Search

This paper investigates distributed MIMO precoding design to maximize the weighted sum rate in a heterogeneous network where the multiple input multiple output full-duplex (MIMO-FD) small cells reuse the downlink spectrum of the macro base-station (BS) to exchange backhaul information. The multi-antenna BS transmits signals to the cellular users using the dirty paper coding technique and the MIMO-FD small cells apply FD precoding structures to effectively balance the received signal, the self-interference (SI), and the co-channel interference (CCI). Since the optimization problem is shown to be nonconvex, obtaining the global optimum is challenging. A low-complexity solution with distributed implementation is introduced with proved convergence. By applying the successive convex approximation technique and the duality between the broadcast channel and the multiple access channel, the original nonconvex problem is decomposed into a sequence of convex subproblems, which can be solved analytically and separately at each small cell and macro BS with limited channel state information exchange. Simulation results confirm the convergence and demonstrate the benefits of the introduced algorithm. It is shown that the SI and CCI can be suppressed effectively with sufficient cancellation power and number of transmit antennas at the FD small cells.

In this paper, we investigate the uplink transmission performance of low-power wide-area (LPWA) networks with regards to coexisting radio modules. We adopt long range (LoRa) radio technique as an example of the network of focus even though our analysis can be easily extended to other situations. We exploit a new topology to model the network, where the node locations of LoRa follow a Poisson cluster process (PCP) while other coexisting radio modules follow a Poisson point process (PPP). Unlike most of the performance analysis based on stochastic geometry, we take noise into consideration. More specifically, two models, with a fixed and a random number of active LoRa nodes in each cluster, respectively, are considered. To obtain insights, both the exact and simple approximated expressions for coverage probability are derived. Based on them, area spectral efficiency and energy efficiency are obtained. From our analysis, we show how the performance of LPWA networks can be enhanced through adjusting the density of LoRa nodes around each LoRa receiver. Moreover, the simulation results unveil that the optimal number of active LoRa nodes in each cluster exists to maximize the area spectral efficiency., Comment: This paper has been accepted by IEEE Trans. Vehicular Technology (TVT)

Most applications of underwater acoustic sensor networks (UASNs) rely upon reliable location information of sensor nodes. However, the harsh underwater environment makes localization more challenging as compared to terrestrial sensor networks. This paper is concerned with a localization issue for UASNs, subject to clock asynchronization and node mobility. A hybrid architecture including autonomous underwater vehicles (AUVs), active and passive sensor nodes is designed, where AUVs act as anchor nodes to provide localization information for sensor nodes. In order to eliminate the effect of asynchronous clocks and compensate the mobility of sensor nodes, an asynchronous localization algorithm with mobility prediction is provided for active and passive sensor nodes. Then, two localization optimization problems are formulated as minimizing the sum of all measurement errors. To solve the localization optimization problems, iterative least squares estimators are designed. In addition, the convergence analysis and Crame r–Rao lower bound for the localization errors are also provided. Finally, simulation results show that the proposed localization approach can reduce the localization time by comparing with the exhaustive search-based localization method. Meanwhile, the asynchronous algorithm in this paper can effectively eliminate the impact of the clock asynchronization and node mobility.

In electric and hybrid electric vehicles, series-connected battery packs are commonly used. Should the state-of-health (SOH) of one or several individual cells deteriorate, the entire battery pack is affected, reducing battery pack capacity, which in turn reduces the maximum distance able to be driven. In order to predict the SOH of the individual battery cells, this paper introduces the concept of effective battery capacitance. Effective capacitance is defined as the local slope of the voltage versus charge curve derived from a third-order polynomial relationship between these parameters. The location of maximum effective capacitance can be used as a means of identifying end-of-life and/or catastrophic failure of battery modules. Four different Toyota Prius battery packs were used in establishing this method in the proof of concept work. This paper presents a linear relationship between maximum effective capacitance and SOH; this relationship is confirmed with a larger dataset.

This paper proposes the use of an adaptive control of a hybrid electric vehicle with dual clutch transmission (HDCT). First, this paper shows mathematical equations for the nonlinear system. Then it presents the linearized model for the proposed system. The control objective of the model reference adaptive controller (MRAC) considered in this paper is to minimize fuel consumption and reduce torque interruption in a hybrid electric vehicle. The MRAC can be used to control the electric motor during changes in speed and gear, and the system can adapt to a model that simulates different driver patterns. The effects of using different model responses as input combinations are analyzed in an effort to exploit the over-actuation feature of the system, as is the sensitivity of the performance to various design factors. The simulation results for an HDCT demonstrate that the MRAC achieves reduced torque interruption and less vehicle jerk compared to the conventional method of operation.

Increasing safety and automation in transportation systems has led to the proliferation of radar and IEEE 802.11pbased dedicated short-range communication (DSRC) in vehicles. However, current implementations of vehicular radar devices are expensive, use a substantial amount of bandwidth, and are susceptible to multiple security risks. In this paper, we use the IEEE 802.11 orthogonal frequency-division multiplexing communications waveform, as found in IEEE 802.11a/g/p, to perform radar functions. In this paper, we present an approach that determines the mean-normalized channel energy from frequencydomain channel estimates and models it as a direct sinusoidal function of target range, enabling closest target range estimation. In addition, we propose an alternative to vehicular forward collision detection by extending IEEE 802.11 DSRC and WiFi technology to radar, extending the foundation ofjoint communications and radar frameworks. Furthermore, we perform an experimental demonstration near DSRC spectrum using IEEE 802.11 standard compliant software defined radios with potentially minimal modification through algorithm processing on frequency-domain channel estimates. The results of this paper show that our solution delivers sufficient accuracy and reliability for vehicular RADAR if we use the largest bandwidth available to IEEE 802.11p (20 MHz). This indicates significant potential for industrial devices with joint vehicular communications and radar capabilities.

Using a special type of sliding mode controller, a new type of traction control system (TCS) for hybrid four-wheel drive vehicles is developed. This paper makes two major contributions. First, a new electric powertrain architecture with an in-wheel motor at the front wheels and a clutch on the rear of the transmission is proposed for maximum traction force. The in-wheel motors are controlled to cycle near the optimal slip point. Based on the cycling patterns of the front wheels, the desired wheel speed for rear wheel is defined. The rear wheels are controlled to track this defined speed by controlling the clutch torque. Unlike conventional TCS algorithms, the proposed method exploits clutch control instead of brake control. Second, a special type of sliding mode controller which uses a nonlinear characteristic of the tire is proposed. An important distinction between the proposed sliding mode control method and other conventional feedback controllers is that the former does not depend on feedback error but provides the same functionality. Therefore, the practical aspects are emphasized in this paper. The developed method is confirmed in simulations, and the results reveal that the proposed method opens up opportunities for new types of TCS.

