A novel Automatic repeat ReQuest (ARQ) protocol called cooperative ARQ is presented in this let-ter, where a relay terminal is requested to retransmit an erroneously received packet, instead of the source ter-minal. T...A novel Automatic repeat ReQuest (ARQ) protocol called cooperative ARQ is presented in this let-ter, where a relay terminal is requested to retransmit an erroneously received packet, instead of the source ter-minal. The data link layer Packet Error Rate (PER) performance of cooperative ARQ is derived in correlated wireless channel. The results show that even though the relay-destination channel is worse than the source-destination channel, the new protocol outperforms the traditional one as long as the average SNR of the relay-destination channel is better than a certain threshold. It is also demonstrated that a second order diversity gain can be achieved with the cooperative ARQ protocol.展开更多
Uncertainty characterization has become increasingly recognized as an integral component in thematic mapping based on remotely sensed imagery, and descriptors such as percent correctly classified pixels (PCC) and Kapp...Uncertainty characterization has become increasingly recognized as an integral component in thematic mapping based on remotely sensed imagery, and descriptors such as percent correctly classified pixels (PCC) and Kappa coefficients of agreement have been devised as thematic accuracy metrics. However, such spatially averaged measures about accuracy neither offer hints about spatial variation in misclassification, nor are useful for quantifying error margins in derivatives, such as the areal extents of different land cover types and the land cover change statistics. Such limitations originate from the deficiency that spatial dependency is not accommodated in the conventional methods for error analysis. Geostatistics provides a good framework for uncertainty characterization in land cover information. Methods for predicting and propagating misclassification will be described on the basis of indicator samples and covariates, such as spectrally derived posteriori probabilities. An experiment using simulated datasets was carried out to quantify the error in land cover change derived from postclassification comparison. It was found that significant biases result from applying joint probability rules assuming temporal independence between misclassifications across time, thus emphasizing the need for the stochastic simulation in error modeling. Further investigations, incorporating indicators and probabilistic data for mapping and propagating misclassification, are anticipated.展开更多
基金Supported by the National Natural Science Foundation of China (No.60472079), and Natural Science Founda-tion of Zhejiang Province (No.Z104252).
文摘A novel Automatic repeat ReQuest (ARQ) protocol called cooperative ARQ is presented in this let-ter, where a relay terminal is requested to retransmit an erroneously received packet, instead of the source ter-minal. The data link layer Packet Error Rate (PER) performance of cooperative ARQ is derived in correlated wireless channel. The results show that even though the relay-destination channel is worse than the source-destination channel, the new protocol outperforms the traditional one as long as the average SNR of the relay-destination channel is better than a certain threshold. It is also demonstrated that a second order diversity gain can be achieved with the cooperative ARQ protocol.
基金Supported by the National 973 Program of China (No. 2006CB701302)the Hubei Department of Science and Technology (No. 2007ABA276)
文摘Uncertainty characterization has become increasingly recognized as an integral component in thematic mapping based on remotely sensed imagery, and descriptors such as percent correctly classified pixels (PCC) and Kappa coefficients of agreement have been devised as thematic accuracy metrics. However, such spatially averaged measures about accuracy neither offer hints about spatial variation in misclassification, nor are useful for quantifying error margins in derivatives, such as the areal extents of different land cover types and the land cover change statistics. Such limitations originate from the deficiency that spatial dependency is not accommodated in the conventional methods for error analysis. Geostatistics provides a good framework for uncertainty characterization in land cover information. Methods for predicting and propagating misclassification will be described on the basis of indicator samples and covariates, such as spectrally derived posteriori probabilities. An experiment using simulated datasets was carried out to quantify the error in land cover change derived from postclassification comparison. It was found that significant biases result from applying joint probability rules assuming temporal independence between misclassifications across time, thus emphasizing the need for the stochastic simulation in error modeling. Further investigations, incorporating indicators and probabilistic data for mapping and propagating misclassification, are anticipated.
