The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Rui Zhang - One of the best experts on this subject based on the ideXlab platform.
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intelligent reflecting surface assisted multi user ofdma channel estimation and Training Design
IEEE Transactions on Wireless Communications, 2020Co-Authors: Beixiong Zheng, Changsheng You, Rui ZhangAbstract:To achieve the full passive beamforming gains of intelligent reflecting surface (IRS), accurate channel state information (CSI) is indispensable but practically challenging to acquire, due to the excessive amount of channel parameters to be estimated which increases with the number of IRS reflecting elements as well as that of IRS-served users. To tackle this challenge, we propose in this paper two efficient channel estimation schemes for different channel setups in an IRS-assisted multiuser broadband communication system employing the orthogonal frequency division multiple access (OFDMA). The first channel estimation scheme, which estimates the CSI of all users in parallel simultaneously at the access point (AP), is applicable for arbitrary frequency-selective fading channels. In contrast, the second channel estimation scheme, which exploits a key property that all users share the same (common) IRS-AP channel to enhance the Training efficiency and support more users, is proposed for the typical scenario with line-of-sight (LoS) dominant user-IRS channels. For the two proposed channel estimation schemes, we further optimize their corresponding Training Designs (including pilot tone allocations for all users and IRS time-varying reflection pattern) to minimize the channel estimation error. Moreover, we derive and compare the fundamental limits on the minimum Training overhead and the maximum number of supportable users of these two schemes. Simulation results verify the effectiveness of the proposed channel estimation schemes and Training Designs, and show their significant performance improvement over various benchmark schemes.
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optimized Training Design for wireless energy transfer
IEEE Transactions on Communications, 2015Co-Authors: Yong Zeng, Rui ZhangAbstract:Radio-frequency (RF) enabled wireless energy trans- fer (WET), as a promising solution to provide cost-effective and reliable power supplies for energy-constrained wireless networks, has drawn growing interests recently. To overcome the significant propagation loss over distance, employing multi-antennas at the energy transmitter (ET) to more efficiently direct wireless energy to desired energy receivers (ERs), termed energy beamforming ,i s an essential technique for enabling WET. However, the achievable gain of energy beamforming crucially depends on the available channel state information (CSI) at the ET, which needs to be acquired practically. In this paper, we study the Design of an efficient channel acquisition method for a point-to-point multiple- input multiple-output (MIMO) WET system by exploiting the channel reciprocity, i.e., the ET estimates the CSI via dedicated reverse-link Training from the ER. Considering the limited energy availability at the ER, the Training strategy should be carefully Designed so that the channel can be estimated with sufficient accuracy, and yet without consuming excessive energy at the ER. To this end, we propose to maximize the net harvested energy at the ER, which is the average harvested energy offset by that used for channel Training. An optimization problem is formulated for the Training Design over MIMO Rician fading channels, including the subset of ER antennas to be trained, as well as the Training time and power allocated. Closed-form solutions are obtained for some special scenarios, based on which useful insights are drawn on when Training should be employed to improve the net transferred energy in MIMO WET systems.
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optimized Training for net energy maximization in multi antenna wireless energy transfer over frequency selective channel
arXiv: Information Theory, 2015Co-Authors: Yong Zeng, Rui ZhangAbstract:This paper studies the Training Design problem for multiple-input single-output (MISO) wireless energy transfer (WET) systems in frequency-selective channels, where the frequency-diversity and energy-beamforming gains can be both reaped to maximize the transferred energy by efficiently learning the channel state information (CSI) at the energy transmitter (ET). By exploiting channel reciprocity, a new two-phase channel Training scheme is proposed to achieve the diversity and beamforming gains, respectively. In the first phase, pilot signals are sent from the energy receiver (ER) over a selected subset of the available frequency sub-bands, through which the ET determines a certain number of "strongest" sub-bands with largest antenna sum-power gains and sends their indices to the ER. In the second phase, the selected sub-bands are further trained by the ER, so that the ET obtains a refined estimate of the corresponding MISO channels to implement energy beamforming for WET. A Training Design problem is formulated and optimally solved, which takes into account the channel Training overhead by maximizing the net harvested energy at the ER, defined as the average harvested energy offset by that consumed in the two-phase Training. Moreover, asymptotic analysis is obtained for systems with a large number of antennas or a large number of sub-bands to gain useful insights on the optimal Training Design. Finally, numerical results are provided to corroborate our analysis and show the effectiveness of the proposed scheme that optimally balances the diversity and beamforming gains achieved in MISO WET systems with limited-energy Training.