This paper focuses on the secure transmission of wireless-powered full-duplex (FD) relay systems, where a multiantenna source communicates with a single-antenna destination with the help of a FD relay in the presence of a single-antenna eavesdropper. It is assumed that the FD relay is wireless energy harvesting-enabled, adopting both transmit and receive antennas to harvest energy in a time switching (TS) mode. As the objective of this paper is to maximize the system secrecy rate through jointly designing the energy beamforming vector, the information beamforming vector and the TS coefficient, an optimization problem is formulated. The formulated problem is proved to be nonconvex and the challenge to solve which is to concurrently solve out the three variables. To cope with this difficulty, an iterative algorithm is proposed to convert the formulated optimization problem into three convex subproblems, based on which the closed form solutions for the beamforming vectors are derived and the TS coefficient is obtained. Convergence property of the iterative method is analyzed. Simulations are also done to verify the theoretical derivations in terms of the convergence speed and the secrecy rate. Results reveal that the secrecy rate performance of exploiting transmit antenna together with receive antenna for energy harvesting at the FD relay outperforms only receive antenna case. Moreover, although there exists loopback interference between antennas, the FD relaying can always substantially boost the secrecy rate compared with half-duplex relaying architecture.

With the emergence of in-vehicle applications, providing the required computational capabilities is becoming a crucial problem. This paper proposes a framework named autonomous vehicular edge (AVE) for edge computing on the road, with the aim of increasing the computational capabilities of vehicles in a decentralized manner. By managing the idle computational resources on vehicles and using them efficiently, the proposed AVE framework can provide computation services in dynamic vehicular environments without requiring particular infrastructures to be deployed. Specifically, this paper introduces a workflow to support the autonomous organization of vehicular edges. Efficient job caching is proposed to better schedule jobs based on the information collected on neighboring vehicles, including GPS information. A scheduling algorithm based on ant colony optimization is designed to solve this job assignment problem. Extensive simulations are conducted, and the simulation results demonstrate the superiority of this approach over competing schemes in typical urban and highway scenarios.

As the conventional spatial modulation (SM) system lacks an efficient downlink multiple access scheme, this paper proposes a novel downlink nonorthogonal multiple access aided multiple-input multiple-output SM system with finite alphabet inputs. Mutual information (MI) is used to characterize the system's achievable spectral efficiency (SE). Because the MI lacks a closed-form formulation, in this paper we propose a computational-efficient lower bound to provide an approximation to the simulated MI. Besides, preprocessing is also designed at the receiver to mitigate the detrimental effect of channel correlation, which leads to striking a higher SE performance. Simulation results not only confirm the accuracy of our SE analysis, but also substantiate the superior SE performance achieved by the proposed preprocessing.

Both caching and interference alignment (IA) are promising techniques for next-generation wireless networks. Nevertheless, most of the existing works on cache-enabled IA wireless networks assume that the channel is invariant, which is unrealistic considering the time-varying nature of practical wireless environments. In this paper, we consider realistic time-varying channels. Specifically, the channel is formulated as a finite-state Markov channel (FSMC). The complexity of the system is very high when we consider realistic FSMC models. Therefore, in this paper, we propose a novel deep reinforcement learning approach, which is an advanced reinforcement learning algorithm that uses a deep $Q$ network to approximate the $Q$ value-action function. We use Google TensorFlow to implement deep reinforcement learning in this paper to obtain the optimal IA user selection policy in cache-enabled opportunistic IA wireless networks. Simulation results are presented to show that the performance of cache-enabled opportunistic IA networks in terms of the network's sum rate and energy efficiency can be significantly improved by using the proposed approach.

Existing research on a simultaneous wireless information and power transfer (SWIPT) system is based on the assumption of Gaussian inputs. However, the optimal design on Gaussian inputs may lead to dramatic performance loss for practical systems with finite-alphabet inputs. This paper focuses on the precoder design for a SWIPT system with finite-alphabet inputs and instantaneous channel state information over a multiple-input multiple-output channel. We formulate the optimal precoder design as an optimization problem, in which the objective is to maximize the mutual information over the channel from the transmitter to the information receiver under the constraints of transmit power and harvested energy threshold. The formulated problem is NP-hard, so a global optimal solution cannot be found within the polynomial time. The main contributions of this paper are as follows: 1) By using its structure, the NP-hard problem is relaxed to a semidefinite programming problem. Then, a general solving framework for both co-located and separated receiver cases, based on the semidefinite relaxation (SDR) technique, is developed to achieve a near optimal precoder. 2) For the case of co-located receivers, we show that the optimal precoder design is a concave problem with respect to power allocation; then, a specific algorithm for co-located receivers is proposed. Compared with the general SDR-based method, the specific algorithm for co-located receivers exhibits almost the same performance but much lower complexity. 3) The performance of several practical co-located receiver designs is analyzed in SWIPT systems with finite-alphabet inputs. Finally, we provide simulation results to show the efficacy of the proposed algorithms.

Applications of unmanned aircraft systems (UAS) are expected to expand dramatically in the coming decades. In order to provide safe and reliable links for UAS, NASA Glenn Research Center sponsored a measurement campaign in 2013 to characterize air-ground (AG) channels. In the first three of our AG channel characterization series papers, path loss, small scale fading, stationarity distance, root mean square (RMS) delay spread, spatial correlations, and tapped delay line models were presented for a range of ground site environment. In this paper, we expand the results to a unique and little-studied characteristic of the AG channels—airframe shadowing—for a medium-sized aircraft. Airframe shadowing occurs when the line-of-sight signal is obstructed by the airplane itself in some specific maneuvers. Based on over 200 aircraft wing/engine shadowing events in both C-band (5060 MHz) and L-band (968 MHz) collected in the measurement campaign, shadowing depth, duration, multiple antenna diversity gain, and small scale fading results and their statistics are reported. Algorithms are proposed to simulate a random shadowing event based upon shadowing depth and duration cumulative density functions. Airframe shadowing characteristics are independent of the local ground site conditions and link distance. The shadowing loss is modeled as a function of aircraft roll angle, but shadowing loss and duration are essentially uncorrelated. The maximum shadowing loss measured was over 35 dB and the duration was up to 74 s. As expected, deployment of multiple aircraft antennas is shown helpful to mitigate this shadowing.

