The Experts below are selected from a list of 134193 Experts worldwide ranked by ideXlab platform
Eytan Modiano - One of the best experts on this subject based on the ideXlab platform.
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The Impact of Queue Length Information on Buffer Overflow in Parallel Queues
IEEE Transactions on Information Theory, 2013Co-Authors: Krishna Jagannathan, Eytan ModianoAbstract:We consider a system consisting of N parallel queues, served by one server. Time is slotted, and the server serves one of the queues in each time slot, according to some scheduling policy. We first characterize the exponent of the buffer overflow probability and the most likely overflow trajectories under the Longest Queue First (LQF) scheduling policy. Under statistically identical arrivals to each queue, we show that the buffer overflow Exponents can be simply expressed in terms of the total system occupancy exponent of m parallel queues, for some m ≤ N. We next turn our attention to the rate of queue length information needed to operate a scheduling policy, and its relationship to the buffer overflow Exponents. It is known that queue length blind policies such as processor sharing and random scheduling perform worse than the queue aware LQF policy, when it comes to buffer overflow probability. However, we show that the overflow exponent of the LQF policy can be preserved with arbitrarily infrequent queue length updates.
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The impact of queue length information on buffer overflow in parallel queues
2009 47th Annual Allerton Conference on Communication Control and Computing (Allerton), 2009Co-Authors: Krishna Jagannathan, Eytan ModianoAbstract:We consider a system consisting of N parallel queues, served by one server. Time is slotted, and the server serves one of the queues in each time slot, according to some scheduling policy. In the first part of the paper, we characterize the buffer overflow Exponents and the likeliest overflow trajectories under the Longest Queue First (LQF) scheduling policy. Under statistically identical arrivals to each queue, we show that the buffer overflow exponent can be simply expressed in terms of the total system occupancy exponent of m parallel queues, for some m ¿ N. We next turn our attention to the rate of queue length information needed to operate a scheduling policy, and its relationship to the buffer overflow Exponents. It is known that LQF scheduling has superior overflow Exponents compared to queue blind policies such as processor sharing (PS) and random scheduling. However, we show that the overflow exponent of the LQF policy can be preserved under arbitrarily infrequent queue length information.
Krishna Jagannathan - One of the best experts on this subject based on the ideXlab platform.
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The Impact of Queue Length Information on Buffer Overflow in Parallel Queues
IEEE Transactions on Information Theory, 2013Co-Authors: Krishna Jagannathan, Eytan ModianoAbstract:We consider a system consisting of N parallel queues, served by one server. Time is slotted, and the server serves one of the queues in each time slot, according to some scheduling policy. We first characterize the exponent of the buffer overflow probability and the most likely overflow trajectories under the Longest Queue First (LQF) scheduling policy. Under statistically identical arrivals to each queue, we show that the buffer overflow Exponents can be simply expressed in terms of the total system occupancy exponent of m parallel queues, for some m ≤ N. We next turn our attention to the rate of queue length information needed to operate a scheduling policy, and its relationship to the buffer overflow Exponents. It is known that queue length blind policies such as processor sharing and random scheduling perform worse than the queue aware LQF policy, when it comes to buffer overflow probability. However, we show that the overflow exponent of the LQF policy can be preserved with arbitrarily infrequent queue length updates.
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The impact of queue length information on buffer overflow in parallel queues
2009 47th Annual Allerton Conference on Communication Control and Computing (Allerton), 2009Co-Authors: Krishna Jagannathan, Eytan ModianoAbstract:We consider a system consisting of N parallel queues, served by one server. Time is slotted, and the server serves one of the queues in each time slot, according to some scheduling policy. In the first part of the paper, we characterize the buffer overflow Exponents and the likeliest overflow trajectories under the Longest Queue First (LQF) scheduling policy. Under statistically identical arrivals to each queue, we show that the buffer overflow exponent can be simply expressed in terms of the total system occupancy exponent of m parallel queues, for some m ¿ N. We next turn our attention to the rate of queue length information needed to operate a scheduling policy, and its relationship to the buffer overflow Exponents. It is known that LQF scheduling has superior overflow Exponents compared to queue blind policies such as processor sharing (PS) and random scheduling. However, we show that the overflow exponent of the LQF policy can be preserved under arbitrarily infrequent queue length information.
