based compression. Under our proposed model, numerical results verify that the PCA-based compression method significantly reduces the feedback overhead of the massive MIMO systems with marginal performance degradation from full-CSI feedback. Furthermore, we propose a new design framework by numerically showing that there exists the optimal number of transmit antennas in terms of SE for a given limited feedback amount.]]>66537873797575<![CDATA[Three-Dimensional Angle of Arrival Estimation in Dynamic Indoor Terahertz Channels Using a Forward–Backward Algorithm]]>a priori information to provide a more precise estimate than using the likelihood alone. An indoor human movement model is developed to generate the realistic application scenario and obtain the statistical transition probabilities. The forward–backward algorithm is implemented to carry out the Bayesian inference. The algorithm performance is illustrated using the channel models generated by a ray launching simulator. The background log-likelihood is suggested to adapt the algorithm to the instant channel state change in a multipath environment.]]>665379838112063<![CDATA[Inverse Moment Matching Based Analysis of Cooperative HARQ-IR Over Time-Correlated Nakagami Fading Channels]]>$rho$, as long as $rho <1$, and full diversity from both spatial and time domains can be achieved by cooperative HARQ-IR under time-correlated fading channels. The accuracy of the analytical results is verified by computer simulations, and the results reveal that cooperative HARQ-IR scheme can benefit from high fading order and low channel time correlation. Optimal rate selection to maximize the long-term average throughput given a maximum allowable outage probability is finally discussed as one application of the analytical results.]]>66538123828876<![CDATA[Mutual Interference Analysis of FBMC-Based Return Channel for Bidirectional T-DMB System]]>${text 10}^-{text 2}$ ( $10^-{text 4}$). Consequently, the additional throughput above can be achieved in a given resource without the adverse effect to the legacy systems.]]>665382938422237<![CDATA[Open-Loop Precoder Design for Spatial Multiplexing Systems in Transmit Correlated MIMO Channels]]>665384338551535<![CDATA[Antenna Selection in RF-Chain-Limited MIMO Interference Networks Under Interference Alignment]]>66538563870921<![CDATA[Hybrid Spatio-Temporal Artificial Noise Design for Secure MIMOME-OFDM Systems]]>66538713886645<![CDATA[On Secrecy Rate and Optimal Power Allocation of the Full-Duplex Amplify-and-Forward Relay Wire-Tap Channel]]>]]>66538873899988<![CDATA[Robust MMSE Beamforming for Multiantenna Relay Networks]]>66539003912570<![CDATA[On the Performance of HARQ-IR Over Nakagami-m Fading Channels in Mobile Ad Hoc Networks]]>$lambda$ when the network is sparse, while it follows the $(1 - frac{alpha }{2})$ power law over the network density where $alpha$ is the path loss exponent when the network is dense.]]>66539133929677<![CDATA[Security-Reliability Tradeoff Analysis of Artificial Noise Aided Two-Way Opportunistic Relay Selection]]>66539303941870<![CDATA[FBMC System: An Insight Into Doubly Dispersive Channel Impact]]>66539423956848<![CDATA[On the Outage Probability of MIMO Full-Duplex Relaying: Impact of Antenna Correlation and Imperfect CSI]]>$text{min}(N_{R}-1, N_{T})$ , where $N_{R}$ and $N_{T}$ are the number of relay receive and transmit antennas, respectively. Numerical results sustained by Monte Carlo simulations show the exactness and tightness of the proposed closed-form exact and lower bound expressions, respectively. In addition, it is seen that the outage probability performance of FD relaying outperforms that of the conventional half-duplex (HD) relaying at low to medium signal-to-noise ratio (SNR). However, at high SNR, the performance of HD relaying outperforms that of the FD relaying. Furthermore, in the presence of channel estimation errors, an outage probability error floor is seen at high SNR. Therefore, for optimum outage performance, hybr-