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Optimized Training for Net Energy Maximization in Multi-Antenna Wireless Energy Transfer Over Frequency-Selective Channel
IEEE Transactions on Communications, 2015Co-Authors: Yong Zeng, Rui ZhangAbstract:This paper studies the Training Design problem for multiple-input single-output (MISO) wireless energy transfer (WET) systems in frequency-selective channels, where the frequency-diversity and energy-beamforming gains can be both reaped to maximize the transferred energy by efficiently learning the channel state information (CSI) at the energy transmitter (ET). By exploiting channel reciprocity, a new two-phase channel Training scheme is proposed to achieve the diversity and beamforming gains, respectively. In the first phase, pilot signals are sent from the energy receiver (ER) over a selected subset of the available frequency sub-bands, through which the ET determines a certain number of “strongest” sub-bands with largest antenna sum-power gains and sends their indices to the ER. In the second phase, the selected sub-bands are further trained by the ER, so that the ET obtains a refined estimate of the corresponding MISO channels to implement energy beamforming for WET. A Training Design problem is formulated and optimally solved, which takes into account the channel Training overhead by maximizing the net harvested energy at the ER, defined as the average harvested energy offset by that consumed in the two-phase Training. Moreover, asymptotic analysis is obtained for systems with a large number of antennas or a large number of sub-bands to gain useful insights on the optimal Training Design. Finally, numerical results are provided to corroborate our analysis and show the effectiveness of the proposed scheme that optimally balances the diversity and beamforming gains in MISO WET systems with limited-energy Training.
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optimized Training Design for wireless energy transfer
arXiv: Information Theory, 2014Co-Authors: Yong Zeng, Rui ZhangAbstract:Radio-frequency (RF) enabled wireless energy transfer (WET), as a promising solution to provide cost-effective and reliable power supplies for energy-constrained wireless networks, has drawn growing interests recently. To overcome the significant propagation loss over distance, employing multi-antennas at the energy transmitter (ET) to more efficiently direct wireless energy to desired energy receivers (ERs), termed \emph{energy beamforming}, is an essential technique for enabling WET. However, the achievable gain of energy beamforming crucially depends on the available channel state information (CSI) at the ET, which needs to be acquired practically. In this paper, we study the Design of an efficient channel acquisition method for a point-to-point multiple-input multiple-output (MIMO) WET system by exploiting the channel reciprocity, i.e., the ET estimates the CSI via dedicated reverse-link Training from the ER. Considering the limited energy availability at the ER, the Training strategy should be carefully Designed so that the channel can be estimated with sufficient accuracy, and yet without consuming excessive energy at the ER. To this end, we propose to maximize the \emph{net} harvested energy at the ER, which is the average harvested energy offset by that used for channel Training. An optimization problem is formulated for the Training Design over MIMO Rician fading channels, including the subset of ER antennas to be trained, as well as the Training time and power allocated. Closed-form solutions are obtained for some special scenarios, based on which useful insights are drawn on when Training should be employed to improve the net transferred energy in MIMO WET systems.
Yong Zeng - One of the best experts on this subject based on the ideXlab platform.
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optimized Training Design for wireless energy transfer
IEEE Transactions on Communications, 2015Co-Authors: Yong Zeng, Rui ZhangAbstract:Radio-frequency (RF) enabled wireless energy trans- fer (WET), as a promising solution to provide cost-effective and reliable power supplies for energy-constrained wireless networks, has drawn growing interests recently. To overcome the significant propagation loss over distance, employing multi-antennas at the energy transmitter (ET) to more efficiently direct wireless energy to desired energy receivers (ERs), termed energy beamforming ,i s an essential technique for enabling WET. However, the achievable gain of energy beamforming crucially depends on the available channel state information (CSI) at the ET, which needs to be acquired practically. In this paper, we study the Design of an efficient channel acquisition method for a point-to-point multiple- input multiple-output (MIMO) WET system by exploiting the channel reciprocity, i.e., the ET estimates the CSI via dedicated reverse-link Training from the ER. Considering the limited energy availability at the ER, the Training strategy should be carefully Designed so that the channel can be estimated with sufficient accuracy, and yet without consuming excessive energy at the ER. To this end, we propose to maximize the net harvested energy at the ER, which is the average harvested energy offset by that used for channel Training. An optimization problem is formulated for the Training Design over MIMO Rician fading channels, including the subset of ER antennas to be trained, as well as the Training time and power allocated. Closed-form solutions are obtained for some special scenarios, based on which useful insights are drawn on when Training should be employed to improve the net transferred energy in MIMO WET systems.
