摘要 :
The paper introduces a new diversity algorithm that uses real valued weights to reduce the complexity of coherent combining. The approach starts with Selection Diversity but allows any signal branch to contribute to the output by ...
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The paper introduces a new diversity algorithm that uses real valued weights to reduce the complexity of coherent combining. The approach starts with Selection Diversity but allows any signal branch to contribute to the output by a simple real valued scaling operation. Channel estimation is performed only on the branch with the strongest signal and the rest of the combining weights are then computed directly from the received signals themselves. This simplifies the weight computation and the resulting real-valued weights allows for combining to be performed at the analog or RF/IF stage, reducing the need for multiple receive chains required by digital combining. Simulation results reveal that the performance of the proposed algorithm is just 0.7 dB and 1.6 dB below optimum for 2 and 4 antennas respectively in independent Rayleigh fading channels.
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摘要 :
The paper introduces a new diversity algorithm that uses real valued weights to reduce the complexity of coherent combining. The approach starts with Selection Diversity but allows any signal branch to contribute to the output by ...
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The paper introduces a new diversity algorithm that uses real valued weights to reduce the complexity of coherent combining. The approach starts with Selection Diversity but allows any signal branch to contribute to the output by a simple real valued scaling operation. Channel estimation is performed only on the branch with the strongest signal and the rest of the combining weights are then computed directly from the received signals themselves. This simplifies the weight computation and the resulting real-valued weights allows for combining to be performed at the analog or RF/IF stage, reducing the need for multiple receive chains required by digital combining. Simulation results reveal that the performance of the proposed algorithm is just 0.7 dB and 1.6 dB below optimum for 2 and 4 antennas respectively in independent Rayleigh fading channels.
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摘要 :
The paper introduces a new diversity algorithm that uses real valued weights to reduce the complexity of coherent combining. The approach starts with Selection Diversity but allows any signal branch to contribute to the output by ...
展开
The paper introduces a new diversity algorithm that uses real valued weights to reduce the complexity of coherent combining. The approach starts with Selection Diversity but allows any signal branch to contribute to the output by a simple real valued scaling operation. Channel estimation is performed only on the branch with the strongest signal and the rest of the combining weights are then computed directly from the received signals themselves. This simplifies the weight computation and the resulting real-valued weights allows for combining to be performed at the analog or RF/IF stage, reducing the need for multiple receive chains required by digital combining. Simulation results reveal that the performance of the proposed algorithm is just 0.7 dB and 1.6 dB below optimum for 2 and 4 antennas respectively in independent Rayleigh fading channels.
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摘要 :
We have developed a multichannel output coupler enabling coherent beam combining in the two-micron spectral range. We demonstrate experimentally the combining of multiple beams utilizing a set of thulium-doped, double-clad, single...
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We have developed a multichannel output coupler enabling coherent beam combining in the two-micron spectral range. We demonstrate experimentally the combining of multiple beams utilizing a set of thulium-doped, double-clad, single-mode optical fibers. The fibers are pumped by fiber-pigtailed laser diodes at 793 nm via (4+1)xl pump-signal combiners. The combiners are fabricated using vanishing-core technology, which allows for preservation of the mode field through the tapering process. The output of individual lasing channels is generated over a 20 nm spectral band at around 1970 nm without any spectrally selective elements. The slope efficiency of individual lasers is approximately 50% with respect to the pump power. All lasing channels are fused into a monolithic silica structure with channel spacing of 32 microns on a triangular lattice. The fused assembly is fabricated in a glass microforming tapering process with a draw ratio of 3.9. In the process, the mode field at 1970 nm expands slightly to about 15 microns at the end of the taper, while the outer diameter is reduced from 2.3 mm to approximately 590 microns. The tapered end is straight polished and fusion spliced to a 600-micron diameter silica glass rod. The rod is cleaved and optically polished at zero degrees. The length of the rod is one half of the Talbot distance for optimal coherent beam combining. In the experiment, an antiphase supermode is observed when only the seven inner channels are pumped, and an in-phase supermode is excited when the number of channels is nineteen or larger.
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摘要 :
We have developed a multichannel output coupler enabling coherent beam combining in the two-micron spectral range. We demonstrate experimentally the combining of multiple beams utilizing a set of thulium-doped, double-clad, single...
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We have developed a multichannel output coupler enabling coherent beam combining in the two-micron spectral range. We demonstrate experimentally the combining of multiple beams utilizing a set of thulium-doped, double-clad, single-mode optical fibers. The fibers are pumped by fiber-pigtailed laser diodes at 793 nm via (4+1)xl pump-signal combiners. The combiners are fabricated using vanishing-core technology, which allows for preservation of the mode field through the tapering process. The output of individual lasing channels is generated over a 20 nm spectral band at around 1970 nm without any spectrally selective elements. The slope efficiency of individual lasers is approximately 50% with respect to the pump power. All lasing channels are fused into a monolithic silica structure with channel spacing of 32 microns on a triangular lattice. The fused assembly is fabricated in a glass microforming tapering process with a draw ratio of 3.9. In the process, the mode field at 1970 nm expands slightly to about 15 microns at the end of the taper, while the outer diameter is reduced from 2.3 mm to approximately 590 microns. The tapered end is straight polished and fusion spliced to a 600-micron diameter silica glass rod. The rod is cleaved and optically polished at zero degrees. The length of the rod is one half of the Talbot distance for optimal coherent beam combining. In the experiment, an antiphase supermode is observed when only the seven inner channels are pumped, and an in-phase supermode is excited when the number of channels is nineteen or larger.
