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Electric vehicles (EVs) are being actively adopted as a solution to sustainable transportation. However, a bottleneck remains with charging, where two of the main problems are the long charging time and the range anxiety of EV drivers. In this research, we investigate the deployment of dynamic charging systems, i.e., electrified roads that wirelessly charge EVs on the go, with a view to accelerating EV adoption rate. We propose a traffic-based deployment strategy, statistically quantify its impact, and apply the strategy to two case studies of real traffic in New York City (USA) and Xi’an (China). We find that our analytical estimates not only closely match the real data, but they also suggest that dynamic charging considerably extends the driving range of popular EV models in urban mobility. For example, when only 5% of the existing roads in New York City are equipped with this technology, an EV model such as the Nissan Leaf will approximately maintain its battery level without stopping to recharge. If the percentage of charging roads is increased to 10%, then the Leaf will gain nearly 10% of its battery after every 40 kilometers of driving. Our framework provides a solution to public and private organizations that support and facilitate vehicle electrification through charging infrastructure. [ABSTRACT FROM AUTHOR]

This work investigates a random access (RA) game for a time-slotted RA system, where $N$ players choose a set of slots of a frame and each frame consists of $M$ multiple time slots. We obtain the pure strategy Nash equilibria (PNEs) of this RA game, where slots are fully utilized as in the centralized scheduling. As an algorithm to realize a PNE (Pure strategy Nash Equilibrium), we propose an Exponential-weight algorithm for Exploration and Exploitation (EXP3)-based multi-agent (MA) learning algorithm, which has the computational complexity of $O(N N_{\max }^{2} T)$. EXP3 is a bandit algorithm designed to find an optimal strategy in a multi-armed bandit (MAB) problem that users do not know the expected payoff of each strategy. Our simulation results show that the proposed algorithm can achieve PNEs. Moreover, it can adapt to time-varying environments, where the number of players varies over time. [ABSTRACT FROM AUTHOR]

In massive MIMO systems, the high complexity of signal detection comes mainly from computing a Gram matrix and its inversion. In this correspondence, we propose a low-complexity MIMO detection method based on the Neumann series (NS), which does not require explicitly computing the Gram matrix and its inversion. The proposed method leverages the statistical information of the Gram matrix to compute an initialization matrix, which guarantees the convergence of NS expansion. The signal detection is conducted implicitly by transforming matrix inversion and product operations into matrix-vector multiplication operations. By doing so, it reduces the computational complexity of MIMO signal detection from $\mathcal {O}(BU^{2}+U^{3})$ to $\mathcal {O}(BUN)$ , where $B$ , $U$ , and $N$ are the numbers of antennas at the base station, at the user equipment, and NS terms, respectively. Simulation results show that, compared with existing approaches, the proposed method can significantly reduce the signal detection complexity while achieving similar performance. [ABSTRACT FROM AUTHOR]

This work performs the statistical QoS analysis of a Rician block-fading reconfigurable intelligent surface (RIS)-assisted D2D link in which the transmit node operates under delay QoS constraints. First, we perform mode selection for the D2D link, in which the D2D pair can either communicate directly by relaying data from RISs or through a base station (BS). Next, we provide closed-form expressions for the effective capacity (EC) of the RIS-assisted D2D link. When channel state information at the transmitter (CSIT) is available, the transmit D2D node communicates with the variable rate $r_{t}(n)$ (adjustable according to the channel conditions); otherwise, it uses a fixed rate $r_{t}$. It allows us to model the RIS-assisted D2D link as a Markov system in both cases. We also extend our analysis to overlay and underlay D2D settings. To improve the throughput of the RIS-assisted D2D link when CSIT is unknown, we use the HARQ retransmission scheme and provide the EC analysis of the HARQ-enabled RIS-assisted D2D link. Finally, simulation results demonstrate that: i) the EC increases with an increase in RIS elements, ii) the EC decreases when strict QoS constraints are imposed at the transmit node, iii) the EC decreases with an increase in the variance of the path loss estimation error, iv) the EC increases with an increase in the probability of ON states, v) EC increases by using HARQ when CSIT is unknown, and it can reach up to $5\times$ the usual EC (with no HARQ and without CSIT) by using the optimal number of retransmissions. [ABSTRACT FROM AUTHOR]

We consider a reflection pattern (RP) designfor reconfigurable intelligent surface (RIS)-assisted vehicle-to-vehicle (V2V) communication systems. Fast-moving-vehicle environments necessitate developing more intuitive RP alignment technique that aligns a predefined codeword to the dominant channel directions. To support a fast RP alignment in RIS-assisted V2V systems, we provide a design guideline of the RP codebook by considering passive reflecting elements at the RIS. We analyze distributions of the incident and reflection channels by taking the street geometry in V2V networks into consideration. By using the statistical information on the difference in spatial frequencies between the incident and reflection channels, we define a set of ideal RPs that are likely to maximize a reflection beamforming gain. The RP codebook is constructed by designing a set of reflecting codewords that can generate actual RPs close to predefined ideal RPs. Numerical results verify that the proposed RP codebook provides a higher link gain than previously reported codebooks. [ABSTRACT FROM AUTHOR]

In this correspondence, new fundamental statistics for the $\alpha$ - $\mu$ fading model are derived. Broadly speaking, these involve two correlated complex $\alpha$ - $\mu$ variates with identical clustering parameters, but arbitrary nonlinearity and power parameters and nonuniform phase distribution. More specifically, the following statistics are derived: (i) exact joint bivariate probability density function (PDF) of quadrature processes; (ii) exact joint bivariate phase-envelope PDF; (iii) approximate bivariate phase PDF; (iv) exact phase correlation coefficient; (v) exact phase autocorrelation coefficient as a function of the fading environment parameters; (vi) phase power spectrum density; (vii) exact phase difference PDF; and (viii) approximate phase difference PDF. As a by-product, a useful mathematical identity is obtained. Strikingly, the approximate phase difference PDF encompasses, as a particular case, the von Mises or Tikhonov PDF. An application example considers a signal transmission over a slow-flat $\alpha$ - $\mu$ fading channel, and the pilot symbol assisted modulation as the channel estimation technique, assuming imperfect channel estimation. It is clearly seen that a uniform phase assumption depicts a pessimistic scenario. [ABSTRACT FROM AUTHOR]