d relaying modes that switches between HD and FD relaying modes is proposed.]]>66539573965621<![CDATA[An Outage Performance Analysis With Moving Relays on Suburban Trains for Uplink]]>66539663975522<![CDATA[The Feasibility of Mobile Physical-Layer Network Coding with BPSK Modulation]]>665397639901217<![CDATA[An Efficient Precoder Design for Multiuser MIMO Cognitive Radio Networks With Interference Constraints]]>66539914004947<![CDATA[Uplink HARQ for Cloud RAN via Separation of Control and Data Planes]]>separation of control and data planes, in which retransmission control decisions are made at the edge of the network, that is, by the RRHs or user equipment (UE), while data decoding is carried out remotely at the BBUs. This solution enables low-latency local retransmission decisions to be made at the RRHs or UE, which are not subject to the fronthaul latency constraints, while, at the same time, leveraging the decoding capability of the BBUs. A system with a BBU Hoteling system is considered first in which each RRH has a dedicated BBU in the cloud. For this system, the control-data separation leverages low-latency local feedback from an RRH to drive the HARQ process of a given UE. The throughput and the probability of error of this solution are analyzed for the three standard HARQ modes of Type-I, chase combining, and incremental redundancy over a general fading multiple-input multiple-output (MIMO) link. Then, novel user-centric low-latency feedback strategies are proposed and analyzed for the C-RAN architecture, with a single centralized BBU, based on limited “hard” or “soft” local feedback from the RRHs to the UE and on retransmission decisions taken at the UE. The analysis presented in this paper allows the optimization of the considered schemes, as well as the investigation of the impact of system parameters such as HARQ protocol type, blocklength, and number of antennas on the performance of low-latency local HARQ decisions in BBU Hoteling and C-RAN architectures.]]>66540054016730<![CDATA[Jointly Optimized Reed–Muller Codes for Multilevel Multirelay Coded-Cooperative VANETS]]>design criteria and an efficient algorithm for proper bit selection at the relay nodes to achieve the best possible code at the destination. It is observed that the increase in the number of levels as well as relays result in better channel code at the destination, as compared to the lesser number of relays, however, at the cost of increased decoding complexity. The channels considered to analyze the bit error rate (BER) performances of proposed coded-cooperative schemes are fast and slow Rayleigh fading channels. At the destination, soft decision maximum likelihood decoding is employed. Numerical simulations show that the single-relay RM coded-cooperative scheme provides significant BER performance gains over the noncooperative and state-of-the-art distributed turbo coded-cooperative schemes under identical conditions.]]>665401740281335<![CDATA[On the Scaling Behavior of the Average Rate Performance of Large-Scale Distributed MIMO Systems]]>$N_c$ and the number of user antennas $N_c$ go to infinity with $N/N_crightarrow eta$, asymptotic lower-bounds of the average per-antenna capacities with and without channel state information at the transmitter side (CSIT) in the single-user case are characterized as an explicit function of the ratio $eta$ and the number of BS antenna clusters $L$. Simulation results verify that the average per-antenna capacities with and without CSIT logarithmically increase with $L$ in the orders of $Theta (frac{alpha }{2}log _2 L)$ and $Theta ((frac{alpha }{2}-1)log _2 L)$, respectively, where $alpha >text{2}$ is the path-loss factor. The analysis is further extended to the multiuser case with $K$ uniformly distributed users. By assuming that -
inline-formula>$N,N_crightarrow infty$ with $N/N_crightarrow eta$, an asymptotic lower-bound of the average per-antenna rate with block diagonalization (BD) is derived. Simulation results verify that the average per-antenna rate scales in the order of $Theta(log _2 frac{lfloor L-eta (K-1)rfloor ^{alpha /2}}{K})$ if the ratio $eta$ is fixed. The effect of the cluster size on the average rate performance is further analyzed. Simulation results verify that for a given number of BS antennas, the average per-antenna capacities with and without CSIT in the single-user case and the average per-antenna rate with BD in the multiuser case increase monotonically as the number of BS antennas at each cluster decreases, which indicates that a fully distributed BS antenna layout can achieve the highest average rate performance.]]