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optimized Training for net energy maximization in multi antenna wireless energy transfer over frequency selective channel
arXiv: Information Theory, 2015Co-Authors: Yong Zeng, Rui ZhangAbstract:This paper studies the Training Design problem for multiple-input single-output (MISO) wireless energy transfer (WET) systems in frequency-selective channels, where the frequency-diversity and energy-beamforming gains can be both reaped to maximize the transferred energy by efficiently learning the channel state information (CSI) at the energy transmitter (ET). By exploiting channel reciprocity, a new two-phase channel Training scheme is proposed to achieve the diversity and beamforming gains, respectively. In the first phase, pilot signals are sent from the energy receiver (ER) over a selected subset of the available frequency sub-bands, through which the ET determines a certain number of "strongest" sub-bands with largest antenna sum-power gains and sends their indices to the ER. In the second phase, the selected sub-bands are further trained by the ER, so that the ET obtains a refined estimate of the corresponding MISO channels to implement energy beamforming for WET. A Training Design problem is formulated and optimally solved, which takes into account the channel Training overhead by maximizing the net harvested energy at the ER, defined as the average harvested energy offset by that consumed in the two-phase Training. Moreover, asymptotic analysis is obtained for systems with a large number of antennas or a large number of sub-bands to gain useful insights on the optimal Training Design. Finally, numerical results are provided to corroborate our analysis and show the effectiveness of the proposed scheme that optimally balances the diversity and beamforming gains achieved in MISO WET systems with limited-energy Training.
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Optimized Training for Net Energy Maximization in Multi-Antenna Wireless Energy Transfer Over Frequency-Selective Channel
IEEE Transactions on Communications, 2015Co-Authors: Yong Zeng, Rui ZhangAbstract:This paper studies the Training Design problem for multiple-input single-output (MISO) wireless energy transfer (WET) systems in frequency-selective channels, where the frequency-diversity and energy-beamforming gains can be both reaped to maximize the transferred energy by efficiently learning the channel state information (CSI) at the energy transmitter (ET). By exploiting channel reciprocity, a new two-phase channel Training scheme is proposed to achieve the diversity and beamforming gains, respectively. In the first phase, pilot signals are sent from the energy receiver (ER) over a selected subset of the available frequency sub-bands, through which the ET determines a certain number of “strongest” sub-bands with largest antenna sum-power gains and sends their indices to the ER. In the second phase, the selected sub-bands are further trained by the ER, so that the ET obtains a refined estimate of the corresponding MISO channels to implement energy beamforming for WET. A Training Design problem is formulated and optimally solved, which takes into account the channel Training overhead by maximizing the net harvested energy at the ER, defined as the average harvested energy offset by that consumed in the two-phase Training. Moreover, asymptotic analysis is obtained for systems with a large number of antennas or a large number of sub-bands to gain useful insights on the optimal Training Design. Finally, numerical results are provided to corroborate our analysis and show the effectiveness of the proposed scheme that optimally balances the diversity and beamforming gains in MISO WET systems with limited-energy Training.
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optimized Training Design for wireless energy transfer
arXiv: Information Theory, 2014Co-Authors: Yong Zeng, Rui ZhangAbstract:Radio-frequency (RF) enabled wireless energy transfer (WET), as a promising solution to provide cost-effective and reliable power supplies for energy-constrained wireless networks, has drawn growing interests recently. To overcome the significant propagation loss over distance, employing multi-antennas at the energy transmitter (ET) to more efficiently direct wireless energy to desired energy receivers (ERs), termed \emph{energy beamforming}, is an essential technique for enabling WET. However, the achievable gain of energy beamforming crucially depends on the available channel state information (CSI) at the ET, which needs to be acquired practically. In this paper, we study the Design of an efficient channel acquisition method for a point-to-point multiple-input multiple-output (MIMO) WET system by exploiting the channel reciprocity, i.e., the ET estimates the CSI via dedicated reverse-link Training from the ER. Considering the limited energy availability at the ER, the Training strategy should be carefully Designed so that the channel can be estimated with sufficient accuracy, and yet without consuming excessive energy at the ER. To this end, we propose to maximize the \emph{net} harvested energy at the ER, which is the average harvested energy offset by that used for channel Training. An optimization problem is formulated for the Training Design over MIMO Rician fading channels, including the subset of ER antennas to be trained, as well as the Training time and power allocated. Closed-form solutions are obtained for some special scenarios, based on which useful insights are drawn on when Training should be employed to improve the net transferred energy in MIMO WET systems.