Diversity is used to combat fading and shadowing in narrowband spectrum sensing. In this paper, we propose two new detection algorithms for wideband spectrum sensing that use square law combining (SLC) diversity to improve the detection performance. We analyze the performance of these algorithms under Nakagami fading. The proposed schemes include channel by channel square law combining (CC-SLC) and ranked square law combining (R-SLC). We first provide the asymptotic analysis for decision statistic of received energy to simplify the analysis. The approximated probability density function (pdf) to decision statistic is used to derive the pdf of received energy for $P$ number of diversity branches. The performance of the proposed algorithms is measured in terms of probability of insufficient spectrum opportunity ( $P_{\text{ISO}}$ ) and probability of excessive interference opportunity ( $P_{\text{EIO}}$ ). The analysis provided in this paper is general and can be used with any fading model and diversity scheme. Experiments are carried out using theoretical analysis and also verified by using Monte-Carlo simulation. Our analysis shows that the proposed algorithms using diversity outperform the channel by channel and ranked channel detection algorithms used under no diversity. Our results also indicate that among the proposed detection algorithms R-SLC performs better.

In electric vehicles (EVs), the source of noise, vibration, and harshness (NVH) involves various components inside/outside of the vehicle. Besides the vibration from each NVH source, the resonances between multiple sources contribute even more significant vibrations. This paper proposes a generalized real-time suppression strategy to mitigate the coupling vibration during vehicle idling and cruising. Using a high-performance field-oriented-controlled electric motor drive, a relatively small amount of rotation velocity offset can be added to the traction motor to significantly reduce the resonant vibration with negligible impact to vehicular speed. In this paper, torsional and bending coupling vibrations between traction motor and driveline are investigated because these two are the most common forced vibrations due to resonances. The system is simulated using MATLAB/Simulink, and the results of the vibration suppression are presented. The strategy proposed in this paper presents a simple and robust way to suppress the coupling vibration in EVs with no additional hardware requirement.

The proposed solutions in the literature for integrating Device-to-Device (D2D) communication in cellular networks require added functionalities and consume valuable network resources, mainly in the discovery process. Unlike existing solutions, this paper mitigates the requirement of additional resources in the LTE-A network. This is achieved by proposing to offload a portion of the discovery traffic and processing of D2D communications that involve vehicular users (drivers and passengers) into Vehicular Ad-hoc Networks (VANETs) by using the inherent knowledge of the Road Side Units (RSUs) about users in their coverage areas. In addition, the paper develops an analytical model to analyze the duration of peer discovery in highway scenarios. The results are validated through simulation experiments using both the Network Simulator NS3 and Matlab. The analytical and numerical results demonstrate the effectiveness of the proposed scheme, and show that a low discovery latency is obtained.

This paper reconsiders the design of blind interference alignment (BIA) for $K$ -user MISO interference channel in which each receiver is equipped with a reconfigurable antenna of preset $M_k\geq 3$ modes. Beyond the degree of freedom (DoF)-oriented BIA (D-BIA) design proposed by Lu et al. , we further take into account the frequency of mode switching within one supersymbol frame. Even though every receiving antenna is reconfigurable with preset multiple modes, from an engineering perspective, it is of practical importance for each receiving antenna to make less number of mode switching in order to gain the benefits such as less switching power consumption and less signal distortion. To this end, we propose in this paper switching-oriented BIA (S-BIA) scheme, which preserves the same degree of freedom (DoF) gain as D-BIA while offering reduced mode switching frequency. Theoretical analysis and simulation results demonstrate that, compared to D-BIA, S-BIA can gain $100/(M-1)$ percent of switching saving when $M_k = M$ is odd for all transmitters. Remarkably, S-BIA offers 50% switching saving for multiuser $3\times 1$ interference channels.

With the ever-growing number of mobile users and the rapid growth of wireless data service requirement, quality of experience (QoE) has emerged as an essential indicator for users, service providers, and operators. Meanwhile, to improve coverage and serve users, a lot of small cell base stations (SBSs) must be installed, and a great amount of energy is consumed. However, as far as is known, there are few works that have studied the combinatorial problem of QoE and energy aware SBS management, which jointly implements power selection, load management (SU allocation), and channel allocation. This paper investigates the problem of QoE and energy aware SBS management, which consists of power selection, load management, and channel allocation. In this paper, we resort to cloud technologies to solve such a complicated combinatorial problem and employ an iterative approach in which two subproblems are alternatively assigned and optimized at each iteration, i.e., 1) transmission power and load joint management and 2) channel allocation. We propose a two-dimensional-action extended weakly acyclic game theoretical scheme to optimize the two subproblems distributedly and iteratively. We define a novel two-dimensional-action pure strategy Nash equilibrium (2D-NE) and prove that at least one 2D-NE exists in the proposed game. With the help of cloud, we propose two kinds of better response algorithms to achieve 2D-NE of the proposed game $G_w$ . Moreover, simulation results show that the proposed approach could achieve a good QoE-energy utility performance and a high QoE energy efficiency.

In cognitive radio (CR) networks, cooperative spectrum sensing is utilized to improve the sensing performance to avoid potential interference to primary users (PUs) and increase spectrum access opportunities for secondary users (SUs). A cooperative spectrum sensing process is divided into three phases: individual sensing/detection, reporting/fusion, and data transmission. In the reporting phase, one or more reporting channels are needed to transmit individual sensing results to a fusion center (FC), and global spectrum sensing results are determined at the FC. The number of required reporting channels depends on the number of spectrum sensors or SUs, which relates to reporting channel efficiency and channel scheduling complexity. That is to say, the reporting channel design can be a challenge, especially when fixed assignment scheduling is used. Therefore, in this paper, we design a reporting channel scheme based on random access protocols, including slotted Aloha and reservation-Aloha. Performance evaluations in terms of PU detection probabilities and false alarm probabilities considering the proposed reporting channels are presented. In addition, the impact of soft/unquantized spectrum sensors or detectors (SUs) and malicious SUs is considered in this paper. Analytical and simulation results illustrate the effectiveness of the proposed reporting channel scheduling methods in improving the cooperative spectrum sensing performance.