>66540294043966<![CDATA[Joint Beamforming for Multicell Multigroup Multicast With Per-Cell Power Constraints]]>66540444058903<![CDATA[Secure Multiantenna Cognitive Wiretap Networks]]>66540594072947<![CDATA[Outage Analysis of the Full-Duplex Decode-and-Forward Two-Way Relay System]]>665407340861464<![CDATA[Transmission Experiment of Bandwidth Compressed Carrier Aggregation in a Realistic Fading Channel]]>66540874097889<![CDATA[Sum Secrecy Rate Maximization for Relay-Aided Multiple-Source Multiple-Destination Networks]]>665409841091348<![CDATA[Transmit Power Control for D2D-Underlaid Cellular Networks Based on Statistical Features]]>66541104119763<![CDATA[Energy-Aware Dynamic Selection of Overlay and Underlay Spectrum Sharing for Cognitive Small Cells]]>665412041321038<![CDATA[Cross-Layer Rate Control and Resource Allocation in Spectrum-Sharing OFDMA Small-Cell Networks With Delay Constraints]]>66541334147657<![CDATA[CRIL: An Efficient Online Adaptive Indoor Localization System]]>$text{1} , mathrm{m}$, while previous schemes’ localization errors are up to several meters or even tens of meters. Moreover, we test CRIL in real experiments, and its localization error is up to 3 m in dynamic environments.]]>665414841601656<![CDATA[Distributed Spectrum Management in TV White Space Networks]]>665416141721026<![CDATA[Fundamental Analysis on Data Dissemination in Mobile Opportunistic Networks With Lévy Mobility]]>665417341871569<![CDATA[Network-Centric Versus User-Centric Multihoming Strategies in LTE/WiFi Networks]]>66541884199757<![CDATA[A New Comprehensive RSU Installation Strategy for Cost-Efficient VANET Deployment]]>66542004211803<![CDATA[Fixed Rank Kriging for Cellular Coverage Analysis]]>665421242222045<![CDATA[Packet-Centric Tradeoff and Unfair Success Region in IEEE 802.11 WLANs]]>unfair success region to be avoided in fair resource allocation schemes. One of the primary objectives of any CSMA/CA-based medium access control (MAC) protocol is to increase the frame delivery probability, while reducing the average delay of delivery. With such considerations, we show a tradeoff relationship between the delivery probability and access delay with respect to the maximum retransmission limit in a packet-centric approach. We numerically evaluate the packet-centric behavior of the DCF protocol for single-hop single access point networks involving both the saturated and unsaturated stations, imperfect wireless channel, and varying payload sizes.]]>66542234230683<![CDATA[$i$CAR-II: Infrastructure-Based Connectivity Aware Routing in Vehicular Networks]]>$i$ CAR-II that enables multihop vehicular applications, as well as mobile data offloading and Internet-based services. $i$CAR-II consists of a number of algorithms triggered and run by vehicles to predict local network connectivity and update location servers with real-time network information, in order to construct a global network topology. By providing real-time connectivity awareness, $i$CAR-II improves the routing performance in VANETs by dynamically selecting routing paths with guaranteed connectivity and reduced delivery delay. Detailed analysis and simulation-based evaluations of $i$CAR-II demonstrate the validity of using VANETs for mobile data offloading and the significant improvement of VANETs performance in terms of packet delivery ratio and end to end delay.]]>66542314244951<![CDATA[Cellular Offloading in Heterogeneous Mobile Networks With D2D Communication Assistance]]>66542454255883<![CDATA[Information-Centric Networks With Correlated Mobility]]>

under fast mobility, correlated mobility improves delay performance at the cost of throughput performance;]]>