Feifei Gao - One of the best experts on this subject based on the ideXlab platform.
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channel estimation for plnc under frequency flat fading scenario
2014Co-Authors: Feifei Gao, Chengwen Xing, Gongpu WangAbstract:In this chapter, we consider channel estimation for PLNC system in a frequency flat fading scenario. We propose a two-phase Training protocol for channel estimation that can be easily embedded into the two-phase data transmission. Each terminal targets at estimating the individual channel parameters. We first derive the maximum-likelihood (ML) estimator, which is nonlinear and differs much from the conventional least-square (LS) estimator. Due to the difficulty in obtaining a closed-form expression of the mean square error (MSE) for the ML estimator, we resort to the Cramer-Rao lower bound (CRLB) of the estimation MSE to Design the optimal Training sequence. In the mean time, we introduce a new type of estimator that aims at maximizing the effective receive signal-to-noise ratio (SNR) after taking into consideration the channel estimation errors, referred to as the linear maximum signal-to-noise ratio (LMSNR) estimator. Furthermore, we prove that orthogonal Training Design is optimal for both the CRLB- and the LMSNR-based Design criteria. Finally, simulations are presented to corroborate the proposed studies.
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optimal Training Design for individual channel estimation in two way relay networks
IEEE Transactions on Signal Processing, 2012Co-Authors: Shun Zhang, Feifei Gao, Changxing PeiAbstract:This correspondence considers the optimal Training Design in a classical three-node amplify-and-forward two-way relay network (TWRN) that targets at estimating the individual channel between each source node and the relay node. The transmission environment is assumed to be frequency selective and the orthogonal-frequency-division multiplexing (OFDM) modulation is adopted. We derive the Bayesian Cramer-Rao bound (CRB) for the individual channel estimation, from which the optimal Training is obtained. Extensive numerical results are provided to corroborate the proposed studies.
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channel estimation and Training Design for two way relay networks in time selective fading environments
IEEE Transactions on Wireless Communications, 2011Co-Authors: Gongpu Wang, Feifei Gao, Wen Chen, Chintha TellamburaAbstract:In this paper, channel estimation and Training sequence Design are considered for amplify-and-forward (AF)-based two-way relay networks (TWRNs) in a time-selective fading environment. A new complex-exponential basis expansion model (CE-BEM) is proposed to represent the mobile-to-mobile time-varying channels. To estimate such channels, a novel pilot symbol-aided transmission scheme is developed such that a low complex linear approach can estimate the BEM coefficients of the convoluted channels. More essentially, two algorithms are Designed to extract the BEM coefficients of the individual channels. The optimal Training parameters, including the number of the pilot symbols, the placement of the pilot symbols, and the power allocation to the pilot symbols, are derived by minimizing the channel mean-square error (MSE). The selections of the system parameters are thoroughly discussed in order to guide practical system Design. Finally, extensive numerical results are provided to corroborate the proposed studies.
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channel estimation and Training Design for two way relay networks with power allocation
IEEE Transactions on Wireless Communications, 2010Co-Authors: Bin Jiang, Feifei Gao, Xiqi Gao, Arumugam NallanathanAbstract:In this paper, we propose a new channel estimation prototype for the amplify-and-forward (AF) two-way relay network (TWRN). By allowing the relay to first estimate the channel parameters and then allocate the powers for these parameters, the final data detection at the source terminals could be optimized. Specifically, we consider the classical three-node TWRN where two source terminals exchange their information via a single relay node in between and adopt the maximum likelihood (ML) channel estimation at the relay node. Two different power allocation schemes to the Training signals are then proposed to maximize the average effective signal-to-noise ratio (AESNR) of the data detection and minimize the mean-square-error (MSE) of the channel estimation, respectively. The optimal/sub-optimal Training Designs for both schemes are found as well. Simulation results corroborate the advantages of the proposed technique over the existing ones.