With the never-ending increase in the number of mobile connected devices and the need for higher data rates anywhere, anytime, higher frequency bands are being considered for communications. As millimeter-wave technology moves from research to commercial deployments, and motivated by the still limited bandwidth, the terahertz (THz) band is envisioned as the next frontier for communications. When it comes to vehicular networks, communication at much higher frequencies and, consequently, with much higher data rates brings many exciting opportunities as well as challenges. In this paper, an overview of the opportunities and challenges in THz communications for vehicular networks is provided. In addition, the papers in this Special Section which provide first-time solutions to some of these challenges, are introduced.

In this paper, we consider a heterogenous network in which low-power indoor femtocells are deployed in the coverage area of the existing macrobase station (MBS). This paper proposes a novel coordinated random beamforming and user-scheduling strategy to improve the throughput of users served by the femtocell access point (FAP) while satisfying the quality-of-service (QoS) requirements of users served by both MBS and FAP. The strategy, termed as QoS-aware coordinated scheduling (QACS), requires limited coordination between the MBS and FAP, i.e., only the indexes of the qualified beams are shared. Exact statistical analysis for the ergodic achievable rate of both FAP and MBS with the proposed strategy are presented. Scheduling fairness is also addressed for the proposed QACS.

The ever-increasing demand for mobile Internet and high-data-rate applications poses unique challenging requirements for 5G mobile networks, including spectrum limitations and massive connectivity. Cognitive radio and carrier aggregation (CA) have recently been proposed as promising technologies to overcome these challenges. In this paper, we investigate joint relay selection and optimal power allocation in an underlay cooperative cognitive radio with CA, taking into account the availability of multiple carrier components in two frequency bands, subject to outage probability requirements for primary users (PUs). The secondary user network employs relay selection, where the relay that maximizes the end-to-end sum rate is selected, assuming both decode-and-forward and amplify-and-forward relaying. The resulting optimization problems are optimally solved using convex optimization tools, i.e., dual decomposition and an efficient iterative method, allowing their application in practical implementations. Simulation results illustrate that the proposed configuration exploits the available degrees of freedom efficiently to maximize the SU rate, while meeting the PU average outage probability constraints. 1 1967-2012 IEEE. Manuscript received May 27, 2016; revised October 2, 2016; accepted November 7, 2016. Date of publication December 2, 2016; date of current version July 14, 2017. The work of P. D. Diamantoulakis, G. K. Karagiannidis, and T. Khattab was supported by the NPRP under Grant NPRP 6-1326-2-532 from the Qatar National Research Fund (a member of Qatar Foundation). This work was presented in part at the IEEE Wireless Communications and Networking Conference (WCNC), Doha, Qatar, Apr. 2016. The review of this paper was coordinated by Prof. D. B. da Costa. Scopus

With the proliferation of sensors, such as accelerometers, in mobile devices, activity and motion tracking has become a viable technology to understand and create an engaging user experience. This paper proposes a fast adaptation and learning scheme of activity tracking policies when user statistics are unknown a priori, varying with time, and inconsistent for different users. In our stochastic optimization, user activities are required to be synchronized with a backend under a cellular data limit to avoid overcharges from cellular operators. The mobile device is charged intermittently using wireless or wired charging for receiving the required energy for transmission and sensing operations. Firstly, we propose an activity tracking policy by formulating a stochastic optimization as a constrained Markov decision process (CMDP). Secondly, we prove that the optimal policy of the CMDP has a threshold structure using a Lagrangian relaxation approach and the submodularity concept. We accordingly present a fast Q-learning algorithm by considering the policy structure to improve the convergence speed over that of conventional Q-learning. Finally, simulation examples are presented to support the theoretical findings of this paper., 14 pages, 10 figure

In this paper, a new cooperative two-way cognitive relaying scheme is proposed. In this model, a primary user (PU) network consisting of two PU sources communicate with each other via a single amplify-and-forward (AF) relay. In addition, a secondary user (SU) source transmits its data to an SU destination via the same PU relay node. To mitigate the SU interference caused to the PU network, the PU network considers the SU network pairs as two additional relay nodes helping the original PU relay node in improving the PU network performance. As a reward for its cooperation, the PU network allows the SU network to communicate simultaneously via the PU relay node by using the decode-and-forward protocol. The proposed system allows the transmission of four PU symbols and one SU symbol in four/three time slots resulting in a bandwidth efficiency of $1.25/1.67$ based on the cooperative scheme, respectively. Two power allocation optimization problems are formulated: the first problem minimizes the weighted sum of the average symbol error probability of both PU and SU systems, whereas the second problem maximizes the total achievable sum rate of the PU and SU networks. Lagrangian multiplier method is used to find the optimal solutions for both problems under the constraint of maximum allowable power budget. In addition, the paper investigates how the proposed model improves the PU physical layer security performance against a single passive eavesdropper. The results show that the error performance of the proposed relay selection model outperforms the conventional two-way relaying networks with AF protocol. Moreover, findings illustrate that the total achievable sum rate of the proposed relay elimination model is higher than the total achievable rate of the conventional two-way relaying model. From secrecy point of view, the proposed model is shown to achieve a nonzero secrecy rate that improves the PU system security against eavesdropping attacks.

In this paper, we propose a novel distributed cooperative sensing algorithm for connected vehicles. The adaptive energy detection threshold, which is used to decide whether the channel is busy, is optimized in this paper by using a computationally efficient numerical approach. The proposed optimization approach minimizes the probability of an incorrect detection as a function of both the probability of false alarm and the probability of missed detection. By considering both probabilities when computing the probability of incorrect detection, to the best of the authors' knowledge, the proposed approach is novel. During the energy detection process, large-scale fading, multipath fading, Doppler effect, and transmission errors affecting the control messaging process are accounted for in order to provide a practical solution for connected vehicles. Once the available channels have been detected, the vehicles share this information using broadcast control messages. Each vehicle evaluates the available channels by voting on the information received from one-hop neighbors, where the credibility of each neighbor is weighted during the voting process. An interdisciplinary approach referred to as entropy-based weighting is used to define the neighbor, as well as the vehicle's own credibility. The voting mechanism is switched between the proposed voting mechanism and the traditional voting approach obtained from the current state of the art in order to maintain a balance between the computational cost/latency and robust spectrum sensing. Experimental results show that by using the proposed distributed cooperative spectrum sensing mechanism, spectrum detection error converges to zero.