66542564270761<![CDATA[Protocol Design and Game Theoretic Solutions for Device-to-Device Radio Resource Allocation]]>66542714286848<![CDATA[Understanding the Impact of Employing Relay Node on Wireless Networks]]>66542874299626<![CDATA[Up-and-Down Routing Through Nested Core-Periphery Hierarchy in Mobile Opportunistic Social Networks]]>665430043141613<![CDATA[Self-tuning of Remote Electrical Tilts Based on Call Traces for Coverage and Capacity Optimization in LTE]]>66543154326743<![CDATA[A Novel Reservation-Based MAC Scheme for Distributed Cognitive Radio Networks]]>665432743401341<![CDATA[Probabilistic Small-Cell Caching: Performance Analysis and Optimization]]>665434143541124<![CDATA[Exploiting Overlapped Bands for Efficient Broadcast in Multichannel Wireless Networks]]>665435543701376<![CDATA[Improving Dense Network Performance Through Centralized Scheduling and Interference Coordination]]>665437143821417<![CDATA[Modeling and Dimensioning of a Virtualized MME for 5G Mobile Networks]]>66543834395807<![CDATA[Low-Complexity and Low-Feedback-Rate Channel Allocation in CA MIMO Systems With Heterogeneous Channel Feedback]]>$M$ selected feedback strategy. The precoding matrix indicator (PMI) and rank indicator (RI) are designed with a low complexity so that the PMI/RI reports are calculated more efficiently per CC and user. This approach reduces the complexity significantly in a multiuser and multichannel scenario. Next, we deal with the low-feedback rate and thus devise a channel allocation problem. The problem on one hand provides the minimization of the feedback overhead and, on the other hand, it guarantees the quality-of-service request of each user for a specific target throughput. The problem is considered as a channel assignment problem; thus, the Hungarian method is first used. Next, a low complexity solution is devised using the stable matching algorithm. A complexity analysis and evaluation among the original and the proposed approach for the low-complexity and the low-feedback-rate channel allocation is carried out. This comparison reveals the benefits of the proposed solution for CA MIMO system in HetNets. The proposed approach can be considered as a proposal to the future massive CA, i.e., up to 32 CCs.]]>665439644091034<![CDATA[Invisible Hand: A Privacy Preserving Mobile Crowd Sensing Framework Based on Economic Models]]>665441044232200<![CDATA[A Novel Spectrum Sensing for Cognitive Radio Networks With Noise Uncertainty]]>66544244429518<![CDATA[Modeling Fading Channels With Binary Erasure Finite-State Markov Channels]]>66544294434401<![CDATA[AFD-DFE Using Constraint-Based RLS and Phase Noise Compensation for Uplink SC-FDMA]]>66544354443650<![CDATA[Joint Relay Beamforming Design for Multilevel Nondistributed and Distributed Amplify-and-Forward Relay Networks]]>66544434448435<![CDATA[Information-Aided Iterative Equalization: A Novel Approach for Single-Carrier Spatial Modulation in Dispersive Channels]]>66544484452567<![CDATA[Energy and Spectral Efficiency Tradeoff for Massive MIMO Systems With Transmit Antenna Selection]]>665445344571929<![CDATA[On the Secrecy Rate Maximization With Uncoordinated Cooperative Jamming by Single-Antenna Helpers]]>66544574462582<![CDATA[Radio Environment Map-Aided Doppler Shift Estimation in LTE Railway]]>66544624467285<![CDATA[Full-Duplex Device-to-Device-Aided Cooperative Nonorthogonal Multiple Access]]>66544674471387<![CDATA[Compensation of Transmitter I/Q Imbalance in Millimeter-Wave SC-FDE Systems]]>66544724476316<![CDATA[Transceiver Design for Interference MIMO Relay Systems With Direct Links]]>66544764481464<![CDATA[How Much Bandpass Filtering is Required in Massive MIMO Base Stations?]]>$M$) increases, the required attenuation at the BPFs increases as $mathcal {O}(sqrt{M})$ with $M rightarrow infty$, provided the desired information sum rate (both in the presence and in the absence of aliased OOB interferers) remains fixed. This implies a practical limit on the number of BS antennas due to the increase in BPF design complexity and power consumption with increasing $M$.]]>66544814486413<![CDATA[Modular and Asynchronous Backpressure in Multihop Networks: Model and Optimization]]>66544864491567<![CDATA[Constrained Turbo Product and Block-Convolutional Codes in Wireless Applications]]>66544914495360<![CDATA[A Non-Orthogonal Resource Allocation Scheme in Spatial Group Based Random Access for Cellular M2M Communications]]>66544964500678<![CDATA[On the Spectral Efficiency of Selective Decode-and-Forward Relaying]]>66545004506568<![CDATA[Outage Performance Analysis of Full-Duplex Relay-Assisted Device-to-Device Systems in Uplink Cellular Networks]]>66545064510553<![CDATA[Become a published author in 4 to 6 weeks]]>66545114511887<![CDATA[Imagine a community hopeful for the future]]>665451245121453<![CDATA[IEEE Vehicular Technology Society Information]]>665C3C3145