In this paper, two widely used spectrum sensing algorithms, sphericity test and energy detection are considered. The statistics of sphericity test and energy detector with estimated noise power are weighted and fused to propose a mixture detector. The proposed mixture detector based on the fusion test statistics can simultaneously utilize the power information and correlation feature of the received signals. Specifically, the theoretical optimal weight is derived by maximizing the deflection coefficient of the mixture detector. Besides, the analytical expressions for the decision threshold, false alarm probability and detection probability of this algorithm are derived by moment-matching Beta approximation. Numerical examples are provided to illustrate the validity of theoretical analysis. And simulations show that the proposed mixture detector with optimal weight can improve the spectrum sensing performance compared with existing algorithms. [ABSTRACT FROM AUTHOR]

Due to the scarcity of channel availability in IEEE 802.11p standard, applications related to infotainment and multimedia services are vulnerable to suffer from delays and connectivity. Cognitive radio (CR) has the potential to overcome the issue of spectrum scarcity. By making use of the CR technology, secondary users (SU) can access a radio frequency spectrum other than 802.11p through spectrum sensing. Majority of low complex existing algorithms are based on energy detection that are considering the static environment of SUs. In practice, the algorithms, which considers mobility are mainly focused on low vehicle speed. However, the combined effect of primary user (PU) activity statistics and SU mobility has not been tackled while evaluating the performance of opportunistic spectrum access. In particular, this work considers the joint impact of PU activity and vehicle mobility on spectrum sensing performance by considering single lane and double lane vehicular scenarios. We also evaluate the impact of traffic densities on sensing performance. The main contribution of this work is to show the impact of PU activity statistics on sensing performance by deriving analytical expressions for miss-detection probability. These expressions are used for analysis and simulation of cognitive radio enabled vehicular communications. From our observations, it is noted that the probability of vehicle connectivity is improved approximately by 50 $\%$ for initial distance of SU when spectrum sensing is employed under PU activity. [ABSTRACT FROM AUTHOR]

Most works in the literature on parameter estimation with multiple-input multiple-output (MIMO) systems are limited to estimating only the direction of arrival (DOA), and/or based on the far-field assumption. However, the joint estimation of DOA and range is an important challenge for near-field (NF) applications. In addition, backscatter coefficient estimation, as one of the most important parameters of radar systems, has been neglected in existing works. In this paper, the joint estimation of the DOA, range and reflectivity is presented for a MIMO array in the NF. By employing the symmetry property in the array, first, two special spatial expressions based on fourth-order and second-order statistics from the received data are defined so that the output of the first expression lacks targets’ range information. By collecting the information of all the sensors and calculating all the interactions between the transmit and receive sensors based on the first expression, a spatial cumulant matrix is constructed. In two different approaches, with/without the use of eigendecomposition, the virtual steering vectors are extracted. Next, DOAs are estimated by a one-dimensional (1D) spectral search. The second expression, which contains both angle and range information, is used to construct several covariance submatrices and then to construct a covariance matrix with a size corresponding to the array’s dimensions. Then, corresponding ranges are estimated by employing estimated DOAs, eigendecomposition and 1D spectral searches. Finally, the equations required to estimate the reflectivity are derived. The simulation results show the remarkable performance of the proposed method in terms of computational time and estimation accuracy. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) has recently risen as a promising paradigm to meet the increasing resource requirements of the terminal devices. Meanwhile, small cell network (SCN) with MEC has been emerging to handle the exponentially increasing data traffic and improve the network coverage, and is recognized as one key component of the next generation wireless networks. However, with the growing number of terminal devices requiring computation offloading to the edge servers, the network would be heavily congested and thus the performance would be degraded and unbalanced among multiple devices. In this paper, we propose the joint admission control and computation resource allocation in the MEC enabled SCN, and formulate it as a stochastic optimization problem. The goal is to maximize the system utility combining the throughput and fairness while bounding the queue. We decouple the original problem into three independent subproblems, which can be solved in a distributed manner without requiring the system statistical information. An admission control and computation resource allocation (ACCRA) algorithm is designed to obtain the optimal solutions of the subproblems. Theoretical analysis proves that the ACCRA algorithm can achieve the close-to-optimal system utility and reach the arbitrary tradeoff between the utility and the queue length. Experiments are conducted to validate the derived analytical results and evaluate the performance of the ACCRA algorithm. [ABSTRACT FROM AUTHOR]

Vehicle-to-vehicle (V2V) communications is an important technology in vehicular ad hoc network (VANET) to support autonomous data exchange among vehicles. Multiple V2V communications modes have been investigated for VANET, including dedicated short-range communications (DSRC) based on IEEE 802.11p and LTE-V2X, which are suitable for different packet transmission cases. In order to fully exploit the strengths of various modes, hybrid V2V communications strategies are designed in this paper, where each vehicle is allowed to choose different modes for different kinds of transmissions in separate frequency bands for transmission throughput improvement. Furthermore, since it is usually impractical to decide all the vehicles’ communications strategies globally due to high computational complexity and heavy overhead to broadcast the decisions, we model the selection of hybrid V2V communications strategies for vehicles as an evolutionary game. A strategy selection algorithm is then proposed, where each vehicle can select its hybrid V2V communications strategy locally based on its evaluation of the payoffs for different strategies and limited signalling from the base station. Simulation results demonstrate that the proposed algorithm can converge to an asymptotic stable state, which can improve the transmission throughput of vehicles, and is robust to the slight evaluation errors of payoffs and the strategy mutations of a few vehicles. [ABSTRACT FROM AUTHOR]

Terahertz (THz, $0.3\!\!-\!\!3$ THz) wireless access is nowadays considered as a major enabling technology for sixth generation (6G) cellular systems. To compensate for high propagation losses, these systems will utilize antenna arrays with extremely directional beams. The performance of such systems will thus be heavily affected by micromobility such as shakes and rotations of user equipment (UE) even when user is in stationary position. The ultimate impact of micromobility is spontaneous degradation of signal-to-noise (SNR) level leading to outages. In this paper, we measure and statistically investigate the micromobility process of various applications including video viewing, phone calling, virtual reality viewing and racing game. Particularly, we characterize occupancy distributions and first-passage time (FPT) to outage for various antenna configurations. We also assess the radial symmetry in micromobility patterns and characterize distance-dependent velocity and drift to the origin parameters. The obtained results are essential for developing mathematical models of micromobility patterns that needs to be further used in system-level analysis of THz cellular systems. To this end, we also illustrate that Markov models are only suitable for applications with low and purely random micromobility dynamics such as video viewing and phone calling. When a user is controlled by the application, as in the case of gaming, Markov models overestimate FPT to outage. [ABSTRACT FROM AUTHOR]

