Showing papers in "IEEE Transactions on Vehicular Technology in 2010"
TL;DR: In this paper, the authors present state-of-the-art energy storage topologies for hybrid electric vehicles and plug-in hybrid vehicles (PHEVs) and compare battery, UC, and fuel cell technologies.
Abstract: The fuel economy and all-electric range (AER) of hybrid electric vehicles (HEVs) are highly dependent on the onboard energy-storage system (ESS) of the vehicle. Energy-storage devices charge during low power demands and discharge during high power demands, acting as catalysts to provide energy boost. Batteries are the primary energy-storage devices in ground vehicles. Increasing the AER of vehicles by 15% almost doubles the incremental cost of the ESS. This is due to the fact that the ESS of HEVs requires higher peak power while preserving high energy density. Ultracapacitors (UCs) are the options with higher power densities in comparison with batteries. A hybrid ESS composed of batteries, UCs, and/or fuel cells (FCs) could be a more appropriate option for advanced hybrid vehicular ESSs. This paper presents state-of-the-art energy-storage topologies for HEVs and plug-in HEVs (PHEVs). Battery, UC, and FC technologies are discussed and compared in this paper. In addition, various hybrid ESSs that combine two or more storage devices are addressed.
TL;DR: Implementation of the CACC system, the string-stability characteristics of the practical setup, and experimental results are discussed, indicating the advantages of the design over standard adaptive-cruise-control functionality.
Abstract: The design of a cooperative adaptive cruise-control (CACC) system and its practical validation are presented. Focusing on the feasibility of implementation, a decentralized controller design with a limited communication structure is proposed (in this case, a wireless communication link with the nearest preceding vehicle only). A necessary and sufficient frequency-domain condition for string stability is derived, taking into account heterogeneous traffic, i.e., vehicles with possibly different characteristics. For a velocity-dependent intervehicle spacing policy, it is shown that the wireless communication link enables driving at small intervehicle distances, whereas string stability is guaranteed. For a constant velocity-independent intervehicle spacing, string stability cannot be guaranteed. To validate the theoretical results, experiments are performed with two CACC-equipped vehicles. Implementation of the CACC system, the string-stability characteristics of the practical setup, and experimental results are discussed, indicating the advantages of the design over standard adaptive-cruise-control functionality.
TL;DR: The state of the art for electric, hybrid, and fuel-cell vehicles is reviewed, with a focus on architectures and modeling for energy management.
Abstract: With the advent of more stringent regulations related to emissions, fuel economy, and global warming, as well as energy resource constraints, electric, hybrid, and fuel-cell vehicles have attracted increasing attention from vehicle constructors, governments, and consumers. Research and development efforts have focused on developing advanced powertrains and efficient energy systems. This paper reviews the state of the art for electric, hybrid, and fuel-cell vehicles, with a focus on architectures and modeling for energy management. Although classic modeling approaches have often been used, new systemic approaches that allow better understanding of the interaction between the numerous subsystems have recently been introduced.
TL;DR: In this paper, an analytical closed-form expression of an achievable secrecy rate was derived for the case of noncolluding eavesdroppers and an upper bound on the secrecy rate is provided.
Abstract: We consider the problem of secure communication with multiantenna transmission in fading channels. The transmitter simultaneously transmits an information-bearing signal to the intended receiver and artificial noise to the eavesdroppers. We obtain an analytical closed-form expression of an achievable secrecy rate and use it as the objective function to optimize the transmit power allocation between the information signal and the artificial noise. Our analytical and numerical results show that equal power allocation is a simple yet near-optimal strategy for the case of noncolluding eavesdroppers. When the number of colluding eavesdroppers increases, more power should be used to generate the artificial noise. We also provide an upper bound on the SNR, above which, the achievable secrecy rate is positive and shows that the bound is tight at low SNR. Furthermore, we consider the impact of imperfect channel state information (CSI) at both the transmitter and the receiver and find that it is wise to create more artificial noise to confuse the eavesdroppers than to increase the signal strength for the intended receiver if the CSI is not accurately obtained.