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Classifier combination has become a very important topic, because it is possible to train many classifiers using different feature, instance subsets or different types of classifiers. Classifier diversity and accuracy are two comp...
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Classifier combination has become a very important topic, because it is possible to train many classifiers using different feature, instance subsets or different types of classifiers. Classifier diversity and accuracy are two competing requirements for classifier combination. In this paper, we study classifier combination using the kernelized eigenclassifiers The eigenclassifier method, tries to handle the linear correlations among classifier outputs by applying PCA to uncorrelate them before fusing with a second classifier, is introduced by Ulas et al. (2012). Our contribution is to adapt the kernel PCA in this method to handle non-linear correlations among classifier outputs and we compared the eigen and kernelized eigenclassifiers to SVM based stacking algorithms both for linear and rbf kernels and simple average method to see the performance of these methods. Our experiments on the 38 datasets used by (Ulas et al. 2009) show that the kernelized eigenclassifiers method performs better than the other methods in terms of test accuracy.
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摘要 :
Classifier combination has become a very important topic, because it is possible to train many classifiers using different feature, instance subsets or different types of classifiers. Classifier diversity and accuracy are two comp...
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Classifier combination has become a very important topic, because it is possible to train many classifiers using different feature, instance subsets or different types of classifiers. Classifier diversity and accuracy are two competing requirements for classifier combination. In this paper, we study classifier combination using the kernelized eigenclassifiers The eigenclassifier method, tries to handle the linear correlations among classifier outputs by applying PCA to uncorrelate them before fusing with a second classifier, is introduced by Ulas et al. (2012). Our contribution is to adapt the kernel PCA in this method to handle non-linear correlations among classifier outputs and we compared the eigen and kernelized eigenclassifiers to SVM based stacking algorithms both for linear and rbf kernels and simple average method to see the performance of these methods. Our experiments on the 38 datasets used by (Ulas et al. 2009) show that the kernelized eigenclassifiers method performs better than the other methods in terms of test accuracy.
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摘要 :
Classifier combination has become a very important topic, because it is possible to train many classifiers using different feature, instance subsets or different types of classifiers. Classifier diversity and accuracy are two comp...
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Classifier combination has become a very important topic, because it is possible to train many classifiers using different feature, instance subsets or different types of classifiers. Classifier diversity and accuracy are two competing requirements for classifier combination. In this paper, we study classifier combination using the kernelized eigenclassifiers The eigenclassifier method, tries to handle the linear correlations among classifier outputs by applying PCA to uncorrelate them before fusing with a second classifier, is introduced by Ulas et al. (2012). Our contribution is to adapt the kernel PCA in this method to handle non-linear correlations among classifier outputs and we compared the eigen and kernelized eigenclassifiers to SVM based stacking algorithms both for linear and rbf kernels and simple average method to see the performance of these methods. Our experiments on the 38 datasets used by (Ulas et al. 2009) show that the kernelized eigenclassifiers method performs better than the other methods in terms of test accuracy.
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In this paper, we study the transmission of data destined for several users on the radio interface using the multicast mode, an interesting alternative of the conventional unicast mode. In the multicast mode, a packet is sent simu...
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In this paper, we study the transmission of data destined for several users on the radio interface using the multicast mode, an interesting alternative of the conventional unicast mode. In the multicast mode, a packet is sent simultaneously to several terminals in the same cell. We consider different techniques of macro diversity, namely Selective Combining (SC) and Maximal Ratio Combining (MRC). We develop an analytical model that allows the computation of the mean bitrate for both multicast and unicast schemes. We use a scheduler that allocates bandwidth to mobiles according to their instantaneous channel quality. In this context, we propose an efficient user clustering considering their average radio channel quality. The study shows that macro diversity improves the transmission performance especially for pure multicast.
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摘要 :
In this paper, we study the transmission of data destined for several users on the radio interface using the multicast mode, an interesting alternative of the conventional unicast mode. In the multicast mode, a packet is sent simu...
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In this paper, we study the transmission of data destined for several users on the radio interface using the multicast mode, an interesting alternative of the conventional unicast mode. In the multicast mode, a packet is sent simultaneously to several terminals in the same cell. We consider different techniques of macro diversity, namely Selective Combining (SC) and Maximal Ratio Combining (MRC). We develop an analytical model that allows the computation of the mean bitrate for both multicast and unicast schemes. We use a scheduler that allocates bandwidth to mobiles according to their instantaneous channel quality. In this context, we propose an efficient user clustering considering their average radio channel quality. The study shows that macro diversity improves the transmission performance especially for pure multicast.
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