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optimal channel estimation and Training Design for two way relay networks
IEEE Transactions on Communications, 2009Co-Authors: Feifei Gao, Rui Zhang, Yingchang LiangAbstract:In this work, we consider the two-way relay network (TWRN) where two terminals exchange their information through a relay node in a bi-directional manner and study the Training-based channel estimation under the amplify-and-forward (AF) relay scheme. We propose a two-phase Training protocol for channel estimation: in the first phase, the two terminals send their Training signals concurrently to the relay; and in the second phase, the relay amplifies the received signal and broadcasts it to both terminals. Each terminal then estimates the channel parameters required for data detection. First, we assume the channel parameters to be deterministic and derive the maximum-likelihood (ML) -based estimator. It is seen that the newly derived ML estimator is nonlinear and differs from the conventional least-square (LS) estimator. Due to the difficulty in obtaining a closed-form expression of the mean square error (MSE) for the ML estimator, we resort to the Crameacuter-Rao lower bound (CRLB) on the estimation MSE for Design of optimal Training sequence. Secondly, we consider stochastic channels and focus on the class of linear estimators. In contrast to the conventional linear minimum-mean-square-error (LMMSE) -based estimator, we introduce a new type of estimator that aims at maximizing the effective receive signal-to-noise ratio (SNR) after taking into consideration the channel estimation errors, thus referred to as the linear maximum SNR (LMSNR) estimator. Furthermore, we prove that orthogonal Training Design is optimal for both the CRLB- and the LMSNR-based Design criteria. Finally, simulations are conducted to corroborate the proposed studies.
Arumugam Nallanathan - One of the best experts on this subject based on the ideXlab platform.
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channel estimation and Training Design for two way relay networks with power allocation
IEEE Transactions on Wireless Communications, 2010Co-Authors: Bin Jiang, Feifei Gao, Xiqi Gao, Arumugam NallanathanAbstract:In this paper, we propose a new channel estimation prototype for the amplify-and-forward (AF) two-way relay network (TWRN). By allowing the relay to first estimate the channel parameters and then allocate the powers for these parameters, the final data detection at the source terminals could be optimized. Specifically, we consider the classical three-node TWRN where two source terminals exchange their information via a single relay node in between and adopt the maximum likelihood (ML) channel estimation at the relay node. Two different power allocation schemes to the Training signals are then proposed to maximize the average effective signal-to-noise ratio (AESNR) of the data detection and minimize the mean-square-error (MSE) of the channel estimation, respectively. The optimal/sub-optimal Training Designs for both schemes are found as well. Simulation results corroborate the advantages of the proposed technique over the existing ones.
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optimal Training Design for channel estimation in decode and forward relay networks with individual and total power constraints
IEEE Transactions on Signal Processing, 2008Co-Authors: Feifei Gao, Tao Cui, Arumugam NallanathanAbstract:In this paper, we study the channel estimation and the optimal Training Design for relay networks that operate under the decode-and-forward (DF) strategy with the knowledge of the interference covariance. In addition to the total power constraint on all the relays, we introduce individual power constraint for each relay, which reflects the practical scenario where all relays are separated from one another. Considering the individual power constraint for the relay networks is the major difference from that in the traditional point-to-point communication systems where only a total power constraint exists for all colocated antennas. Two types of channel estimation are involved: maximum likelihood (ML) and minimum mean square error (MMSE). For ML channel estimation, the channels are assumed as deterministic and the optimal Training results from an efficient multilevel waterfilling type solution that is derived from the majorization theory. For MMSE channel estimation, however, the second-order statistics of the channels are assumed known and the general optimization problem turns out to be nonconvex. We instead consider three special yet reasonable scenarios. The problem in the first scenario is convex and could be efficiently solved by state-of-the-art optimization tools. Closed-form waterfilling type solutions are found in the remaining two scenarios, of which the first one has an interesting physical interpretation as pouring water into caves.