TL;DR: A closed-form expression is derived for the mean SU capacity under the imperfect CSI scenario where the SU cannot always satisfy the peak received interference power constraint at the PU and has to back off its transmit power.
Abstract: Cognitive radio (CR) design aims to increase spectrum utilization by allowing the secondary users (SUs) to coexist with the primary users (PUs), as long as the interference caused by the SUs to each PU is properly regulated. At the SU, channel-state information (CSI) between its transmitter and the PU receiver is used to calculate the maximum allowable SU transmit power to limit the interference. We assume that this CSI is imperfect, which is an important scenario for CR systems. In addition to a peak received interference power constraint, an upper limit to the SU transmit power constraint is also considered. We derive a closed-form expression for the mean SU capacity under this scenario. Due to imperfect CSI, the SU cannot always satisfy the peak received interference power constraint at the PU and has to back off its transmit power. The resulting capacity loss for the SU is quantified using the cumulative-distribution function of the interference at the PU. Additionally, we investigate the impact of CSI quantization. To investigate the SU error performance, a closed-form average bit-error-rate (BER) expression was also derived. Our results are confirmed through comparison with simulations.
TL;DR: The results show that the proposed GLRT exhibits better performance than other existing techniques, particularly when the number of samples is small, which is particularly critical in vehicular applications.
Abstract: In this paper, we consider the problem of detecting a primary user in a cognitive radio network by employing multiple antennas at the cognitive receiver. In vehicular applications, cognitive radios typically transit regions with differing densities of primary users. Therefore, speed of detection is key, and so, detection based on a small number of samples is particularly advantageous for vehicular applications. Assuming no prior knowledge of the primary user's signaling scheme, the channels between the primary user and the cognitive user, and the variance of the noise seen at the cognitive user, a generalized likelihood ratio test (GLRT) is developed to detect the presence/absence of the primary user. Asymptotic performance analysis for the proposed GLRT is also presented. A performance comparison between the proposed GLRT and other existing methods, such as the energy detector (ED) and several eigenvalue-based methods under the condition of unknown or inaccurately known noise variance, is provided. Our results show that the proposed GLRT exhibits better performance than other existing techniques, particularly when the number of samples is small, which is particularly critical in vehicular applications.
TL;DR: Extensive simulations demonstrate that PASS outperforms previously reported schemes in terms of the revocation cost and the certificate updating overhead and provides strong privacy preservation to the vehicles so that the adversaries cannot trace any vehicle, even though all RSUs have been compromised.
Abstract: In this paper, we propose an efficient pseudonymous authentication scheme with strong privacy preservation (PASS), for vehicular communications. Unlike traditional pseudonymous authentication schemes, the size of the certificate revocation list (CRL) in PASS is linear with the number of revoked vehicles and unrelated to how many pseudonymous certificates are held by the revoked vehicles. PASS supports the roadside unit (RSU)-aided distributed certificate service that allows the vehicles to update certificates on road, but the service overhead is almost unrelated to the number of updated certificates. Furthermore, PASS provides strong privacy preservation to the vehicles so that the adversaries cannot trace any vehicle, even though all RSUs have been compromised. Extensive simulations demonstrate that PASS outperforms previously reported schemes in terms of the revocation cost and the certificate updating overhead.
TL;DR: This paper efficiently copes with challenges with a decentralized group-authentication protocol in the sense that the group is maintained by each roadside unit (RSU) rather than by a centralized authority, as in most existing protocols that are employing group signatures.
Abstract: Existing authentication protocols to secure vehicular ad hoc networks (VANETs) raise challenges such as certificate distribution and revocation, avoidance of computation and communication bottlenecks, and reduction of the strong reliance on tamper-proof devices. This paper efficiently copes with these challenges with a decentralized group-authentication protocol in the sense that the group is maintained by each roadside unit (RSU) rather than by a centralized authority, as in most existing protocols that are employing group signatures. In our proposal, we employ each RSU to maintain and manage an on-the-fly group within its communication range. Vehicles entering the group can anonymously broadcast vehicle-to-vehicle (V2V) messages, which can be instantly verified by the vehicles in the same group (and neighboring groups). Later, if the message is found to be false, a third party can be invoked to disclose the identity of the message originator. Our protocol efficiently exploits the specific features of vehicular mobility, physical road limitations, and properly distributed RSUs. Our design leads to a robust VANET since, if some RSUs occasionally collapse, only the vehicles that are driving in those collapsed areas will be affected. Due to the numerous RSUs sharing the load to maintain the system, performance does not significantly degrade when more vehicles join the VANET; hence, the system is scalable.