For large-scale antenna array multiple-input multiple-output (MIMO) systems, a typical solution often put forward for uplink detection consists in applying low-complexity linear receivers such as zero-forcing (ZF) and minimum mean-square error (MMSE) combiners. In this paper, the performance of uplink MMSE detection has been analyzed in the context of a multi-cell multiuser MIMO system. An approximate expression for the signal-to-interference-plus-noise ratio (SINR) is proposed, which is in simple form and becomes exact in the low SNR regime. This approximate SINR applies to systems where the base station (BS) has the statistical information of the out-of-cell interfering signals, and systems where the BS ignores the presence of such interfering signals. This approximate SINR is tight for arbitrary user powers, arbitrary number of antennas at the base station, and arbitrary channel correlation matrix associated with any particular user. Approximate expressions for capacity and bit-error rate/symbol-error rate have also been derived. Particularly, it can be proven that, when the MMSE receiver ignores the presence of out-of-cell interfering signals, the performance gap between ZF and MMSE vanishes in the high SNR regime. [ABSTRACT FROM AUTHOR]

This paper presents a mathematical framework to evaluate distance statistics of first arriving multi-path components at millimeter wave (mm-Wave) frequencies in a typical indoor environment. The proposed modeling approach is based on a numerically tractable channel model that accounts for the line-of-sight (LoS) component and first-order reflection components from surrounding walls. Based on the derived statistics, an approximation for coverage probability of mm-Wave indoor wireless local area networks (WLAN)s is obtained. Further, the coverage probability for an access point (AP) to user node link is determined after accounting for blockage of LoS component and optimal antenna beamforming in the modeling framework. The analysis of coverage probability provides useful insights which can be applied to plan optimum AP location layout in a typical mm-Wave indoor WLAN. [ABSTRACT FROM AUTHOR]

Zero-forcing precoding is a commonly-used multiple-user, multiple-input multiple-output beamforming technique. Applying such precoding, the signal-to-interference-plus-noise ratio (SINR) statistics with outdated channel state information, which involve various projections related to the multi-dimensional channel vectors and precoding vectors, have never been explicitly derived. In this paper, for the multiple-input single-output scenario with uniform power allocation, the distributions of these projections are derived, and then the SINR distribution and the conditional outage probability can be computed using those distributions. [ABSTRACT FROM AUTHOR]

The large-scale collection of individuals’ mobility data poses serious privacy concerns. Instead of perturbing data by adding noise to the raw location data to preserve privacy of individuals, we propose an approach that achieves privacy-preservation at the statistics level of aggregating mobility datasets with the probabilistic data structure Count-Min Sketch (CMS) , which has been widely used to provide efficient statistic functions with a tunable error bound. We use CMS to estimate the population density distributions in the mobility datasets, where the error bound determines utility guarantees. We develop P4Mobi, a novel Probabilistic Privacy-Preserving Publishing framework for Mobility datasets that protects individuals’ privacy while complying to a specific utility requirement. We empirically validate the performance of P4Mobi in terms of utility and privacy-preservation by demonstrating its resilience against a recently proposed reconstruction attack model using two real-world datasets. We compare P4Mobi to two state-of-the-art methods and show that with the same level of privacy achieved against our attack model, P4Mobi significantly improves the utility of the published mobility datasets by up to $20\%$. We also provide a theoretical estimate of the utility achieved by P4Mobi. We found a very consistent match between the estimated and empirical utility of P4Mobi as evaluated on two datasets. [ABSTRACT FROM AUTHOR]

This study proposes a multiobjective beamforming power control scheme to achieve the robust quality of service (QoS), the optimal signal-to-interference-plus-noise ratio (SINR) target tracking and the minimum average power consumption simultaneously in multicell multiuser MIMO (multicell MU-MIMO) wireless system with imperfect channel. The advantages of the proposed multiobjective beamforming power control optimization method are threefold. First, the proposed power control scheme can simultaneously achieve three objectives as follows: 1) the optimal $H_{\infty }$ robust SINR target tracking performance, 2) the optimal $H_{2}$ SINR target tracking performance, and 3) the minimum average power consumption in the multicell MU-MIMO wireless system. The proposed multiobjective optimization problem (MOP) is equivalently derived as a MOP by minimizing their corresponding upper bounds under the constraint of a set of linear matrix inequalities (LMIs). Second, without the requirement of statistical information and the norm-bounded condition of the channel uncertainty, the effect of inter-cell interference, intra-cell interference, channel uncertainty, round trip delay, and noise can be efficiently attenuated by the proposed robust SINR target tracking method. Third, through the proposed LMIs-constrained multiobjective evolution algorithm (MOEA), the designer can obtain Pareto optimal solutions efficiently with a good distribution along the Pareto front to achieve the proposed multiobjective power control design. Several simulation examples with the proposed multiobjective control scheme are produced with favourable results under the guarantee of the theoretical analysis. [ABSTRACT FROM AUTHOR]

In this work, a novel method of event-based V2G scheduling is devised that is suitable for dynamic real time aggregator control in large scale V2G applications within centrally controlled EV car parks. The method is applicable in deterministic systems where a V2G network provides or receives electricity in reoccurring and predictable patterns (events). The scheduling strategy shown is based on a robust modular high-level aggregator control structure and a proposed communications and data management system. The scheduling consists of three algorithm layers, differentiating between predictive scheduling for in-event periods, smart charging for out-of-event periods and reactive scheduling for ongoing adjustments in real-time to account for uncertainty. The scheduling process is described in terms of its underlying rules for prioritising EVs to be either charged or discharged. It's behaviour is then analysed using a simulated car park of up to one thousand connected EVs for an example application in which a V2G network is used to support nearby electrified rail infrastructure, providing power for train acceleration and accepting power from regenerative braking. The departure or arrival of a train of known type and speed pattern can be regarded as a reoccurring event and its effect on the V2G network is therefore predictable due to train schedules and tracking. [ABSTRACT FROM AUTHOR]

