The Experts below are selected from a list of 6738 Experts worldwide ranked by ideXlab platform
Jeffrey G Andrews - One of the best experts on this subject based on the ideXlab platform.
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an upper bound on multihop transmission capacity with dynamic routing selection
IEEE Transactions on Information Theory, 2012Co-Authors: Yuxin Chen, Jeffrey G AndrewsAbstract:This paper develops upper bounds on the end-to-end transmission capacity of multihop wireless networks. Potential source-destination paths are dynamically selected from a pool of randomly located relays, from which a closed-form lower bound on the outage probability is derived in terms of the expected number of potential paths. This is in turn used to provide an upper bound on the number of successful transmissions that can occur per unit area, which is known as the transmission capacity. The upper bound results from Assuming Independence among the potential paths, and can be viewed as the maximum diversity case. A useful aspect of the upper bound is its simple form for an arbitrary-sized network, which allows insights into how the number of hops and other network parameters affect spatial throughput in the nonasymptotic regime. The outage probability analysis is then extended to account for retransmissions with a maximum number of allowed attempts. In contrast to prevailing wisdom, we show that predetermined routing (such as nearest neighbor) is suboptimal, since more hops are not useful once the network is interference-limited. Our results also make clear that randomness in the location of relay sets and dynamically varying channel states is helpful in obtaining higher aggregate throughput, and that dynamic route selection should be used to exploit path diversity.
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an upper bound on multi hop transmission capacity with dynamic routing selection
International Symposium on Information Theory, 2010Co-Authors: Yuxin Chen, Jeffrey G AndrewsAbstract:This paper develops an upper bound on the end-to-end transmission capacity of multi-hop wireless networks, in which all nodes are randomly distributed. Potential source-destination paths are dynamically selected from a pool of randomly located relays, from which a closed-form bound on the outage probability is derived in terms of the number of potential paths. This in turn gives an upper bound on the number of successful transmissions that can occur per unit area, which is known as the transmission capacity. The upper bound results from Assuming Independence among the potential paths, and can be viewed as the maximum diversity case. A useful aspect of the upper bound is its simple form for an arbitrary-sized network, which allows us to immediately observe how the number of hops and other network traits affect spatial throughput. Our analysis indicates that predetermined routing approach (such as nearest-neighbor) cannot achieve optimal throughput: more hops are not necessarily helpful in interference-limited networks compared with single-hop direct transmission.
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an upper bound on multi hop transmission capacity with dynamic routing selection
arXiv: Information Theory, 2010Co-Authors: Yuxin Chen, Jeffrey G AndrewsAbstract:This paper develops upper bounds on the end-to-end transmission capacity of multi-hop wireless networks. Potential source-destination paths are dynamically selected from a pool of randomly located relays, from which a closed-form lower bound on the outage probability is derived in terms of the expected number of potential paths. This is in turn used to provide an upper bound on the number of successful transmissions that can occur per unit area, which is known as the transmission capacity. The upper bound results from Assuming Independence among the potential paths, and can be viewed as the maximum diversity case. A useful aspect of the upper bound is its simple form for an arbitrary-sized network, which allows insights into how the number of hops and other network parameters affect spatial throughput in the non-asymptotic regime. The outage probability analysis is then extended to account for retransmissions with a maximum number of allowed attempts. In contrast to prevailing wisdom, we show that predetermined routing (such as nearest-neighbor) is suboptimal, since more hops are not useful once the network is interference-limited. Our results also make clear that randomness in the location of relay sets and dynamically varying channel states is helpful in obtaining higher aggregate throughput, and that dynamic route selection should be used to exploit path diversity.
Luc Duchateau - One of the best experts on this subject based on the ideXlab platform.