TL;DR: A fully distributed and scalable cooperative spectrum-sensing scheme based on recent advances in consensus algorithms that not only has proven sensitivity in detecting the primary user's presence but also has robustness in choosing a desirable decision threshold.
Abstract: In cognitive radio (CR) networks, secondary users can cooperatively sense the spectrum to detect the presence of primary users. In this paper, we propose a fully distributed and scalable cooperative spectrum-sensing scheme based on recent advances in consensus algorithms. In the proposed scheme, the secondary users can maintain coordination based on only local information exchange without a centralized common receiver. Unlike most of the existing decision rules, such as the or-rule or the 1-out-of-N rule, we use the consensus of secondary users to make the final decision. Simulation results show that the proposed consensus scheme can have significant lower missing detection probabilities and false alarm probabilities in CR networks. It is also demonstrated that the proposed scheme not only has proven sensitivity in detecting the primary user's presence but also has robustness in choosing a desirable decision threshold.
TL;DR: To help researchers better understand the practical implications of AI to their CR designs, this paper reviews several CR implementations that used the following AI techniques: artificial neural networks (ANNs), metaheuristic algorithms, hidden Markov models (HMMs), rule-based systems, ontology- based systems (OBSs), and case-based system (CBSs).
Abstract: Cognitive radio (CR) is an enabling technology for numerous new capabilities such as dynamic spectrum access, spectrum markets, and self-organizing networks. To realize this diverse set of applications, CR researchers leverage a variety of artificial intelligence (AI) techniques. To help researchers better understand the practical implications of AI to their CR designs, this paper reviews several CR implementations that used the following AI techniques: artificial neural networks (ANNs), metaheuristic algorithms, hidden Markov models (HMMs), rule-based systems, ontology-based systems (OBSs), and case-based systems (CBSs). Factors that influence the choice of AI techniques, such as responsiveness, complexity, security, robustness, and stability, are discussed. To provide readers with a more concrete understanding, these factors are illustrated in an extended discussion of two CR designs.
TL;DR: This paper proposes convex estimators specifically for the RSS-based localization problems and applies the semidefinite relaxation technique to the derived nonconvex estimator, which improves the estimation performance.
Abstract: The received signal strength (RSS)-based approach to wireless localization offers the advantage of low cost and easy implementability. To circumvent the nonconvexity of the conventional maximum likelihood (ML) estimator, in this paper, we propose convex estimators specifically for the RSS-based localization problems. Both noncooperative and cooperative schemes are considered. We start with the noncooperative RSS-based localization problem and derive a nonconvex estimator that approximates the ML estimator but has no logarithm in the residual. Next, we apply the semidefinite relaxation technique to the derived nonconvex estimator and develop a convex estimator. To further improve the estimation performance, we append the ML estimator to the convex estimator with the result by the convex estimator as the initial point. We then extend these techniques to the cooperative localization problem. The corresponding Cramer-Rao lower bounds (CRLB) are derived as performance benchmarks. Our proposed convex estimators comply well with the RSS measurement model, and simulation results clearly demonstrate their superior performance for RSS-based wireless localization.
TL;DR: A more-realistic network model where a known and fixed number of nodes are independently distributed in a given region is considered and the distribution of the Euclidean internode distances follows a generalized beta distribution.
Abstract: In wireless networks, knowledge of internode distances is essential for performance analysis and protocol design. When determining distance distributions in random networks, the underlying nodal arrangement is almost universally taken to be a stationary Poisson point process. While this may be a good approximation in some cases, there are also certain shortcomings to this model, such as the fact that, in practical networks, the number of nodes in disjoint areas is not independent. This paper considers a more-realistic network model where a known and fixed number of nodes are independently distributed in a given region and characterizes the distribution of the Euclidean internode distances. The key finding is that, when the nodes are uniformly randomly placed inside a ball of arbitrary dimensions, the probability density function (pdf) of the internode distances follows a generalized beta distribution. This result is applied to study wireless network characteristics such as energy consumption, interference, outage, and connectivity.