This paper investigates cooperative security for slow-fading channels, where the source sends confidential messages to the destination with the assistance of a cooperative jammer coexisting with multiple passive eavesdroppers. Assuming that accurate legitimate channel information and statistical illegitimate channel information are available with an independent transmitter side and a highly correlated receiver side, we propose a correlation-based cooperative jamming approach to reduce the secrecy loss due to receiver-side correlation. In particular, the correlation is considered to be a resource for acquiring more knowledge about illegitimate channels, based on which the statistical distribution of illegitimate channels is described more precisely. Then, the power of artificial noise in the null space of the legitimate channel from the jammer to the destination is placed more reasonably rather than as a simple uniform distribution. Additionally, a correlation-based beamformer is designed for transmitting the information-bearing signal. Finally, the achievable secrecy rate under a secrecy outage constraint is computed. The numerical results confirm that the proposed scheme achieves a performance improvement in terms of secrecy rate under a secrecy outage constraint when the receiver side is highly correlated. [ABSTRACT FROM AUTHOR]

There are several advantages of compressive sensing (CS) technologies for spectrum sensing in wideband cognitive radios (CRs), especially in reducing the sampling rate. However, in CS, the number of measurements needed to reconstruct the sparse signal is determined by the actual sparsity order of the signal, which, in practice, is often unknown and dynamically varying. Thus, in practice, an excessive number of measurements has to be chosen, and choosing such a number degrades the sensing performance. To solve this problem, this article proposes a two-step adaptive compressive spectrum sensing (TS-ACSS) method to estimate the sparsity order. By using a simple statistical analysis of the compressive measurements, a coarse estimation of the sparsity order can be obtained, and an accurate estimation can be acquired by comparing multiple iterations. Compared with existing CS approaches, the proposed TS-ACSS method can not only adaptively adopt an optimal number of measurements without a priori knowledge about current sparsity order but can also detect a sensing failure that can happen if the wideband signal is not sparse in the frequency domain and if the noise level is too large to sense successfully. Furthermore, we verify the effectiveness of our method in dynamic spectrum changing scenarios. The results show that our method can work in both low signal-to-noise ratios (SNRs) and high SNRs environments, and can also be applied in the condition of different energy coefficients and positions of channel occupancy. [ABSTRACT FROM AUTHOR]

Real-time battery state-of-charge (SOC) estimation is critical in many applications. The extended Kalman filter (EKF) has been successfully deployed in SOC estimation allowing real-time SOC monitoring. However, modeling inaccuracies, measurement faults, and wrong initialization can cause the estimation algorithm to diverge. The precise knowledge of statistical information about process and measurements noise is crucial for accurate system modeling and estimation. This paper presents a novel SOC estimation approach based on maximum-likelihood estimation (MLE). The process and measurements models are transformed to an error state propagation system where the innovation covariance is utilized to maximize the likelihood of the multivariate innovation distribution with respect to process and measurement covariances. The MLE formulation allows the estimation of the process and measurement noise covariance magnitudes, which are used to obtain an optimal SOC estimate. The proposed method is validated experimentally using a number of Li-ion battery cells under various testing conditions. The estimation performance is compared with that of the conventional EKF technique as well as previously published results based on autocovariance least-squares measurements noise estimation. The results indicate an enhanced performance for the new algorithm over the traditional EKF across all conducted tests. [ABSTRACT FROM AUTHOR]

In this paper, we analyze cooperative wireless primary networks operating under randomly located interfering nodes (secondary networks). Applying Poisson point processes to modeling of network and interfering node locations, we derive the statistics of signal-to-interference ratio (SIR) under different operational scenarios and show that the SIR statistical models can be viewed as special cases of the H-function distribution. On this basis, we obtain formulas for the average information rates. The presented results are general, and they are applicable to arbitrary fading conditions. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to the statistical characterization of non-central complex Gaussian quadratic forms (CGQFs). Its key strategy is the generation of an auxiliary random variable that replaces the original CGQF and converges in distribution to it. This technique is valid for both definite and indefinite CGQFs and yields simple expressions of the probability density function (PDF) and the cumulative distribution function (CDF) that only involve elementary functions. This overcomes a major limitation of previous approaches, where the complexity of the resulting PDF and CDF does not allow for further analytical derivations. Additionally, the mean square error between the original CGQF and the auxiliary one is provided in a simple closed-form formulation. These new results are then leveraged to analyze the outage probability and the average bit error rate of maximal ratio combining systems over correlated Rician channels. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of energy consumption management of a large population of plug-in hybrid electric vehicles (PHEVs). A PHEV can be derived either on its gasoline or the battery modes while traveling, and can be either charged from or discharged to the grid while parking at home, respectively. This feature is the main novel consideration in our system model, besides taking into account the constraints like traveling time and distance, and the bounds on the charging power and state of charge of the battery. On the basis of the proposed model of PHEV, the optimal battery-gasoline energy management strategy is obtained in order to minimize the cost of energy and battery degradation for each user. Such a strategy can also compensate the peak load. Since the strategy of each PHEV affects other users’ cost through the electricity price, we model such an interaction as a game problem and we prove that there exist a unique Nash equilibrium point of the game. Then, due to the fact that the effect of a single PHEV of a large population on the other users’ costs is negligible, the theory of mean filed (MF) game is utilized to develop a decentralized control strategy for PHEVs. The lightweight information exchange of the proposed MF algorithm ensures a low computational cost and communication overhead. The proof of convergence for the proposed algorithm is given and it is shown that as the number of PHEVs goes to infinity the MF optimal strategy of the players converges to the unique Nash equilibrium point of the game. In simulation results, the effectiveness of the proposed method in terms of demand side management, peak shaving, convergence rate, and robustness is studied. [ABSTRACT FROM AUTHOR]

The drastic growth of mobile traffic greatly challenges the capacity of mobile infrastructures. Dense deployment of low-power small cells helps alleviate the congestion in the radio access network, yet it also introduces large complexity for network management. Software-defined radio access network has been proposed to tackle the added complexity. However, existing software-defined solutions rely on a fully centralized control plane to make decisions for the whole network, which greatly limits the scalability and responsiveness of the control plane. In this paper, we propose a hierarchical software-defined radio access network architecture. The proposed architecture leverages the hierarchical structure of radio access networks, deploying additional local controllers near the network edge. Utilizing the intrinsic locality in radio access networks, it offloads control tasks from the central controller to local controllers with limited overhead introduced. Under the architecture, a distributed optimization framework is proposed, and a typical optimization problem is studied to illustrate the effectiveness of the proposed architecture and framework. Both analysis and experiments validate that the proposed architecture and framework can improve the network objective during the optimization, meanwhile balancing load and improving scalability and responsiveness. [ABSTRACT FROM AUTHOR]

