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Steven Weber - One of the best experts on this subject based on the ideXlab platform.
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interactive scalar quantization for distributed resource allocation
IEEE Transactions on Signal Processing, 2016Co-Authors: Bradford D Boyle, Jie Ren, John Maclaren Walsh, Steven WeberAbstract:In many resource allocation problems, a centralized controller needs to award some resource to a user selected from a collection of distributed users with the goal of maximizing the utility the user would receive from the resource. This can be modeled as the controller computing an extremum of the distributed users’ utilities. The Overhead Rate necessary to enable the controller to reproduce the users’ local state can be prohibitively high. An approach to reduce this Overhead is interactive communication wherein Rate savings are achieved by tolerating an increase in delay. In this paper, we consider the design of a simple achievable scheme based on successive refinements of scalar quantization at each user. The optimal quantization policy is computed via a dynamic program and we demonstRate that tolerating a small increase in delay can yield significant Rate savings. We then consider two simpler quantization policies to investigate the scaling properties of the Rate-delay tradeoffs. Using a combination of these simpler policies, the performance of the optimal policy can be closely approximated with lower computational costs.
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interactive scalar quantization for distributed extremization
arXiv: Information Theory, 2015Co-Authors: Bradford D Boyle, Jie Ren, John Maclaren Walsh, Steven WeberAbstract:In many resource allocation problems, a centralized controller needs to award some resource to a user selected from a collection of distributed users with the goal of maximizing the utility the user would receive from the resource. This can be modeled as the controller computing an extremization of the distributed users’ utilities. The Overhead Rate necessary to enable the controller to reproduce the users’ local state can be prohibitively high. Two approaches to reduce this Overhead are lossy estimation and interactive communication. In the lossy estimator framework, Rate savings are achieved by tolerating a bounded expected reduction in utility. In interactive communication, Rate savings come at the expense of delay. In this paper, we consider the design of a simple achievable scheme based on successive refinements of scalar quantization at each user. The optimal quantization policy is computed via a dynamic program and we demonstRate that tolerating a small increase in delay can yield significant Rate savings. We then consider two simpler quantization policies to investigate the scaling properties of the Rate-delay trade-os. Using a combination of these simpler policies, the performance of the optimal policy can be closely approximated with lower computational costs.
Bradford D Boyle - One of the best experts on this subject based on the ideXlab platform.
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interactive scalar quantization for distributed resource allocation
IEEE Transactions on Signal Processing, 2016Co-Authors: Bradford D Boyle, Jie Ren, John Maclaren Walsh, Steven WeberAbstract:In many resource allocation problems, a centralized controller needs to award some resource to a user selected from a collection of distributed users with the goal of maximizing the utility the user would receive from the resource. This can be modeled as the controller computing an extremum of the distributed users’ utilities. The Overhead Rate necessary to enable the controller to reproduce the users’ local state can be prohibitively high. An approach to reduce this Overhead is interactive communication wherein Rate savings are achieved by tolerating an increase in delay. In this paper, we consider the design of a simple achievable scheme based on successive refinements of scalar quantization at each user. The optimal quantization policy is computed via a dynamic program and we demonstRate that tolerating a small increase in delay can yield significant Rate savings. We then consider two simpler quantization policies to investigate the scaling properties of the Rate-delay tradeoffs. Using a combination of these simpler policies, the performance of the optimal policy can be closely approximated with lower computational costs.
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interactive scalar quantization for distributed extremization
arXiv: Information Theory, 2015Co-Authors: Bradford D Boyle, Jie Ren, John Maclaren Walsh, Steven WeberAbstract:In many resource allocation problems, a centralized controller needs to award some resource to a user selected from a collection of distributed users with the goal of maximizing the utility the user would receive from the resource. This can be modeled as the controller computing an extremization of the distributed users’ utilities. The Overhead Rate necessary to enable the controller to reproduce the users’ local state can be prohibitively high. Two approaches to reduce this Overhead are lossy estimation and interactive communication. In the lossy estimator framework, Rate savings are achieved by tolerating a bounded expected reduction in utility. In interactive communication, Rate savings come at the expense of delay. In this paper, we consider the design of a simple achievable scheme based on successive refinements of scalar quantization at each user. The optimal quantization policy is computed via a dynamic program and we demonstRate that tolerating a small increase in delay can yield significant Rate savings. We then consider two simpler quantization policies to investigate the scaling properties of the Rate-delay trade-os. Using a combination of these simpler policies, the performance of the optimal policy can be closely approximated with lower computational costs.
John Maclaren Walsh - One of the best experts on this subject based on the ideXlab platform.