Tomoaki Ohtsuki - One of the best experts on this subject based on the ideXlab platform.
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RSS-Based Localization in Environments with Different Path Loss Exponent for Each Link
VTC Spring 2008 - IEEE Vehicular Technology Conference, 2008Co-Authors: Junichi Shirahama, Tomoaki OhtsukiAbstract:The path loss exponent is very important parameter for localization using receive signal strength (RSS). In actual environments, path loss exponent for each link (target to each receive node) differs. However, the conventional localization methods use the same path loss exponent for all links. Hence, there are some mismatches between the real path loss exponent and the one used to estimate. We proposed the localization method that considers all the combinations of path loss Exponents for each link and estimates the target location by averaging the target locations derived with all the combinations. However, the amount of calculation is huge. In this paper we propose RSS-based localization in environments with different path loss exponent for each link. The proposed method is a grid-based centralized localization using RSS. First the proposed method sets the minimum distance di,min and maximum distance di,max for each node i by using the RSS of each receive node i and the minimum and maximum path loss Exponents set before estimation. Next, it calculates the distance di,(k,l) between the candidate target position (k, I) and each receive node i. If di,min les di,(k,l) les di,max, vote the grid (k,l). These processes are performed for all the receive nodes over the search area. Finally, the grid point with most voting is estimated to be the target location. According to the simulation results, we show that the proposed method achieves the higher localization accuracy than the conventional localization method using the same path loss exponent for all the links when the distribution of the path loss Exponents over the field is uniform distribution.
Kenneth A Loparo - One of the best experts on this subject based on the ideXlab platform.
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on the relationship between the sample path and moment lyapunov Exponents for jump linear systems
IEEE Transactions on Automatic Control, 2002Co-Authors: Yuguang Fang, Kenneth A LoparoAbstract:In this paper, we study the relationship between the sample and moment Lyapunov Exponents for jump linear systems. Using a large deviation theorem, a modified version of Arnold's formula for connecting sample path and moment Lyapunov Exponents for continuous-time linear stochastic systems is extended to discrete-time jump linear systems. Sample path stability properties of linear stochastic systems are determined by the top Lyapunov exponent and relating sample and moment Lyapunov Exponents may be useful for developing computationally efficient methods for determining the almost-sure (sample path) stability of linear stochastic systems.
Junichi Shirahama - One of the best experts on this subject based on the ideXlab platform.
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RSS-Based Localization in Environments with Different Path Loss Exponent for Each Link
VTC Spring 2008 - IEEE Vehicular Technology Conference, 2008Co-Authors: Junichi Shirahama, Tomoaki OhtsukiAbstract:The path loss exponent is very important parameter for localization using receive signal strength (RSS). In actual environments, path loss exponent for each link (target to each receive node) differs. However, the conventional localization methods use the same path loss exponent for all links. Hence, there are some mismatches between the real path loss exponent and the one used to estimate. We proposed the localization method that considers all the combinations of path loss Exponents for each link and estimates the target location by averaging the target locations derived with all the combinations. However, the amount of calculation is huge. In this paper we propose RSS-based localization in environments with different path loss exponent for each link. The proposed method is a grid-based centralized localization using RSS. First the proposed method sets the minimum distance di,min and maximum distance di,max for each node i by using the RSS of each receive node i and the minimum and maximum path loss Exponents set before estimation. Next, it calculates the distance di,(k,l) between the candidate target position (k, I) and each receive node i. If di,min les di,(k,l) les di,max, vote the grid (k,l). These processes are performed for all the receive nodes over the search area. Finally, the grid point with most voting is estimated to be the target location. According to the simulation results, we show that the proposed method achieves the higher localization accuracy than the conventional localization method using the same path loss exponent for all the links when the distribution of the path loss Exponents over the field is uniform distribution.