TL;DR: A cross-layer opportunistic spectrum access and dynamic routing algorithm for cognitive radio networks, which is called the routing and dynamic spectrum-allocation (ROSA) algorithm, which aims to maximize the network throughput by performing joint routing, dynamic spectrum allocation, scheduling, and transmit power control.
Abstract: Throughput maximization is one of the main challenges in cognitive radio ad hoc networks, where the availability of local spectrum resources may change from time to time and hop by hop. For this reason, a cross-layer opportunistic spectrum access and dynamic routing algorithm for cognitive radio networks is proposed, which is called the routing and dynamic spectrum-allocation (ROSA) algorithm. Through local control actions, ROSA aims to maximize the network throughput by performing joint routing, dynamic spectrum allocation, scheduling, and transmit power control. Specifically, the algorithm dynamically allocates spectrum resources to maximize the capacity of links without generating harmful interference to other users while guaranteeing a bounded bit error rate (BER) for the receiver. In addition, the algorithm aims to maximize the weighted sum of differential backlogs to stabilize the system by giving priority to higher capacity links with a high differential backlog. The proposed algorithm is distributed, computationally efficient, and has bounded BER guarantees. ROSA is shown through numerical model-based evaluation and discrete-event packet-level simulations to outperform baseline solutions, leading to a high throughput, low delay, and fair bandwidth allocation.
TL;DR: A privacy-preserving system that guarantees message trustworthiness in vehicle-to-vehicle (V2V) communications and offers the possibility of a posteriori tracing the message generator and its endorsers is proposed.
Abstract: Vehicular ad hoc networks (VANETs) are being designed to improve traffic safety and efficiency. To meet this goal, the messages disseminated in VANETs must be trustworthy. We propose a privacy-preserving system that guarantees message trustworthiness in vehicle-to-vehicle (V2V) communications. Vehicle privacy is provided as long as a vehicle does not attempt to endorse the same message more than once. In spite of a message having been validly endorsed, if it is later found to be false, the system offers the possibility of a posteriori tracing the message generator and its endorsers. Our proposal demonstrates a number of distinctive features. The system is equipped with both a priori and a posteriori countermeasures. The threshold used for a priori endorsement can adaptively change according to the message urgency and traffic context, rather than being preset in the system design stage as in existing schemes. The verification of authenticated V2V messages is accelerated by batch message-processing techniques. Simulation results illustrate that the system maintains its performance under various traffic conditions.
TL;DR: The high accuracy of the method allows the detection of traffic isles as a distinct class and can be used in complex applications from collision avoidance to path planning.
Abstract: A new approach for the detection of the road surface and obstacles is presented. The high accuracy of the method allows the detection of traffic isles as a distinct class. The 3-D data inferred from dense stereo are transformed into a rectangular digital elevation map (DEM). Two classifiers are proposed, namely, density based and road surface based. The density-based obstacle classifier marks DEM cells as road or obstacles, using the density of 3-D points as a criterion. A quadratic road surface model is initially fitted by a random sample consensus (RANSAC) approach to the region in front of the ego vehicle. A region growing-like process refines this primary solution, driven by the 3-D uncertainty model of the stereo sensor. A robust global solution for the road surface is obtained. The road surface is used for discrimination between road, traffic isle, and obstacle points. Fusion and error filtering is performed on the results of the two classifiers. The proposed real-time algorithm was evaluated in an urban scenario and can be used in complex applications from collision avoidance to path planning.
TL;DR: A form of real-time multiagent reinforcement learning, which is known as decentralized Q-learning, is proposed to manage the aggregated interference generated by multiple WRAN systems.