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on channel estimation and optimal Training Design for amplify and forward relay networks
IEEE Transactions on Wireless Communications, 2008Co-Authors: Feifei Gao, Tao Cui, Arumugam NallanathanAbstract:In this paper, we provide a complete study on the Training based channel estimation issues for relay networks that employ the amplify-and-forward (AF) transmission scheme. We first point out that separately estimating the channel from source to relay and relay to destination suffers from many drawbacks. Then we provide a new estimation scheme that directly estimates the overall channels from the source to the destination. The proposed channel estimation well serves the AF based space time coding (STC) that was recently developed. There exists many differences between the proposed channel estimation and that in the traditional single input single out (SISO) and multiple input single output (MISO) systems. For example, a relay must linearly precode its received Training sequence by a sophisticatedly Designed matrix in order to minimize the channel estimation error. Besides, each relay node is individually constrained by a different power requirement because of the non-cooperation among all relay nodes. We study both the linear least-square (LS) estimator and the minimum mean-square-error (MMSE) estimator. The corresponding optimal Training sequences, as well as the optimal preceding matrices are derived from an efficient convex optimization process.
Eduardo Salas - One of the best experts on this subject based on the ideXlab platform.
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leadership Training Design delivery and implementation a meta analysis
Journal of Applied Psychology, 2017Co-Authors: Christina N Lacerenza, Denise L Reyes, Shannon L Marlow, Dana L Joseph, Eduardo SalasAbstract:Recent estimates suggest that although a majority of funds in organizational Training budgets tend to be allocated to leadership Training (Ho, 2016; O'Leonard, 2014), only a small minority of organizations believe their leadership Training programs are highly effective (Schwartz, Bersin, & Pelster, 2014), calling into question the effectiveness of current leadership development initiatives. To help address this issue, this meta-analysis estimates the extent to which leadership Training is effective and identifies the conditions under which these programs are most effective. In doing so, we estimate the effectiveness of leadership Training across four criteria (reactions, learning, transfer, and results; Kirkpatrick, 1959) using only employee data and we examine 15 moderators of Training Design and delivery to determine which elements are associated with the most effective leadership Training interventions. Data from 335 independent samples suggest that leadership Training is substantially more effective than previously thought, leading to improvements in reactions (δ = .63), learning (δ = .73), transfer (δ = .82), and results (δ = .72), the strength of these effects differs based on various Design, delivery, and implementation characteristics. Moderator analyses support the use of needs analysis, feedback, multiple delivery methods (especially practice), spaced Training sessions, a location that is on-site, and face-to-face delivery that is not self-administered. Results also suggest that the content of Training, attendance policy, and duration influence the effectiveness of the Training program. Practical implications for Training development and theoretical implications for leadership and Training literatures are discussed. (PsycINFO Database Record
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simulation based team Training at the sharp end a qualitative study of simulation based team Training Design implementation and evaluation in healthcare
Journal of Emergencies Trauma and Shock, 2010Co-Authors: Sallie J Weaver, Eduardo Salas, Rebecca Lyons, Elizabeth H Lazzara, Michael A Rosen, Deborah Diazgranados, Julia G Grim, Jeffery S Augenstein, David J Birnbach, Heidi KingAbstract:This article provides a qualitative review of the published literature dealing with the Design, implementation, and evaluation of simulation-based team Training (SBTT) in healthcare with the purpose of providing synthesis of the present state of the science to guide practice and future research. A systematic literature review was conducted and produced 27 articles meeting the inclusion criteria. These articles were coded using a low-inference content analysis coding scheme Designed to extract important information about the Training program. Results are summarized in 10 themes describing important considerations for what occurs before, during, and after a Training event. Both across disciplines and within Emergency Medicine (EM), SBTT has been shown to be an effective method for increasing teamwork skills. However, the literature to date has underspecified some of the fundamental features of the Training programs, impeding the dissemination of lessons learned. Implications of this study are discussed for team Training in EM.
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measuring the importance of teamwork the reliability and validity of job task analysis indices for team Training Design
Military Psychology, 1994Co-Authors: Clint A. Bowers, David P. Baker, Eduardo SalasAbstract:Training interventions Designed to improve coordination and communication in the cockpit increasingly emphasize the teaching of specific coordination skills. However, there is little guidance in the literature regarding the manner by which these skills should be selected. This investigation compared a variety of task- importance indices used previously with individual tasks in predicting the overall importance of team tasks. All of the indices demonstrated relatively poor reliability. Composite indices, including one newly derived index, demon- strated greater validity. The results are discussed in terms of implications for future research and for team-Training Design.