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interactive scalar quantization for distributed resource allocation
IEEE Transactions on Signal Processing, 2016Co-Authors: Bradford D Boyle, Jie Ren, John Maclaren Walsh, Steven WeberAbstract:In many resource allocation problems, a centralized controller needs to award some resource to a user selected from a collection of distributed users with the goal of maximizing the utility the user would receive from the resource. This can be modeled as the controller computing an extremum of the distributed users’ utilities. The Overhead Rate necessary to enable the controller to reproduce the users’ local state can be prohibitively high. An approach to reduce this Overhead is interactive communication wherein Rate savings are achieved by tolerating an increase in delay. In this paper, we consider the design of a simple achievable scheme based on successive refinements of scalar quantization at each user. The optimal quantization policy is computed via a dynamic program and we demonstRate that tolerating a small increase in delay can yield significant Rate savings. We then consider two simpler quantization policies to investigate the scaling properties of the Rate-delay tradeoffs. Using a combination of these simpler policies, the performance of the optimal policy can be closely approximated with lower computational costs.
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interactive scalar quantization for distributed extremization
arXiv: Information Theory, 2015Co-Authors: Bradford D Boyle, Jie Ren, John Maclaren Walsh, Steven WeberAbstract:In many resource allocation problems, a centralized controller needs to award some resource to a user selected from a collection of distributed users with the goal of maximizing the utility the user would receive from the resource. This can be modeled as the controller computing an extremization of the distributed users’ utilities. The Overhead Rate necessary to enable the controller to reproduce the users’ local state can be prohibitively high. Two approaches to reduce this Overhead are lossy estimation and interactive communication. In the lossy estimator framework, Rate savings are achieved by tolerating a bounded expected reduction in utility. In interactive communication, Rate savings come at the expense of delay. In this paper, we consider the design of a simple achievable scheme based on successive refinements of scalar quantization at each user. The optimal quantization policy is computed via a dynamic program and we demonstRate that tolerating a small increase in delay can yield significant Rate savings. We then consider two simpler quantization policies to investigate the scaling properties of the Rate-delay trade-os. Using a combination of these simpler policies, the performance of the optimal policy can be closely approximated with lower computational costs.
Jie Ren - One of the best experts on this subject based on the ideXlab platform.
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interactive scalar quantization for distributed resource allocation
IEEE Transactions on Signal Processing, 2016Co-Authors: Bradford D Boyle, Jie Ren, John Maclaren Walsh, Steven WeberAbstract:In many resource allocation problems, a centralized controller needs to award some resource to a user selected from a collection of distributed users with the goal of maximizing the utility the user would receive from the resource. This can be modeled as the controller computing an extremum of the distributed users’ utilities. The Overhead Rate necessary to enable the controller to reproduce the users’ local state can be prohibitively high. An approach to reduce this Overhead is interactive communication wherein Rate savings are achieved by tolerating an increase in delay. In this paper, we consider the design of a simple achievable scheme based on successive refinements of scalar quantization at each user. The optimal quantization policy is computed via a dynamic program and we demonstRate that tolerating a small increase in delay can yield significant Rate savings. We then consider two simpler quantization policies to investigate the scaling properties of the Rate-delay tradeoffs. Using a combination of these simpler policies, the performance of the optimal policy can be closely approximated with lower computational costs.
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interactive scalar quantization for distributed extremization
arXiv: Information Theory, 2015Co-Authors: Bradford D Boyle, Jie Ren, John Maclaren Walsh, Steven WeberAbstract:In many resource allocation problems, a centralized controller needs to award some resource to a user selected from a collection of distributed users with the goal of maximizing the utility the user would receive from the resource. This can be modeled as the controller computing an extremization of the distributed users’ utilities. The Overhead Rate necessary to enable the controller to reproduce the users’ local state can be prohibitively high. Two approaches to reduce this Overhead are lossy estimation and interactive communication. In the lossy estimator framework, Rate savings are achieved by tolerating a bounded expected reduction in utility. In interactive communication, Rate savings come at the expense of delay. In this paper, we consider the design of a simple achievable scheme based on successive refinements of scalar quantization at each user. The optimal quantization policy is computed via a dynamic program and we demonstRate that tolerating a small increase in delay can yield significant Rate savings. We then consider two simpler quantization policies to investigate the scaling properties of the Rate-delay trade-os. Using a combination of these simpler policies, the performance of the optimal policy can be closely approximated with lower computational costs.
Michael A. Krainak - One of the best experts on this subject based on the ideXlab platform.
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simultaneous laser ranging and communication from an earth based satellite laser ranging station to the lunar reconnaissance orbiter in lunar orbit
Proceedings of SPIE, 2013Co-Authors: Donald R. Skillman, Ronald S. Zellar, Jan F. Mcgarry, Evan D Hoffman, Wai H Fong, Leva Mcintire, Frederic M Davidson, Michael A. KrainakAbstract:We report a free space laser communication experiment from the satellite laser ranging (SLR) station at NASA Goddard Space Flight Center (GSFC) to the Lunar Reconnaissance Orbiter (LRO) in lunar orbit through the on board one-way Laser Ranging (LR) receiver. Pseudo random data and sample image files were transmitted to LRO using a 4096-ary pulse position modulation (PPM) signal format. Reed-Solomon forward error correction codes were used to achieve error free data transmission at a modeRate coding Overhead Rate. The signal fading due to the atmosphere effect was measured and the coding gain could be estimated.