Abstract: This paper deals with the problem of aggregated interference generated by multiple cognitive radios (CRs) at the receivers of primary (licensed) users. In particular, we consider a secondary CR system based on the IEEE 802.22 standard for wireless regional area networks (WRANs), and we model it as a multiagent system where the multiple agents are the different secondary base stations in charge of controlling the secondary cells. We propose a form of real-time multiagent reinforcement learning, which is known as decentralized Q-learning, to manage the aggregated interference generated by multiple WRAN systems. We consider both situations of complete and partial information about the environment. By directly interacting with the surrounding environment in a distributed fashion, the multiagent system is able to learn, in the first case, an efficient policy to solve the problem and, in the second case, a reasonably good suboptimal policy. Computational and memory requirement considerations are also presented, discussing two different options for uploading and processing the learning information. Simulation results, which are presented for both the upstream and downstream cases, reveal that the proposed approach is able to fulfill the primary-user interference constraints, without introducing signaling overhead in the system.
TL;DR: The results show that it is possible to detect wireless identity-based attacks with both a high detection rate and a low false-positive rate, thereby providing strong evidence of the effectiveness of the attack detector utilizing the spatial correlation of RSS and the attack localizer.
Abstract: Wireless networks are vulnerable to identity-based attacks, including spoofing and Sybil attacks, which allows for many other forms of attacks on the networks. Although the identity of a node can be verified through cryptographic authentication, authentication is not always possible, because it requires key management and additional infrastructural overhead. In this paper, we propose a method for detecting both spoofing and Sybil attacks by using the same set of techniques. We first propose a generalized attack-detection model that utilizes the spatial correlation of received signal strength (RSS) inherited from wireless nodes. We further provide a theoretical analysis of our approach. We then derive the test statistics for detection of identity-based attacks by using the K-means algorithm. Our attack detector is robust when handling the situations of attackers that use different transmission power levels to attack the detection scheme. We further describe how we integrated our attack detector into a real-time indoor localization system, which can also localize the positions of the attackers. We show that the positions of the attackers can be localized using either area- or point-based localization algorithms with the same relative errors as in the normal case. We further evaluated our methods through experimentation in two real office buildings using both an IEEE 802.11 (WiFi) network and an IEEE 802.15.4 (ZigBee) network. Our results show that it is possible to detect wireless identity-based attacks with both a high detection rate and a low false-positive rate, thereby providing strong evidence of the effectiveness of the attack detector utilizing the spatial correlation of RSS and the attack localizer.
TL;DR: The conditions of the signal-to-noise ratio (SNR) and the signal to interface ratios (SIRs) for cases of FDR showing a lower outage probability than that of the half-duplex relay (HDR) system under the target outage probability are obtained.
Abstract: This paper deals with a full-duplex relay (FDR) system over Rayleigh fading channels. The exact outage probability of FDR is derived as a closed form to consider interferences from full duplex. Then, we obtain the conditions of the signal-to-noise ratio (SNR) and the signal to interface ratios (SIRs) for cases of FDR showing a lower outage probability than that of the half-duplex relay (HDR) system under the target outage probability. According to this condition, FDR is superior to HDR with lower SIRs in the low-SNR region rather than in the high-SNR region. In addition, the target outage probability is only satisfied when the SNR and SIRs are within the boundaries. These boundaries vary due to the target rate, the channel states of each link, and the target outage probability.
TL;DR: An energy-efficient distributed multichannel MAC protocol for CR networks (MMAC-CR), which significantly improves performance by borrowing the licensed spectrum and protects primary users (PUs) from interference, even in hidden terminal situations.
Abstract: A cognitive radio (CR) network should be able to sense its environment and adapt communication to utilize the unused licensed spectrum without interfering with licensed users. In this paper, we look at CR-enabled networks with distributed control. As CR nodes need to hop from channel to channel to make the most use of the spectrum opportunities, we believe distributed multichannel medium access control (MAC) protocols to be key enablers for these networks. In addition to the spectrum scarcity, energy is rapidly becoming one of the major bottlenecks of wireless operations and has to be considered as a key design criterion. We present here an energy-efficient distributed multichannel MAC protocol for CR networks (MMAC-CR). Simulation results show that the proposed protocol significantly improves performance by borrowing the licensed spectrum and protects primary users (PUs) from interference, even in hidden terminal situations. Sensing costs are evaluated and shown to contribute only 5% to the total energy cost.