Contrary to the traditional theoretical analysis with perfect self-interference cancellation, this paper studies the outage performance of a full-duplex (FD) decode-and-forward two-way relay system with the consideration of the residual self-interference incurred by the FD mode. The exact closed-form outage probability expressions are derived in this paper. In addition, the asymptotic outage performance is investigated when the transmit powers increase to infinity, and the result demonstrates that the outage probabilities monotonically increase and finally converge to 1 as the transmit power exceeds a certain threshold. Furthermore, optimal power allocation schemes and optimal relay node placement strategies are then proposed to improve the outage performance. Finally, simulation results are conducted to verify the accuracy of the theoretical analysis.

A compact and efficient design for the electromagnetic interference (EMI) filter stage has become one of the most critical challenges in designing a high-density power converter, particularly for avionic applications. To maintain the regulatory standard requirements, EMI filter design needs to be precisely implemented. However, the attenuation characteristics of common-mode (CM) and differential-mode (DM) EMI filters may vary in practice from their respective theoretical calculations due to difficulty and inaccuracy in the noise modeling. This is particularly true for a three-phase active boost rectifier, which inherently has a CM high-frequency component in the rectifier output. This paper puts forward a detailed mathematical modeling of CM noise, originated from different sources, and discusses a concept to significantly reduce the CM noise effects. Furthermore, minimizing the weight and size of DM filter stage has been a design concern as the DM stage consumes a significant part of the weight of the whole system. This paper proposes a compact EMI filter topology with enhanced power density, which aims at satisfying the conducted EMI requirements according to DO-160F in avionics applications. The experimental verifications of the designed EMI filter are performed from the conducted emission results, obtained from a 6-kW laboratory prototype of an integrated stage of EMI and three-phase boost PFC converter.

In this paper, a spectrum-band selection scheme is developed for secondary users (SUs) in underlay spectrum-sharing networks to balance the performance between SUs and primary users (PUs) while guaranteeing PUs' priority. With the developed scheme, among multiple candidate spectrum bands, a given secondary transmitter (ST) is scheduled to access the spectrum band with minimum ratio of the ST–PU channel power gain to the SU link channel power gain. To evaluate this scheme, the SU's performance is investigated concerning its service delay. Under the constraint on average interference from the ST to PUs, the ST's optimal power allocation is obtained in a closed form to achieve the maximum effective capacity (EC) over SU links. Illustrative numerical results and asymptotic discussions substantiate the validity of our derivations and, moreover, demonstrate that, for the case of stringent delay requirements but loose average interference constraint, the band-selection scheme developed in this paper achieves better performance than both the selection of the band with maximum SU link channel power gain and that with minimum ST–PU channel power gain.

With the increasing demand of spectrum and emergence of mobile devices, a cloud radio access network (C-RAN) is a promising technology to improve network capacity and coverage. The key concept of the C-RAN is to separate the radio function unit in remote radio heads (RRHs) from the digital function unit in baseband units (BBUs). This separation facilitates efficient spectrum and infrastructure sharing in the C-RAN. This paper considers the cooperative interference management among RRHs in the C-RAN. The RRHs can form coalitions to mitigate intra-interference and jointly serve their subscribers. To characterize RRH performance under the coalition, this paper develops two signal-to-interference-plus-noise-ratio (SINR)-based downlink throughput models of RRH, where one adopts tools from stochastic geometry to capture the interference from vehicular users (VUs) belonging to noncooperative RRHs, and another ignores the VU interference. In this paper, a coalition formation game is formulated to model the situation in which RRHs of C-RAN can make an individual decision to cooperatively serve their VUs if the throughput of the RRH can be improved. To obtain the solution of the proposed game, we develop a distributed coalition formation algorithm and analyze the stability of the coalition structure using a Markov chain model. Simulation results show that, as compared with the noncoalition and grand coalition, our distributed coalition formation can improve the C-RAN throughput by at least 20% and 80%, respectively. Furthermore, according to extensive simulation, we can define the conditions needed for RRHs to form a coalition and obtain higher throughput.

In cognitive radio networks, secondary users (SUs) can use a frequency band when the primary user (PU) of the band is not transmitting. Spectrum sensing is one of the methods that SUs employ to know if the PU has stopped transmitting. Accurate spectrum sensing boosts the likelihood that an SU detects a spectrum opportunity. However, it will be challenging for affordable receivers to support accurate spectrum sensing due to receiver impairments, including IQ imbalance and aliasing. Several other papers have developed methods that can address the effects of IQ imbalance in spectrum sensing. This paper presents a technique that can address not only IQ imbalance but the effects of aliasing on multiband spectrum sensing as well. We use channelized spectrum-domain modeling of receivers to develop the technique. This paper presents the theoretical development of the receiver modeling technique and the proposed spectrum sensing mechanism. The performance of the proposed mechanism is evaluated using simulations and hardware-based measurements. The results demonstrate that the proposed mechanism significantly enhances the opportunity detection probability of affordable cognitive radios with high IQ imbalance and poor antialiasing filter selectivity performances.

This work considers the problem of reliability and secrecy enhancement for wireless content sharing between a content source node and a content requester. By exploiting the social characteristics of multiple nodes in the presence of multiple independent eavesdroppers, this paper first investigates the impact of mobility for source node selection for transmission reliability, within a cooperative wireless network. Furthermore, trustworthiness and social-tie-based jammer node selections are explored for cooperative jamming to provide secrecy, while allocating power appropriately to the source node and the cooperative jammer node to maximize the worst-case ergodic secrecy rate. Specifically, the source transmitter is selected by first narrowing the potential link set to provide the desired transmission success rate while considering the physical channel and social characteristics jointly. Moreover, the effect of jammer dependability based on its social interactive behavior is also investigated. Instead of focusing on full channel state information (CSI), this paper also investigates the more practical case in which only statistical CSI knowledge is known. Our optimization is based on the upper and lower bounds to the secrecy rate to simplify the power optimization problem without noticeable performance loss. Additional performance analysis for the impact of social characteristics is given. Finally, a one-dimensional search is proposed to further reduce the computational complexity of the power optimization.