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Duchateau L. A Bayesian approach to jointly estimate centre and treatment by centre heterogeneity in a proportional hazards model. Stat Med 2005
2020Co-Authors: Catherine Legrand, Vincent Ducrocq, Paul Janssen, Richard Sylvester, Luc DuchateauAbstract:SUMMARY When multicentre clinical trial data are analysed, it has become more and more popular to look for possible heterogeneity in outcome between centres. However, beyond the investigation of such heterogeneity, it is also interesting to consider heterogeneity in treatment e ect over centres. For time-to-event outcomes, this may be investigated by including a random centre e ect and a random treatment by centre interaction in a Cox proportional hazards model. Assuming Independence between the random e ects, we propose a Bayesian approach to ÿt our proposed model. The parameters of interest are the variance components 2 0 and 2 1 of these random e ects, which can be interpreted as a measure of centre and treatment e ect over centres heterogeneity of the hazard. These variance components are estimated from their marginal posterior density after integrating out the ÿxed treatment e ect and the random e ects. As this integration cannot be performed analytically, the marginal posterior density is approximated using the Laplace integration technique. Statistical inference is then based on the characteristics of the posterior marginal density, such as the mode and the standard deviation. We demonstrate the proposed technique using data from a pooled database of seven EORTC bladder cancer clinical trials. Substantial centre and treatment e ect over centres heterogeneity in disease-free interval was found
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a bayesian approach to jointly estimate centre and treatment by centre heterogeneity in a proportional hazards model
Statistics in Medicine, 2005Co-Authors: Catherine Legrand, Vincent Ducrocq, Paul Janssen, Richard Sylvester, Luc DuchateauAbstract:When multicentre clinical trial data are analysed, it has become more and more popular to look for possible heterogeneity in outcome between centres. However, beyond the investigation of such heterogeneity, it is also interesting to consider heterogeneity in treatment effect over centres. For time-to-event outcomes, this may be investigated by including a random centre effect and a random treatment by centre interaction in a Cox proportional hazards model. Assuming Independence between the random effects, we propose a Bayesian approach to fit our proposed model. The parameters of interest are the variance components sigma(0)(2) and a; of these random effects, which can be interpreted as a measure of centre and treatment effect over centres heterogeneity of the hazard. These variance components are estimated from their marginal posterior density after integrating out the fixed treatment effect and the random effects. As this integration cannot be performed analytically, the marginal posterior density is approximated using the Laplace integration technique. Statistical inference is then based on the characteristics of the posterior marginal density, such as the mode and the standard deviation. We demonstrate the proposed technique using data from a pooled database of seven EORTC bladder cancer clinical trials. Substantial centre and treatment effect over centres heterogeneity in disease-free interval was found. Copyright (c) 2005 John Wiley & Sons, Ltd.
Yuxin Chen - One of the best experts on this subject based on the ideXlab platform.
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an upper bound on multihop transmission capacity with dynamic routing selection
IEEE Transactions on Information Theory, 2012Co-Authors: Yuxin Chen, Jeffrey G AndrewsAbstract:This paper develops upper bounds on the end-to-end transmission capacity of multihop wireless networks. Potential source-destination paths are dynamically selected from a pool of randomly located relays, from which a closed-form lower bound on the outage probability is derived in terms of the expected number of potential paths. This is in turn used to provide an upper bound on the number of successful transmissions that can occur per unit area, which is known as the transmission capacity. The upper bound results from Assuming Independence among the potential paths, and can be viewed as the maximum diversity case. A useful aspect of the upper bound is its simple form for an arbitrary-sized network, which allows insights into how the number of hops and other network parameters affect spatial throughput in the nonasymptotic regime. The outage probability analysis is then extended to account for retransmissions with a maximum number of allowed attempts. In contrast to prevailing wisdom, we show that predetermined routing (such as nearest neighbor) is suboptimal, since more hops are not useful once the network is interference-limited. Our results also make clear that randomness in the location of relay sets and dynamically varying channel states is helpful in obtaining higher aggregate throughput, and that dynamic route selection should be used to exploit path diversity.
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an upper bound on multi hop transmission capacity with dynamic routing selection
International Symposium on Information Theory, 2010Co-Authors: Yuxin Chen, Jeffrey G AndrewsAbstract:This paper develops an upper bound on the end-to-end transmission capacity of multi-hop wireless networks, in which all nodes are randomly distributed. Potential source-destination paths are dynamically selected from a pool of randomly located relays, from which a closed-form bound on the outage probability is derived in terms of the number of potential paths. This in turn gives an upper bound on the number of successful transmissions that can occur per unit area, which is known as the transmission capacity. The upper bound results from Assuming Independence among the potential paths, and can be viewed as the maximum diversity case. A useful aspect of the upper bound is its simple form for an arbitrary-sized network, which allows us to immediately observe how the number of hops and other network traits affect spatial throughput. Our analysis indicates that predetermined routing approach (such as nearest-neighbor) cannot achieve optimal throughput: more hops are not necessarily helpful in interference-limited networks compared with single-hop direct transmission.
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an upper bound on multi hop transmission capacity with dynamic routing selection
arXiv: Information Theory, 2010Co-Authors: Yuxin Chen, Jeffrey G AndrewsAbstract:This paper develops upper bounds on the end-to-end transmission capacity of multi-hop wireless networks. Potential source-destination paths are dynamically selected from a pool of randomly located relays, from which a closed-form lower bound on the outage probability is derived in terms of the expected number of potential paths. This is in turn used to provide an upper bound on the number of successful transmissions that can occur per unit area, which is known as the transmission capacity. The upper bound results from Assuming Independence among the potential paths, and can be viewed as the maximum diversity case. A useful aspect of the upper bound is its simple form for an arbitrary-sized network, which allows insights into how the number of hops and other network parameters affect spatial throughput in the non-asymptotic regime. The outage probability analysis is then extended to account for retransmissions with a maximum number of allowed attempts. In contrast to prevailing wisdom, we show that predetermined routing (such as nearest-neighbor) is suboptimal, since more hops are not useful once the network is interference-limited. Our results also make clear that randomness in the location of relay sets and dynamically varying channel states is helpful in obtaining higher aggregate throughput, and that dynamic route selection should be used to exploit path diversity.