TL;DR: In this paper, an electromagnetic active suspension system that provides both additional stability and maneuverability by performing active roll and pitch control during cornering and braking, as well as eliminating road irregularities, is presented.
Abstract: This paper offers motivations for an electromagnetic active suspension system that provides both additional stability and maneuverability by performing active roll and pitch control during cornering and braking, as well as eliminating road irregularities, hence increasing both vehicle and passenger safety and drive comfort. Various technologies are compared with the proposed electromagnetic suspension system that uses a tubular permanent-magnet actuator (TPMA) with a passive spring. Based on on-road measurements and results from the literature, several specifications for the design of an electromagnetic suspension system are derived. The measured on-road movement of the passive suspension system is reproduced by electromagnetic actuation on a quarter car setup, proving the dynamic capabilities of an electromagnetic suspension system.
TL;DR: Simulation results show that road terrain preview enables fuel savings and the level of improvement depends on the cruising speed, control strategy, road profile, and the size of the battery.
Abstract: Energy-management strategy plays a critical role in high fuel economy that modern hybrid electric vehicles can achieve, yet a lack of information about future driving conditions is one of the limitations of fulfilling the maximum fuel economy potential of hybrid vehicles. Today, with wider deployment of vehicle telematic technologies, prediction of future driving conditions, e.g., road grade, is becoming more realistic. This paper evaluates the potential gain in fuel economy if road grade information is integrated into the energy management of hybrid vehicles. Real-world road geometry information is utilized in power-management decisions by using both dynamic programming (DP) and a standard equivalent consumption minimization strategy (ECMS). At the same time, two baseline control strategies with no future information are developed and validated for comparison purposes. Simulation results show that road terrain preview enables fuel savings. The level of improvement depends on the cruising speed, control strategy, road profile, and the size of the battery.
TL;DR: Dynamic motion models with and without road network information are used to further improve the accuracy via particle filters and the likelihood-calculation mechanism proposed for the particle filters is interpreted as a soft version (called BS-soft) of the BS-strict approach applied in the static case.
Abstract: This paper considers the problem of fingerprinting localization in wireless networks based on received-signal-strength (RSS) observations. First, the performance of static localization using power maps (PMs) is improved with a new approach called the base-station-strict (BS-strict) methodology, which emphasizes the effect of BS identities in the classical fingerprinting. Second, dynamic motion models with and without road network information are used to further improve the accuracy via particle filters. The likelihood-calculation mechanism proposed for the particle filters is interpreted as a soft version (called BS-soft) of the BS-strict approach applied in the static case. The results of the proposed approaches are illustrated and compared with an example whose data were collected from a WiMAX network in a challenging urban area in the capitol city of Brussels, Belgium.
TL;DR: Simulations for a space-time interference suppression application with a direct-sequence code-division multiple-access (DS-CDMA) system show that the proposed scheme outperforms in convergence and tracking the state-of-the-art reduced-rank schemes at a comparable complexity.
Abstract: This paper presents novel adaptive space-time reduced-rank interference-suppression least squares (LS) algorithms based on a joint iterative optimization of parameter vectors. The proposed space-time reduced-rank scheme consists of a joint iterative optimization of a projection matrix that performs dimensionality reduction and an adaptive reduced-rank parameter vector that yields the symbol estimates. The proposed techniques do not require singular value decomposition (SVD) and automatically find the best set of basis for reduced-rank processing. We present LS expressions for the design of the projection matrix and the reduced-rank parameter vector, and we conduct an analysis of the convergence properties of the LS algorithms. We then develop recursive LS (RLS) adaptive algorithms for their computationally efficient estimation and an algorithm that automatically adjusts the rank of the proposed scheme. A convexity analysis of the LS algorithms is carried out along with the development of a proof of convergence for the proposed algorithms. Simulations for a space-time interference suppression application with a direct-sequence code-division multiple-access (DS-CDMA) system show that the proposed scheme outperforms in convergence and tracking the state-of-the-art reduced-rank schemes at a comparable complexity.