The Internet Protocol (IP) Multimedia Subsystem (IMS) is a framework for delivering all-IP-based services. Voice via IMS has been defined as a possible solution from Third-Generation Partnership Project (3GPP) Release 5, prior to long-term evolution (LTE). However, the cost and immediate need for IMS services prevented operators from migration to IMS-based services, particularly with the wide presence of circuit-switched (CS) services offered in Global System for Mobile Communications (GSM) and Universal Mobile Telecommunications System (UMTS) networks. CS fallback (CSFB), which was introduced in 3GPP Release 8, enables the support of voice service without IMS. This is the common deployed scenario for many exiting LTE networks. Voice over LTE (VoLTE) is compulsory to offer rich communication services (RCS) via IMS, in addition to improving the performance of the already deployed CSFB voice solution. However, CSFB and VoLTE are still deployed concurrently, where operators can gradually roll out an LTE/IMS system, while still supporting 2G/3G fallback mechanism. It is, therefore, important to benchmark the performance of both solutions, highlight the deployment challenges, and study the impacts on the end-user experience. This paper provides performance analysis, deployment challenges, and comparisons between these voice solutions. This paper presents practical performance analysis, including end-to-end assessment of call setup delay under different radio conditions, main challenges impacting the in-call performance, as well as performance aspects of Single Radio Voice Call Continuity (SRVCC) and its evolution releases.

Functional safety is of great importance for electric and hybrid electric vehicles (EV/HEVs). One way to improve functional safety of EV/HEVs is to develop reliable and robust onboard diagnostics (OBD) so that, once a component fault is detected, effective remedial actions are taken to avoid system failures. In this paper, we develop a systematic model-based diagnostic approach based on structural analysis for electric drive systems that can form the basis of OBD design for EVs. The structural analysis approach for fault detection and isolation (FDI) evaluates a system's structural model, using the mathematical model of the system in matrix form, from which it is possible to determine the analytic redundancy and to design the structured residuals. In this paper, we demonstrate the application of this methodology by carrying out the design of a diagnostic approach for permanent-magnet synchronous machine (PMSM) drive systems of EVs, with specific focus on sensor fault diagnosis. The diagnostic approach is first verified through a simulation study on the EcoCAR2 vehicle, which is a plug-in HEV developed by the Center for Automotive Research at The Ohio State University, and is then further validated through an experimental study on a test bench consisting of a three-phase inverter enabled by the TMS320F28035 digital signal processor and a PMSM.

The twin goals of maximizing fuel economy (FE) and improving consumer acceptance by reducing the cost of the energy storage system (ESS) in a series hybrid electric vehicle (SHEV) powertrain is addressed here by using energy storage as a means for filtering drive-cycle power demands on the engine, rather than an energy source for supplying an all-electric drive mode. The concept is intended to minimize, if not eliminate, the battery in the SHEV without resorting to full-range proportional control of the engine and generator. An initial optimization study reported for a mid-size SHEV showed that a 4.5-kWh lithium-ion battery pack was still required. In this paper, a sports-car-class SHEV was studied, where the challenge is to reduce the size of the ESS even more because the available space allocation is only one fourth that of the mid-size vehicle. In this paper, a controller is developed, which allows a hybridized SHEV to be realized with a light ESS. The controller includes a duty-cycling feature that manages the engine performance in multiple efficient regions and a bandwidth-limited (BWL) proportional controller feature that limits low-bandwidth battery current. The performance of the controller has been validated for a SHEV powertrain model with an 80- $\text{V}_{\mathrm{dc}}$ 1.125-kWh battery, plus an 80- $\text{V}_{\mathrm{dc}}$ 46.4-F ultracapacitor module using a customized Autonomie vehicle model. The results show that the combined FE of the new design is increased by 13%, compared with the corresponding FE in the equivalent conventional vehicle. Additional FE improvement is possible by reducing the engine size further to reflect the average power demand imposed by real drive cycles.

The use of unmanned aerial systems (UASs), which are also known as unmanned aerial vehicles, and by the term “drones” in the popular press, is growing rapidly. To ensure safety, UAS control and nonpayload communication (CNPC) links must operate very reliably in a variety of conditions. This requires an accurate quantitative characterization of the air–ground (AG) channel, and this channel characterization is the focus of this paper. After providing motivation and background, we describe our methods and modeling approach, followed by a description of our simultaneous dual-band (L-band ∼970 MHz, C-band ∼5 GHz) measurement campaign and the over-water (OW) measurement sites. Example results for path loss and root-mean-square delay spread are provided, as well as the results for channel stationarity distance (SD), used in calculating small-scale Rician $K$ - factor and correlations between the two receiver antennas that we employed in each frequency band. Two distinct SD measures—the power delay profile (PDP) correlation coefficient and the spatial autocorrelation matrix collinearity—were used and found to be of the same order. Path-loss exponents are near that of free space, but significant two-ray cancelation effects for these OW settings warrant more accurate models, which we provide. Delay spreads in the OW channels are also dominated by the two-ray components and are hence typically very small (∼10 ns) but can exceed 350 ns. A third intermittent multipath component (MPC) is also present a nonnegligible fraction of time; hence, we provide statistical wideband AG channel models to represent this. Future papers in this series will report results for the AG channel with ground sites in other types of environments.

This paper investigates the secrecy outage performance of transmit antenna selection (TAS)/maximal ratio combining (MRC) in multiple-input-multiple-output (MIMO) cognitive radio networks (CRNs) over Rayleigh fading channels. In the considered system, a secondary user (SU-TX) equipped with N A (N A ≥ 1) antennas uses TAS to transmit confidential messages to another secondary user (SU-RX), which is equipped with N B (N B ≥ 1) antennas and adopts the MRC scheme to process multiple received signals. Meanwhile, an eavesdropper equipped with N E (N E ≥ 1) antennas also adopts the MRC scheme to overhear the transmitted information between SU-TX and SU-RX. SU-TX adopts the underlay strategy to guarantee the quality of service (QoS) of the primary user (PU) without spectrum sensing. In this paper, we derive the exact and asymptotic closed-form expressions for the secrecy outage probability (SOP). Simulations are conducted to validate the accuracy of the analysis.

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

In this paper, we address the problem of securing information transmission in very adverse environments, i.e., no eavesdropper channel state information (CSI) and imperfect legitimate channel CSI, by introducing a massive multiple-input–multiple-output (M-MIMO) relay. The relay is powered through wireless power transfer from the source, such that it can flexibly adjust its position according to the secrecy requirement. The benefits of a M-MIMO relay are fully exploited to enhance both power transfer efficiency and information transmission security. In particular, the challenging issues incurred by short interception distance and long transfer distance are well addressed simultaneously. The focus of this paper is on investigating the secrecy performance of two classical relaying protocols, namely amplify-and-forward (AF) and decode-and-forward (DF), and obtaining explicit expressions of secrecy outage capacity in terms of transmit power, channel condition, and antenna number. Furthermore, we propose two effective performance optimization schemes, i.e., power control at the source, and relaying protocol switch at the relay. Finally, our theoretical claims are validated by simulation results.