The application of the cross entropy (CE) method to tone injection (TI) can make the peak-to-average power ratio (PAPR) of the orthogonal frequency division multiplexing (OFDM) signals reduced efficiently with great complexity savings. However, as a population-based search method, the CE method requires large samples to find a high-quality solution, which means higher complexity and more time consuming concerning convergence. To reduce the complexity while achieving promising PAPR performance, an improved CE based TI scheme is proposed in this paper. First, a structured extended constellation for rectangular quadrature amplitude modulation is designed to provide more freedom degrees. With proper parameter selection, the proposed extended constellation can offer more design flexibilities and achieve a better tradeoff between freedom degree and power increase. Then, by analyzing the impact of the subcarrier candidate population on the conventional CE based TI scheme, we derive an explicit expression for the determination of the size of the subcarrier candidate population. And, based on the prior information carried by the clipping signal, proper candidate subcarriers are selected. Finally, an improved CE based TI scheme is proposed, which can efficiently enhance the solution quality and greatly accelerate the convergence speed. Simulation results demonstrate that the improved CE based TI scheme achieves great improvements on the performance of PAPR, bit error rate, power increase and complexity compared with the conventional CE based TI scheme. [ABSTRACT FROM PUBLISHER]

Nodal voltages and feeder currents are acquired by running load flows for electric vehicle (EV) charging purposes. The impact of the charging is analyzed by either visually inspecting the voltages and currents or calculating appropriate impact indices. Optimal charging, on the other hand, is performed by incorporating the voltages and currents in charging strategies. Though the impact indices are frequently used for assessing the impact, their usage for optimizing charging has been minimal. Therefore, this paper develops probabilistic impact indices for nodal voltage violations and feeder overloading, which can be applied not only for investigating the impact but also for optimizing charging. Also, aggregated EV load demand of a large EV population is modeled by using a new combined state of charge (SOC)-based charging strategy in time series, for assessing the applicability of the developed impact indices for practical purposes. The combined SOC dynamically integrates arrival and departure of EV and delivered energy. The numerical results show that the proposed impact indices in conjunction with the SOC-based charging have numerous advantages over the conventional charging strategies aided by the load flows. [ABSTRACT FROM AUTHOR]

Limiting the number of retransmissions in practical random access systems such as S-ALOHA and IEEE 802.11 systems has been used, since it was often considered as a way of preventing the system from being congested, by dropping packets in retransmission or letting users know network congestion after the maximum of retransmissions. Therefore, it can be expected that the smaller the maximum of retransmissions is allowed, the smaller the backlog size gets with a higher packet dropping probability. This paper explores how such a retransmission limit alters the shape of the stability and throughput regions of S-ALOHA systems. To do this, we first consider an S-ALOHA system, where two users with infinite queue length have different packet arrival rate and retransmission probability, which is called asymmetric user. Then, we obtain those results of the systems with $N$ symmetric users. [ABSTRACT FROM PUBLISHER]

We consider an energy-harvesting cognitive radio (CR) system coexisting with an unslotted primary user (PU) system. The secondary user (SU) either harvests energy from the PU transmissions or transmits when the PU is sensed to be idle. In this paper, we optimize the sensing intervals to balance between energy harvesting and spectrum access, considering the PU traffic statistics that affects both the energy arrival and consumption in addition to the opportunistic spectrum access. We propose to use different sensing intervals if the SU decides to transmit or harvest energy. The harvesting and transmission durations can be used to optimize the tradeoff between maximizing the utilization of PU spectrum opportunities and harvesting sufficient energy to transmit. We determine a closed-form expression for the probability of SU being active as a function of the PU traffic statistics and SU's access and harvest durations. Optimal transmission and harvesting durations have been found to maximize the CR throughput while protecting the PU. Furthermore, we enhance the system capability by considering that the SU can choose to purchase energy from the PU during the discovered PU idle periods in addition to the energy harvested opportunistically. It can further exploit the difference in the PU busy and idle durations as per its energy needs. The average energy cost spent in a time slot is minimized to achieve a given SU transmission rate while adhering to the PU collision constraint. [ABSTRACT FROM AUTHOR]

Mean-squared error (MSE) is one of the most widely used performance metrics for the designs and analysis of multiple-input multiple-output (MIMO) communications. Weighted MSE minimization, a more general formulation of MSE minimization, plays an important role in MIMO transceiver optimization. While this topic has a long history and has been extensively studied, existing treatments on the methods in solving the weighted MSE optimization are more or less sporadic and nonsystematic. In this paper, for point-to-point MIMO systems, we first review the two major methodologies, Lagrange multiplier method and majorization theory based method, and their common procedures in solving the weighted MSE minimization. Then, some problems and limitations of the methods that were usually neglected or glossed over in existing literature are provided. These problems are fundamental and of critical importance for the corresponding MIMO transceiver optimizations. In addition, a new extended matrix-field-weighted MSE model is proposed. Its solutions and applications are discussed in details. Compared with existing models, this new model has wider applications, e.g., nonlinear MIMO transceiver designs and capacity-maximization transceiver designs for general MIMO networks. [ABSTRACT FROM AUTHOR]

The future wireless network, such as the Centralized Radio Access Network (C-RAN), will need to deliver data rate about 100–1000 times the current fourth-generation (4G) technology. For the C-RAN-based network architecture, there is a pressing need for tremendous enhancement of the effective data rate of the common public radio interface (CPRI). Compression of CPRI data is one of the potential enhancements. In this paper, we introduce a vector quantization based compression algorithm for CPRI links, utilizing the Lloyd algorithm. Methods to vectorize the I/Q samples and enhanced initialization of the Lloyd algorithm for codebook training are investigated for improved performance. Multistage vector quantization and unequally protected multigroup quantization are considered to reduce codebook search complexity and codebook size. Simulation results show that our solution can achieve compression of four times for uplink and 4.5 times for downlink, within $2\%$ error vector magnitude (EVM) distortion. Remarkably, vector quantization codebook proves to be quite robust against data modulation mismatch, fading, signal-to-noise ratio (SNR), and Doppler spread. [ABSTRACT FROM AUTHOR]