Balint Virag - One of the best experts on this subject based on the ideXlab platform.
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dyson s spike for random schroedinger operators and novikov shubin invariants of groups
Communications in Mathematical Physics, 2017Co-Authors: Marcin Kotowski, Balint ViragAbstract:We study one dimensional Schroedinger operators with random edge weights and their expected spectral measures \({\mu_H}\) near zero. We prove that the measure exhibits a spike of the form \({\mu_H(-\varepsilon,\varepsilon) \sim \frac{C}{\mid{{\rm log}\varepsilon}\mid^2}}\) (first observed by Dyson), without Assuming Independence or any regularity of edge weights. We also identify the limiting local eigenvalue distribution, which is different from Poisson and the usual random matrix statistics. We then use the result to compute Novikov–Shubin invariants for various groups, including lamplighter groups and lattices in the Lie group Sol.
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dyson s spike for random schroedinger operators and novikov shubin invariants of groups
arXiv: Probability, 2016Co-Authors: Marcin Kotowski, Balint ViragAbstract:We study Schroedinger operators with random edge weights and their expected spectral measures $\mu_H$ near zero. We prove that the measure exhibits a spike of the form $\mu_H(-\varepsilon,\varepsilon) \sim \frac{C}{| \log\varepsilon|^2}$ (first observed by Dyson), without Assuming Independence or any regularity of edge weights. We also identify the limiting local eigenvalue distribution, which is different from Poisson and the usual random matrix statistics. We then use the result to compute Novikov-Shubin invariants for various groups, including lamplighter groups and lattices in the Lie group Sol.
Catherine Legrand - One of the best experts on this subject based on the ideXlab platform.
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Duchateau L. A Bayesian approach to jointly estimate centre and treatment by centre heterogeneity in a proportional hazards model. Stat Med 2005
2020Co-Authors: Catherine Legrand, Vincent Ducrocq, Paul Janssen, Richard Sylvester, Luc DuchateauAbstract:SUMMARY When multicentre clinical trial data are analysed, it has become more and more popular to look for possible heterogeneity in outcome between centres. However, beyond the investigation of such heterogeneity, it is also interesting to consider heterogeneity in treatment e ect over centres. For time-to-event outcomes, this may be investigated by including a random centre e ect and a random treatment by centre interaction in a Cox proportional hazards model. Assuming Independence between the random e ects, we propose a Bayesian approach to ÿt our proposed model. The parameters of interest are the variance components 2 0 and 2 1 of these random e ects, which can be interpreted as a measure of centre and treatment e ect over centres heterogeneity of the hazard. These variance components are estimated from their marginal posterior density after integrating out the ÿxed treatment e ect and the random e ects. As this integration cannot be performed analytically, the marginal posterior density is approximated using the Laplace integration technique. Statistical inference is then based on the characteristics of the posterior marginal density, such as the mode and the standard deviation. We demonstrate the proposed technique using data from a pooled database of seven EORTC bladder cancer clinical trials. Substantial centre and treatment e ect over centres heterogeneity in disease-free interval was found
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a bayesian approach to jointly estimate centre and treatment by centre heterogeneity in a proportional hazards model
Statistics in Medicine, 2005Co-Authors: Catherine Legrand, Vincent Ducrocq, Paul Janssen, Richard Sylvester, Luc DuchateauAbstract:When multicentre clinical trial data are analysed, it has become more and more popular to look for possible heterogeneity in outcome between centres. However, beyond the investigation of such heterogeneity, it is also interesting to consider heterogeneity in treatment effect over centres. For time-to-event outcomes, this may be investigated by including a random centre effect and a random treatment by centre interaction in a Cox proportional hazards model. Assuming Independence between the random effects, we propose a Bayesian approach to fit our proposed model. The parameters of interest are the variance components sigma(0)(2) and a; of these random effects, which can be interpreted as a measure of centre and treatment effect over centres heterogeneity of the hazard. These variance components are estimated from their marginal posterior density after integrating out the fixed treatment effect and the random effects. As this integration cannot be performed analytically, the marginal posterior density is approximated using the Laplace integration technique. Statistical inference is then based on the characteristics of the posterior marginal density, such as the mode and the standard deviation. We demonstrate the proposed technique using data from a pooled database of seven EORTC bladder cancer clinical trials. Substantial centre and treatment effect over centres heterogeneity in disease-free interval was found. Copyright (c) 2005 John Wiley & Sons, Ltd.