Traffic offloading among coexisting heterogeneous radio access technologies (RATs), i.e., inter-RAT offloading, is considered to be an effective strategy for enhancing the capacity of wireless networks and reducing the power consumption of both base stations (BSs) and mobile stations (MSs). To fully utilize inter-RAT offloading advantages, a mobility-aware evaluation is needed, which takes into account the combined effects of mobility and MS and BS spatial distributions on the performance gains. This paper proposes a novel analytical model that accurately describes MS mobility dynamics in heterogeneous wireless networks, while conveying detailed information on its location. In addition, this paper derives an analytical expression for calculating the packet loss probability for orthogonal-frequency-division-multiple-access (OFDMA)-based networks. Taking users' mobility and spatial distribution into account, this paper develops a Markovian-based framework for evaluating inter-RAT offloading performance. The accuracy of the proposed mobility model and the efficacy of the proposed performance evaluation framework are validated. Results show that MS mobility and vertical handover (VHO) cost significantly affect inter-RAT offloading performance, and ignoring their effects could lead to an inaccurate evaluation. When the effects of MS mobility and VHO cost on the performance are ignored, utilizing inter-RAT offloading decreases the packet loss probability by 9% and saves energy by 6%. However, accounting for these effects results in a 32% increase in the packet loss probability evaluation and a 64% increase in the energy consumption evaluation.

This paper studies the cooperative device-to-device (D2D) transmissions in cellular networks, where two D2D users communicate bidirectionally with each other and simultaneously serve as relays to assist the two-way transmissions between two cellular users. For this scenario, both cellular and D2D links share the same spectrum, thus creating mutual interference. In addition to that, a security problem also exists since the cellular users want to keep their messages secret from the D2D users and vice versa . To address these two issues, a security-embedded interference avoidance scheme is proposed in this paper. By exploiting the constellation rotation technique, the proposed scheme can create interference-free links for both D2D and cellular communications, thereby significantly improving the system error performance. Moreover, our scheme also provides an inherent secrecy protection at the physical layer, which makes the information exchange between cellular users and that between D2D users confidential from each other.

Wireless systems employing frequency-division duplexing (FDD) must use duplexing filters to isolate the sensitive receiver from its own strong transmission. The number of filters in the duplexer is equal to the total number of aggregated transmit and receive bands. These filters are nontunable; therefore, a separate duplexer is required for each combination of operating bands. This paper presents an adaptive duplexer architecture capable of handling more than one receiver channel and tunable over a wide frequency range. The structure uses a combination of a low-isolation device with multiple analog canceling loops controlled by a normalized least mean squares (N-LMS) algorithm to track changes in signal leakage. The control is a multiple-input–multiple-output (MIMO) problem. This paper shows that modeling the inverse plant can orthogonalize the problem and dramatically improve convergence times with reduced complexity over previous search algorithms. Low-power pseudonoise (PN) pilot signals are proposed to detect and cancel the transmitter noise. Simulations show that 50 dB of isolation is possible with 20-MHz Long-Term Evolution (LTE)-like channels. Convergence takes 80 iterations (10 ms) from cold or 15 iterations (1.5 ms) for changes in carrier frequency or antenna loading.

There is an ongoing debate in the research and industry communities as to whether IEEE 802.11p or Third-Generation Partnership Project (3GPP) Long-Term Evolution (LTE) should be used for vehicular communications. In this paper, we argue that a hybrid vehicular network combining both technologies can increase the performance of the system. We first propose a mechanism to improve location-based routing in a hybrid vehicular network architecture by data and signaling traffic separation on independent wireless networks. We then develop analytical models to calculate the stochastic upper bound of the end-to-end delay (E2ED) for location-based routing in three different networking architecture alternatives based on a) short-range ad hoc only, b) cellular only, and c) the proposed hybrid ad hoc/cellular network. The analytical approach in this paper is based on the stochastic network calculus (SNC) theory, which provides a solid and uniform framework for analysis of the upper bound of the E2ED in communication networks. It is demonstrated that the proposed hybrid network provides a lower E2ED compared with the other two alternatives. Comparisons of realistic simulation results, carried out in NS-3, and analytical results show that the proposed delay bounds provide relatively tight approximations for the E2ED in the three alternative architectures for vehicular networks investigated in this paper.

Security and privacy have become increasingly critical demands in wireless networks, which, however, are particularly susceptible to eavesdropping attacks due to the broadcast nature of radio signals. This paper considers the problem of secure communication in the multiuser downlink with a passive eavesdropper (Eve), whose channel state information (CSI) is unavailable. The transmitter simultaneously transmits concurrent information signals to the users and artificial noise (AN) to Eve. We first assume that all users' CSI is perfectly known by the transmitter and derive a closed-form expression for the ergodic secrecy sum rate (SSR) in the large-system limit. We then use it as an objective function to optimize the power allocation between information signals and the AN. It shows that more power needs to be used for AN when Eve has more antennas and when the system serves fewer users. We also extend the analysis to the imperfect-CSI scenario, where the SSR saturates at high transmit power, and it is better to create more AN than to increase the signal strength when the channel estimation error is large. We derive a scale law of feedback bits (for frequency-division duplexing (FDD) systems) to maintain a constant rate offset compared with the perfect-CSI case and the optimal length of training sequence (for time-division duplexing (TDD) systems) to maximize the effective SSR. It shows that more feedback bits and longer training sequences are required to deal with the eavesdropping problem. Closed-form expressions derived in this paper can reduce the complexity of system analysis and design.

Electric vehicles will become a popular mode of travel when petroleum prices increase and global warming intensifies. However, the short driving range of electric vehicles remains a challenge. To avoid battery depletion, navigation systems route electric vehicles to surrounding charging stations for battery exchange or recharge. Nonetheless, zero inventories and occupied sockets may result in variable charging time and longer travel time. Conventional navigation approaches have been unable to respond to variable charging time because these approaches do not consider the charging times and energy consumption of electric vehicles. Navigation systems can manage shared information, traffic, and battery life, and can improve the mileage per kilowatthour of electric vehicles. In this paper, an electric-vehicle navigation system (EVNS) based on an autonomic computing architecture and a hierarchical architecture over vehicular ad-hoc networks (VANETs) is proposed. In addition to moving energy, the proposed EVNS considers air conditioner energy to predict the state of charge (SOC) because air conditioning is the most demanding auxiliary load in electric vehicles. Moreover, this paper proposes a time-dependent routing algorithm that considers shared information. The results show that the proposed EVNS method results in 31.15% and 9.52% improvements in mileage compared with the shortest path first (SPF) method and the distributed method in Manhattan, New York City, NY, USA, respectively. Furthermore, the proposed EVNS method results in 32.39% and 20.67% improvements in mileage compared with the SPF method and the distributed method in Taipei, Taiwan, respectively.