To cope with increasing vehicular traffic and extensive application demands on vehicular cellular networks, opportunistic vehicular networks are used to disseminate mobile data by high-capacity device-to-device communication contacts when vehicles come into the communication range of each other, which offloads significant traffic from the cellular network. The current opportunistic vehicular data transmission models often do not take into account the fact that the contact duration is usually very short, due to the high mobility of vehicles, which will limit the amount of data that can be transmitted during one opportunistic contact. In this paper, we consider a mobile data offloading system that integrates cellular network and vehicular opportunistic communications. Based on this proposed system, we establish a mathematical framework to study the problem of contact-aware optimal resource allocation for mobile data offloading by the explicit consideration of the contact duration. Based on theoretical analysis of this problem, we derive an optimal scheme for mobile data offloading to optimally allocate the network resources in terms of opportunistic contacts and offloading helpers’ storage. By carrying out extensive simulation using two realistic urban vehicular traces, we demonstrate the effectiveness of our contact duration aware optimal offloading scheme, in comparison with a wide range of existing schemes. [ABSTRACT FROM AUTHOR]

The channel feedback overhead for massive multiple-input multiple-output systems with a large number of base station (BS) antennas is very high since the number of feedback bits of traditional codebooks scales linearly with the number of BS antennas. To reduce the feedback overhead, an effective codebook based on channel statistics has been designed, where the required number of feedback bits only scales linearly with the rank of the channel correlation matrix. However, this attractive conclusion was only proved under a particular channel assumption in the literature. To provide a rigorous theoretical proof under a general channel assumption, in this paper, we quantitatively analyze the performance of the channel-statistics-based codebook. Specifically, we first introduce the rate gap between the ideal case of perfect channel state information at the transmitter and the practical case of limited channel feedback, where we find that the rate gap depends on the quantization error of the codebook. Then, we derive an upper bound of the quantization error, based on which we prove that the required number of feedback bits to ensure a constant rate gap only scales linearly with the rank of the channel correlation matrix. Finally, numerical results are provided to verify this conclusion. [ABSTRACT FROM AUTHOR]

In the two-level hierarchical modulation (HM), two data streams—the base layer and refinement layer—are transmitted with different degrees of protection according to their importance. In general, the refinement layer suffers from insufficient protection for users experiencing poor channel conditions. Although cooperative transmission can improve the reception performance via diversity combining the signals from the source node and relay node, the refinement layer may still have unacceptable performance for users with very poor signal quality. To improve the overall performance of multiresolution services, we propose protection-level-exchanging HM for multiresolution services under decode-and-forward cooperative networks. At the relay node, it exchanges the protection levels of the decoded base bits and refinement bits, and remodulates the data based on a redesigned optimal HM constellation mapping for transmission. Hence, the overall bit error rate (BER) of the refinement bits is significantly improved at the destination node. We also propose the weighted combined BER (WCB) to fairly and reasonably evaluate the overall service performance. Based on the WCB, we investigate the optimization of the constellation priority parameters used at the source node and relay node to optimize system performance. According to the simulation results, our proposed scheme achieves better performance than other existing schemes. [ABSTRACT FROM AUTHOR]

Fast-charging stations are connected to the electric grid and can fully charge an electric vehicle (EV) in less than half an hour. The capacity and location of the charging stations bring the costs to the electric grid operator, as well as to the station owner and EV user. A zonal approach has been proposed in this paper to determine the optimal place and capacity of fast-charging stations. Station development cost and the expected costs incurred by the EV user and the grid operator due to EV charging have also been included in the proposed approach. The geographic characteristics associated with the electric substations, urban roads, and city zones have also been considered in the proposed approach. EV user behavior is also considered to determine the expected charging demand and the expected EV user cost. The expected cost associated with extra grid loss due to EV charging has also been calculated by ac power flow using hourly electric grid load scenarios. The problem is formulated as a mixed-integer nonlinear (MINLP) problem to minimize the total expected EV charging costs. The problem is solved using the genetic algorithm technique to determine the optimal location and capacity of charging stations. The proposed approach has been applied to study charging station development for the northwest area of Tehran, Iran, and the robustness of the proposed approach has been evaluated. [ABSTRACT FROM PUBLISHER]

This correspondence proposes a new rotated codebook for a three-dimensional (3-D) multiple-input multiple-output (MIMO) system under a spatially correlated channel. To avoid the problem of high dimensionality led by a large antenna array, the rotation matrix in the rotated codebook is proposed to be decomposed by Tucker decomposition into three low-dimensional units, i.e., statistical channel direction information in horizontal and vertical directions, respectively, and statistical channel power in the joint horizontal and vertical direction. A closed-form suboptimal solution is provided to reduce the computational complexity in Tucker decomposition. The proposed codebook has a significant dimension reduction from conventional rotated codebooks and is applicable for a 3-D MIMO system with an arbitrary form of antenna array. Simulation results demonstrate that the proposed codebook works very well for various 3-D MIMO systems. [ABSTRACT FROM PUBLISHER]

While there is a huge body of research on cooperative spectrum sensing in cognitive radio networks (CRNs), incentives for cooperative behavior or the conditions under which cooperation is more likely have not been explored. We model cooperation among cognitive radios (CRs) as a function of social ties among CRs. In this social CRN , where CRs do not necessarily fulfill every cooperative sensing request, we focus on the cooperator set selection problem, i.e., in which CRs ask for cooperation so that the resulting throughput and sensing accuracy are maximized, subject to detection and false alarm probability constraints. For a single-channel scenario, we devise a multiobjective optimization model and obtain the solution using an evolutionary multiobjective algorithm. Our evaluations show that our solution is near optimal, in terms of throughput under legitimate operation, i.e., no malicious users, whereas it outperforms the expected-throughput-optimal scheme in the case of attackers. A numerical analysis demonstrates the robustness of our proposal with a slight loss in performance when the network is subject to common sensing attacks. In addition, our analysis underlines one significant drawback of existing works: assuming all CRs to be cooperative leads to a substantial overestimation of throughput capacity. Finally, to tackle the increasing complexity under a multichannel setting, we propose a heuristic for multichannel cooperative sensing for the considered social CRN. [ABSTRACT FROM PUBLISHER]