TL;DR: A novel nonlinear feedback, which is called loop cancellation, is introduced and used to cancel the destabilizing effect of the constant-power loads.
Abstract: Tightly regulated closed-loop converters are problematic when used as a load since they tend to draw constant power and exhibit negative incremental resistance. This negative resistance causes stability problems for the feeder system, whether it is an input filter or another converter. In multiconverter power electronic systems, which exist in different land, sea, air, and space vehicles, including electric, hybrid, plug-in hybrid, and fuel-cell vehicles, there are many converters loaded by other converters. Therefore, the destabilizing effect of the load converters, which are called constant-power loads, is a major issue. In this paper, a novel nonlinear feedback, which is called loop cancellation, is introduced. This technique is used to cancel the destabilizing effect of the constant-power loads. Theoretically, any amount of constant-power load can be compensated by this technique, and it can identically be implemented on different types of converters. The effectiveness of the proposed technique has been verified by PSpice simulations and experimental results.
TL;DR: A new backoff decrement model is proposed that retains the simplicity of traditional DCF models while being able to take into account such a correlation between consecutive channel slots and statistical homogeneity.
Abstract: With the popularity of the IEEE 802.11 standards, many analytical saturation throughput studies for the distributed coordination function (DCF) have been reported. In this paper, we outline a number of issues and criticalities raised by previously proposed models. In particular, a careful look at backoff counter decrement rules allows us to conclude that, under saturation conditions, the slot immediately following a successful transmission can be accessed only by the station (STA) that has successfully transmitted in the previous channel access. Moreover, due to the specific acknowledgment (ACK) timeout setting adopted in the standard, the slot immediately following a collision cannot be accessed by any STA. Thus, the hypothesis of uncorrelation between consecutive channel slots and statistical homogeneity is not generally true. We propose a new backoff decrement model that retains the simplicity of traditional DCF models while being able to take into account such a correlation, and we compare the accuracy of our model with that of previously proposed approaches.
TL;DR: This paper offers a structured synthesis of the existing literature on autocorrelation and cross-correlation in wireless shadowing and attempts to fill existing gaps in the analysis of correlation models.
Abstract: There is emerging interest in more detailed models for wireless shadowing, which may include nonconstant shadowing variance, non-lognormal shadowing, and, most importantly, correlation between paths; we focus on this last aspect. This paper offers a structured synthesis of the existing literature on autocorrelation and cross-correlation in wireless shadowing and attempts to fill existing gaps in the analysis of correlation models. We make a survey of these models and argue, as has previously been observed, that certain models are not mathematically feasible, which may lead to problems in simulations or analysis. We then state some theorems that test whether the models are positive semidefinite, which is the central necessary condition for feasibility, and evaluate the existing models accordingly. Additionally, we evaluate the models according to their physical plausibility, which leads us to choose one model among many as arguably the best one in existence so far. This paper should be useful as a guide on how to implement shadowing correlation in one's work, how to choose an appropriate correlation model, and how to modify existing models or create new models so that they fulfill mathematical feasibility.
TL;DR: The statistical analysis shows that a non-complex ANN model performs very well compared with traditional propagation models with regard to prediction accuracy, complexity, and prediction time.
Abstract: This paper presents and evaluates artificial neural network (ANN) models used for macrocell path-loss prediction. Measurement data obtained by utilizing the IS-95 pilot signal from a commercial code-division multiple-access (CDMA) mobile network in rural Australia are used to train and evaluate the models. A simple neuron model and feed-forward networks with different numbers of hidden layers and neurons are evaluated regarding their training time, prediction accuracy, and generalization properties. Furthermore, different backpropagation training algorithms, such as gradient descent and Levenberg-Marquardt, are evaluated. The artificial neural network inputs are chosen to be distance to base station, parameters easily obtained from terrain path profiles, land usage, and vegetation type and density near the receiving antenna. The path-loss prediction results obtained by using the ANN models are evaluated against different versions of the semi-terrain based propagation model Recommendation ITU-R P.1546 and the Okumura-Hata model. The statistical analysis shows that a non-complex ANN model performs very well compared with traditional propagation models with regard to prediction accuracy, complexity, and prediction time. The average ANN prediction results were 1) maximum error: 22 dB; 2) mean error: 0 dB; and 3) standard deviation: 7 dB. A multilayered feed-forward network trained using the standard backpropagation algorithm was compared with a neuron model trained using the Levenberg-Marquardt algorithm. It was found that the training time decreases from 150 000 to 10 iterations, while the prediction accuracy is maintained.