Resource allocation is an important strategy to optimize system performance in multiuser cooperative orthogonal frequency-division multiple-access (OFDMA) systems. In this paper, we jointly consider three different transmission modes in cooperative OFDMA systems, i.e., direct transmission (DT) mode, half-duplex (HD) relay cooperative transmission (HDRCT) mode, and full-duplex (FD) relay transmission (FDRT) mode. The joint optimization problem of transmission mode selection, subcarrier assignment, relay selection, subcarrier pairing, and power allocation (PA) is investigated. In this paper, we transform the binary assignment problem into a maximum-weighted bipartite matching problem, which can be solved by the classical Hungarian method. Based on the dual method, we solve the joint PA and binary assignment problem iteratively. Specifically, since the direct link is considered interference in the FD relay transmission mode, the PA problem in FD relay transmission mode is nontrivial. Thus, we provide a novel hierarchical dual method to solve the PA problem in FD relay transmission mode. In addition, in HDRCT mode, the joint transmission of both the source and relay is taken into account, and we provide a simple and insightful PA scheme. Simulation results show that the proposed algorithms can significantly enhance the overall system throughput, compared with previous works.

A generalized integrated control strategy for vehicle dynamics using an optimal torque vectoring control approach is extended in this paper. The central objective of this approach is to generate optimal additional tire forces and yaw moment over the vehicle through the application of individual wheel torque to keep the vehicle on a target path. This is achieved by minimizing the error between the actual and target forces and moment at the center of gravity (CG). In this paper, this methodology is extended to a constrained optimal control approach that handles additional real-time constraints, which has several vehicle control applications. An online optimization strategy is used to solve the resulting constrained optimization problem that gives the necessary tire force adjustments at the tire level. Some typical applications are 1) differential braking on all wheels, which is applicable to both electric and conventional cars and 2) hybrid torque vectoring on the front wheels and differential braking on the rear wheels. Both simulations and experimental results show the usefulness of this approach of handling constraints with this optimal torque vectoring control.

An improved controller for the energy management system of a power-split hybrid electric vehicle (HEV) is developed with the objectives of minimizing fuel consumption and improving drivability. Considering the specific application of vehicles plying on scheduled trips such as public transport, this paper assumes that the controller is privileged with a priori knowledge of the estimated total tractive energy requirement and the duration of the journey. In comparison to a recently introduced constant high-efficiency region (CHER)-based controller, this paper demonstrates that further reductions in fuel consumption can be achieved under certain driving cycles by limiting the internal-combustion-engine (ICE) operation to a dynamically varying high-efficiency region and adopting state-of-charge (SOC) swing control for battery energy storage. The frequency of engine on/off is therefore directly decided by the size of the energy storage, allowable swing of the SOC, and the tractive energy required. Performances of the CHER and dynamic high-efficiency region (DHER) controllers are compared through simulations against the existing controller of a commercial vehicle. The results reveal that the DHER controller outperforms the other two controllers in terms of fuel consumption in highway-style-driving scenarios. Therefore, to minimize fuel consumption while improving drivability under all driving scenarios, this paper proposes to combine the CHER controller with the DHER controller such that the best features of both controllers can be utilized.

International audience; This paper presents an online diagnosis method for the supercapacitors' aging problem. State-of-health (SoH) estimation is an important feature since aging introduces degradation in the supercapacitors' performance, which might eventually lead to their failure. SoH is usually measured by electrochemical impedance spectroscopy. However, this has to be performed offline and requires interruption of the system's operation. Unlike this method, this paper presents an online diagnosis technique for supercapacitors using an extended Kalman observer as a well-known tool for its particularities and performances to study nonlinear parameter estimation. The main objective of this paper is the online SoH diagnosis based on the supercapacitors' aging indicator estimation. Moreover, the proposed online estimation strategy requires only voltage and current measurements, which reduces the number of sensors with respect to other methods. The effectiveness of the proposed online observer is shown through experimental results, and robustness to noise is also studied.

Fuel cells have increasingly become an attractive option for providing clean electricity at relatively high efficiency. This paper discusses the key features of a proton exchange membrane fuel cell for a fuel cell-based auxiliary power unit (FC-APU) to supply a portion of electric loads in a vehicle. The FC-APU includes two converters: a unidirectional converter to control the fuel cell current and a bidirectional converter to control the dc-link voltage and battery current. This paper proposes 1) an energy management system (EMS) to manage the power transfer between the load and energy sources with emphasis on low stress on the fuel cell, 2) an iterative method to size the fuel cell and battery for a given load profile, based on the proposed EMS, and 3) a current estimator to reduce the effects of system transients on the dc-link voltage and increase system modularity. Simulation and experimental results validate the proposed EMS, sizing, and controller design.

This paper proposes a Flexible Infrastructure for Dynamic Power Control (FIDPC) of Electric Vehicle (EV) Battery Chargers. This infrastructure dynamically adjusts the EV battery charger current, according to the power demand of the home wherein the vehicle is plugged. An infrastructure was implemented to validate this proposal. Such infrastructure is composed by an EV battery charger and a communication system based on a Radio Frequency interface. The battery charger has nominal power of 3.6 kVA and operates with sinusoidal current and unitary total power factor, while the RF interface provides continuous data flow to the battery charger with information about the home total current consumption (rms value). Experimental tests were performed under realistic conditions to validate the concept behind the proposed FIDPC. These tests served to assess the behavior of the EV battery charger with dynamic power control on a single-phase, 230 V, 16 A, 50 Hz residential electrical installation. The experimental results confirm the quick time response of the FIDPC even when working under heavy home load variations., This work was supported by the Fundacao para a Ciencia e Tecnologia (FCT) through Project PEst-UID/CEC/00319/2013. The work of V. Monteiro was supported by the FCT agency through a doctoral scholarship under Grant SFRH/BD/80155/2011. The review of this paper was coordinated by Dr. D. Cao.