Sensor network localization based on connectivity can be modeled as a nonconvex optimization problem. However, current models only consider the convex constraints, i.e., connections among the nodes. The proposed method considers not only the connection constraints but the disconnection constraints, which are nonconvex in nature, as well. It is argued that the connectivity-based localization problem should be represented as an optimization problem with both convex and nonconvex constraints. In this paper, an algorithm combining a modified differential evolution (DE) algorithm and heuristics is presented for the situation in which the communication range value is unknown. The developed algorithm has a new crossover procedure, with refined procedures to produce a new generation of individuals/candidates. A “single node treatment” procedure is also designed for the search procedure to formulate a new set of coordinate locations to jump out from the local minimum. The final solution can reach the most suitable configuration of the unknown nodes (nodes without knowing their location) because all the information on the constraints has been used. Simulation results have shown that better solutions can be obtained when compared with other convex-constraint methods. The proposed method also gets better results than other general nonconvex optimization methods. [ABSTRACT FROM AUTHOR]

Online social networks have become increasingly popular. One of the interesting applications is the friend locator, in which the application server informs a user through a mobile device if some of his/her listed friends are close enough in terms of geographical locations. However, in such services, it is challenging to protect the privacy of the individual users. Previous solutions for the friend locator do not guarantee a high level of privacy and do not maintain efficiency. In this paper, we propose a dual-resolution system structure to guarantee both strong privacy and efficiency. Additionally, we use the polygon decomposition method to achieve both accuracy and flexibility. To be more specific, in the coarse resolution level, each regular mobile user uploads his/her encrypted coarse location information to a central server periodically for comparisons. If a regular mobile user is found to be in the same grid block as an active mobile user, then the friend locator procedure with a higher resolution level will be conducted. Finally, through numerical analysis and simulations, we show that the proposed system design and algorithm can achieve high privacy, efficiency, accuracy, and flexibility. [ABSTRACT FROM PUBLISHER]

Modern engines feature a considerable number of adjustable control parameters. With this increasing number of degrees of freedom (DoFs) for engines and the consequent considerable calibration effort required to optimize engine performance, traditional manual engine calibration or optimization methods are reaching their limits. An automated and efficient engine optimization approach is desired. In this paper, interdisciplinary research on a multiobjective evolutionary algorithm (MOEA)-based global optimization approach is developed for a homogeneous charge compression ignition (HCCI) engine. The performance of the HCCI engine optimizer is demonstrated by the cosimulation between an HCCI engine Simulink model and a Strength Pareto Evolutionary Algorithm 2 (SPEA2)-based multiobjective optimizer Java code. The HCCI engine model is developed by Simulink and validated with different engine speeds (1500–2250 r/min) and indicated mean effective pressures (IMEPs) (3–4.5 bar). The model can simulate the HCCI engine's indicated specific fuel consumption (ISFC) and indicated specific hydrocarbon (ISHC) emissions with good accuracy. The introduced MOEA optimization is an approach to efficiently optimize the engine ISFC and ISHC simultaneously by adjusting the settings of the engine's actuators automatically through the SPEA2. In this paper, the settings of the HCCI engine's actuators are intake valve opening (IVO) timing, exhaust valve closing (EVC) timing, and relative air-to-fuel ratio $\lambda$. The cosimulation study and experimental validation results show that the MOEA engine optimizer can find the optimal HCCI engine actuators' settings with satisfactory accuracy and a much lower time consumption than usual. [ABSTRACT FROM PUBLISHER]

Several studies have shown the importance of realistic micromobility and macromobility modeling in vehicular ad hoc networks (VANETs). At the macroscopic level, most researchers focus on a detailed and accurate description of road topology. However, a key factor often overlooked is a spatiotemporal configuration of vehicular traffic. This factor greatly influences network topology and topology variations. Indeed, vehicle distribution has high spatial and temporal diversity that depends on the time of the day and place attraction. This diversity impacts the quality of radio links and, thus, network topology. In this paper, we propose a new mobility model for vehicular networks in urban and suburban environments. To reproduce realistic network topology and topological changes, the model uses real static and dynamic data on the environment. The data concern particularly the topographic and socioeconomic characteristics of infrastructures and the spatiotemporal population distribution. We validate our model by comparing the simulation results with real data derived from individual displacement survey. We also present statistics on network topology, which show the interest of taking into account the spatiotemporal mobility variation. [ABSTRACT FROM PUBLISHER]

In relay-assisted cellular networks, when and how to cooperate for potential relay nodes is an important issue, particularly from the energy-aware perspective. Moreover, addressing the dynamic cooperative partner-selection problem with incomplete private information is a challenging issue, particularly in the competitive and changing environments. In this paper, we model the energy-aware dynamic competition of cooperative partner selection as an evolutionary game. Specifically, the replicator dynamics are applied to model the adaptation of strategic interactions while considering the dynamic nature in time dependence, and the evolutionarily stable strategy (ESS) is referred to as the robust solution of the proposed game. Moreover, we prove the uniqueness of the resulting ESS, guarantee the convergence to the ESS, and analyze the convergence behavior under the replicator dynamics from different perspectives. In practical terms, we further develop a cooperative partner-selection evolutionary algorithm based on the Q-learning approach, which only requires local knowledge for the potential relay nodes. In addition, we extend our observation by studying the dynamic cooperative partner selection with delay and discuss the properties of the resulting ESS. [ABSTRACT FROM AUTHOR]

This paper proposes a reduced-complexity multichannel assignment scheme for short-range cellular systems. It treats the scenario when a number of small-cell (e.g., femtocell) access points (APs) can be shared to serve active scheduled users. The APs employ isotropic antenna arrays and operate using an open-access control strategy. To improve the reuse ratio of physical resources, the APs are assumed to occupy a single physical channel, wherein coordination among them is infeasible. On the other hand, to improve the spatial coverage, a scheduled user can be served by a single transmit channel from an AP at a time. For the case of overloaded APs and when the feedback links are capacity limited, the scheme attempts to identify the suitable transmit channels from the deployed APs in an adaptive manner such that certain performance and/or processing load limits are satisfied. The effects of some system and design parameters on the outcomes of the scheme are thoroughly discussed. Novel results for the statistics of the resulting interference power are presented, from which results for some performance measures and processing loads are obtained. Numerical and simulations results are provided to clarify the achieved gains, as compared with related models under different operating conditions. [ABSTRACT FROM PUBLISHER]

This paper proposes a methodology based on order statistics to study the energy-efficient (EE) power allocation for wireless communication transmitters. Water-filling power allocation maximizes the EE when transmitting and processing power consumption is considered. Solutions are typically presented in an iterative form, which complicates analysis and adaptive implementation. Time-consuming simulations are rather required to assess their performance. Order statistics allows analyzing the solution without knowledge of the channel realization. We analytically show how the EE depends on the system parameters. The computing efficiency of our proposal, which is executing one or two orders of magnitude faster than the state-of-the-art algorithms with a performance loss of less than 1 dB, facilitates its applicability in vehicular environments. [ABSTRACT FROM PUBLISHER]