TL;DR: Simulations and experimental results show the feasibility to implement this “one-source” multilevel system, intended for application in EVs from power ratings up to 150 kW, based on a small and cheap high-frequency link.
Abstract: The main advantage of asymmetrical multilevel inverters is the optimization of levels with a minimum number of power supplies. However, this optimized multilevel system still needs a large number of isolated and floating dc supplies, which makes these converters complicated to implement in electric vehicles (EVs), because the system will require many independent battery packs. In this paper, a very simple scheme, based on a small and cheap high-frequency link (HFL), allows the utilization of only one power supply for the complete multilevel inverter drive, with an inherent regulation of the voltages supplied among the H-bridges. It also allows voltage control with the full number of levels if the dc source is of a variable voltage characteristic. This paper is focused on a 27-level asymmetric inverter, but the strategy, using only one power supply, can be applied to converters with any number of levels. In particular, an asymmetrical 27-level converter needs nine isolated power supplies, and the proposed system reduces these nine sources to only one: the battery car. The topology also permits full regenerative braking working as a three-level converter. The proposed system is intended for application in EVs from power ratings up to 150 kW. Simulations and experimental results show the feasibility to implement this “one-source” multilevel system.
TL;DR: Finite-state Markov channels are considered in the relay-selection problem, and the formulation of the proposed relay- selection scheme is based on recent advances in stochastic control algorithms, which has an indexability property that dramatically reduces the computation and implementation complexity.
Abstract: Relay selection is crucial in improving the performance of wireless cooperative networks. Most previous works for relay selection use the current observed channel conditions to make the relay-selection decision for the subsequent frame. However, this memoryless channel assumption is often not realistic given the time-varying nature of some mobile environments. In this paper, we consider finite-state Markov channels in the relay-selection problem. Moreover, we also incorporate adaptive modulation and coding, as well as residual relay energy in the relay-selection process. The objectives of the proposed scheme are to increase spectral efficiency, mitigate error propagation, and maximize the network lifetime. The formulation of the proposed relay-selection scheme is based on recent advances in stochastic control algorithms. The obtained relay-selection policy has an indexability property that dramatically reduces the computation and implementation complexity. In addition, there is no need for a centralized control point in the network, and relays can freely join and leave from the set of potential relays. Simulation results are presented to show the effectiveness of the proposed scheme.
TL;DR: The original problem is a separable homogeneous quadratically constrained quadratic problem (QCQP), which is an NP-hard problem, even for uncertain CSI, which is reformulate to a relaxed semidefinite program (SDP) and investigates three different approaches based on convex programming.
Abstract: This paper studies the problem of robust downlink beamforming design in a multiuser multiple-input-single-output (MISO) cognitive radio network (CR-Net) in which multiple secondary users (SUs) coexist with multiple primary users (PUs) of a single-cell primary radio network (PR-Net). It is assumed that the channel-state information (CSI) for all relevant channels is imperfectly known, and the imperfectness of the CSI is modeled using a Euclidean ball-shaped uncertainty set. Our design objective is to minimize the transmit power of the SU-Transmitter (SU-Tx) while simultaneously achieving a lower bound on the received signal-to-interference-plus-noise ratio (SINR) for the SUs and imposing an upper limit on the interference power (IP) at the PUs. The design parameters at the SU-Tx are the beamforming weights, and the proposed methodology to solve the problem is based on the worst-case design scenario through which the performance metrics of the design are immune to variations in the channels. The original problem is a separable homogeneous quadratically constrained quadratic problem (QCQP), which is an NP-hard problem, even for uncertain CSI. We reformulate our original design problem to a relaxed semidefinite program (SDP) and then investigate three different approaches based on convex programming. Finally, simulation results are provided to validate the robustness of the proposed methods.