Cognitive radio (CR) is considered a promising technology for addressing the issue of spectrum scarcity and has been receiving increasing attention in recent years. One of its “cognitive” capabilities is environment awareness, which gathers useful information from the radio environment, such that CR devices can accordingly change the operating (e.g., spectrum sensing) strategies. Spectrum measurement is an effective means for radio environment awareness since ground-truth observation of spectrum usage is more convincing than any theoretical assumption. In this paper, we perform in-depth statistical analysis on wireless signal strength in mobile service (MS) bands and propose a novel statistical model based on characteristic function (CF), which characterizes both wireless fading channels and MS dynamics. We validate the proposed model through empirical studies on the spectrum measurement data previously collected in Guangdong Province of China. We statistically fit the CF of the overall signal strength in MS (GSM uplink) band and estimate both Nakagami fading parameters of wireless channels and the arrival rate of MS. Then, we further utilize the estimated information to optimize the spectrum sensing strategy, which aims at maximizing the secondary user's (SU's) expected achievable throughput. The simulation results show that the optimal sensing strategy that we propose significantly improves the long-term average achievable throughput since it fully utilizes the estimated information on wireless fading channels and MS dynamics. [ABSTRACT FROM PUBLISHER]

Enhancing the transmission rate for a better spectral utilization is always the ultimate goal of any wireless network. In this paper, we consider the problem of optimal power and rate allocation to maximize the transmission rate of a cognitive radio (CR) operating in the spectrum sharing (SS) CR network (CRN). Unlike previous literature that takes into account the amount of interference to the primary system (PS) as the protection criterion, we reconsider the protection to the PS and CRs through different levels of protection in signal-to-interference-plus-noise ratio (SINR). We propose the concept of effective signal coverage called the permissible space for optimal power and rate adaption. The permissible space for a cognitive transmitter spans over a 3-D space, and it varies according to the channel statistics. Through detecting to which permissible space the cognitive receiver belongs, a novel algorithm suited for distributed CR operation is proposed. Numerical results confirm that our algorithm implemented in the SS CRN achieves a significant enhancement in sum rate, as compared with that of an opportunistic spectrum access (OSA) CRN. [ABSTRACT FROM PUBLISHER]

In wireless multicast systems, cooperative multicast, in which successful users help to relay received packets to unsuccessful users, has been shown to be effective in combating channel fading and improving system performance. However, this mechanism requires the users' voluntary contributions, which cannot be guaranteed since users are selfish and only care about their own performance. Furthermore, users may have heterogeneous costs (which are their private information) to forward packets, and they may lie about their costs if cheating can improve their utilities. To address these problems, in this paper, we model the interaction among users in the wireless multicast system as a multiseller multibuyer payment-based game, where users pay to receive relay service and get paid if they forward packets to others. A simplified case with homogeneous users that have the same cost to forward packets is investigated first. Then, for the case with heterogeneous users, to encourage users to tell their true costs, we use the second-price sealed-bid auction, which is a truth-telling auction, since bidding the true cost is a weakly dominant strategy. To analyze the multiseller multibuyer payment-based game, we observe that under different selected prices, the game can converge to different equilibria, resulting in different user free-riding probabilities and system throughput. We also study the price selection problem and derive the optimal price that maximizes the system throughput. Simulation results show the effectiveness of our proposed mechanism. [ABSTRACT FROM PUBLISHER]

This paper investigates an adaptive scheduling algorithm for multiuser environments with statistically independent but nonidentically distributed (i.n.d.) channel conditions. The algorithm aims to reduce feedback load by sequentially and arbitrarily examining the user channels. It also provides improved performance by realizing postexamining best user selection. The first part of the paper presents new formulations for the statistics of the signal-to-noise ratio (SNR) of the scheduled user under i.n.d. channel conditions. The second part capitalizes on the findings in the first part and presents various performance and processing complexity measures for adaptive discrete-time transmission. The results are then extended to investigate the effect of outdated channel estimates on the statistics of the scheduled user SNR, as well as some performance measures. Numerical results are provided to clarify the usefulness of the scheduling algorithm under perfect or outdated channel estimates. [ABSTRACT FROM PUBLISHER]

The performance of multihop links is studied in this contribution by both analysis and simulations when communicating over generalized-K (KG) fading channels. The performance metrics considered include symbol error rate (SER), outage probability, level crossing rate (LCR), and average outage duration (AOD). First, the expressions for both the SER and outage probability are derived by approximating the probability density function (pdf) of the end-to-end signal-to-noise ratio (SNR) using an equivalent end-to-end pdf. We show that this equivalent end-to-end pdf is accurate for analyzing the outage probability. Then, the second-order statistics of LCR and AOD of multihop links are analyzed. Finally, the performance of multihop links is investigated by either simulations or evaluation of the expressions derived. Our performance results show that the analytical expressions obtained can be well justified by the simulation results. The studies show that the KG channel model and the expressions derived in this paper are highly efficient for the prediction of the performance metrics and statistics for the design of multihop communication links. [ABSTRACT FROM PUBLISHER]

We present a blind recursive algorithm for tracking rapidly time-varying wireless channels in precoded multiple-input–multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. Subspace-based tracking is normally considered for slowly time-varying channels only. Due to the frequency correlation of the wireless channels, the proposed scheme can collect data not only from the time but from the frequency domain as well to speed up the update of the required second-order statistics. After each such update, the subspace information is recomputed using the orthogonal iteration, and then, a new channel estimate is obtained. We also investigate choices of precoder in terms of the tradeoff between the symbol recovery capability and the channel estimation performance and demonstrate the convergence properties of our approach. The proposed algorithm is evaluated in a Third-Generation Partnership Project (3GPP) Spatial Channel Model suburban macro scenario, in which a mobile station is allowed to move in any direction with a speed up to 100 km/h, corresponding to a maximum Doppler shift of about 230 Hz in this case. Numerical experiments show that the normalized mean square error of the channel estimates converges to a level of -30 dB within less than five OFDM symbols when the signal-to-noise ratio (SNR) (per symbol) is \geq 20 dB. [ABSTRACT FROM PUBLISHER]