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Martin Haenggi - One of the best experts on this subject based on the ideXlab platform.
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LIMIT OF THE Transport Capacity OF A DENSE WIRELESS NETWORK
Journal of Applied Probability, 2010Co-Authors: Radha Krishna Ganti, Martin HaenggiAbstract:It is known that the Transport Capacity of a dense wireless ad hoc network with n nodes scales like √ n. We show that the Transport Capacity divided by √ n approaches a nonrandom limit with probability 1 when the nodes are uniformly distributed on the unit square. To show the existence of the limit, we prove that the Transport Capacity under the protocol model is a subadditive Euclidean functional and use the machinery of subadditive functions in the spirit of Steele.
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random access Transport Capacity
arXiv: Information Theory, 2009Co-Authors: Jeffrey G. Andrews, Steven Weber, Marios Kountouris, Martin HaenggiAbstract:We develop a new metric for quantifying end-to-end throughput in multihop wireless networks, which we term random access Transport Capacity, since the interference model presumes uncoordinated transmissions. The metric quantifies the average maximum rate of successful end-to-end transmissions, multiplied by the communication distance, and normalized by the network area. We show that a simple upper bound on this quantity is computable in closed-form in terms of key network parameters when the number of retransmissions is not restricted and the hops are assumed to be equally spaced on a line between the source and destination. We also derive the optimum number of hops and optimal per hop success probability and show that our result follows the well-known square root scaling law while providing exact expressions for the preconstants as well. Numerical results demonstrate that the upper bound is accurate for the purpose of determining the optimal hop count and success (or outage) probability.
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A simple upper bound on random access Transport Capacity
2009 47th Annual Allerton Conference on Communication Control and Computing (Allerton), 2009Co-Authors: Jeffrey G. Andrews, Steven Weber, Marios Kountouris, Martin HaenggiAbstract:We attempt to quantify end-to-end throughput in multihop wireless networks using a metric that measures the maximum density of source-destination pairs that can successfully communicate over a specified distance at certain data rate. We term this metric the random access Transport Capacity, since it is similar to Transport Capacity but the interference model presumes uncoordinated transmissions. A simple upper bound on this quantity is derived in closed-form in terms of key network parameters when the number of retransmissions is not restricted and the hops are assumed to be equally spaced on a line between the source and destination. We also derive the optimum number of hops — which is small and finite — and optimal per hop success probability for integer path loss exponents. We show that our result follows the well-known square root scaling law while providing exact expressions for the preconstants as well.
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LIMIT OF THE Transport Capacity OF A DENSE WIRE
2008Co-Authors: Radha Krishna Ganti, Martin HaenggiAbstract:It is known that the Transport Capacity of a dense wireless ad hoc network with n nodes scales like p n. We show that the Transport Capacity divided by p n approaches a non-random limit with probability one when the nodes are i.i.d uniformly distributed on the unit square. To show the existence of the limit we prove that the Transport Capacity under the protocol model is a subadditive Euclidean functional and use the machinery of subadditive functions in the
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MASS - The Transport Capacity of a wireless network is a subadditive euclidean functional
2008 5th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, 2008Co-Authors: Radha Krishna Ganti, Martin HaenggiAbstract:The Transport Capacity of a dense ad hoc network with n nodes scales like radicn. We show that the Transport Capacity divided by radicn approaches a non-random limit with probability one when the nodes are i.i.d. distributed on the unit square. We prove that the Transport Capacity under the protocol model is a subadditive Euclidean functional and use the machinery of subadditive functions in the spirit of Steele to show the existence of the limit.
Guang-hui Zhang - One of the best experts on this subject based on the ideXlab platform.
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Effect of stem basal cover on the sediment Transport Capacity of overland flows
Geoderma, 2019Co-Authors: Y.r. Liu, Guang-hui ZhangAbstract:Abstract Vegetation cover can effectively prevent soil erosion and plays an important role in soil and water conservation. Accurate estimation of the sediment Transport Capacity (Tc) is critical for soil erosion models. Tc data for different levels of vegetation cover, however, are quite limited. The objectives of this study were to evaluate the influence of stem basal cover, slope gradient and discharge on the Transport Capacity of overland flows for Tc prediction. A non-erodible flume (5 m long and 0.37 m wide) was used in this study. The discharge ranged from 0.5 × 10−3 to 2 × 10−3 m3 s−1, the slope gradient was from 8.8% to 25.9% and an artificial stem basal cover of 0, 1.25%, 2.5%, 5%, 10%, 15%, 20%, 25% and 30% was used to represent the natural vegetation. Stems 2 mm in diameter were randomly arranged. The sediment size for the experiment ranged from 0.25 to 0.59 mm with a median diameter of 0.35 mm. The results show that the measured Tc decreased exponentially as the stem basal cover increased, and the rate of decrease was far greater than what has been reported in the literature. The Transport Capacity was affected more by the stem basal cover than by slope and discharge when the cover exceeded approximately 2–3%. The research shows that the surface or stem basal cover plays a critical role in reducing the Transport Capacity of overland flows.
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Transport Capacity of Overland Flow for Sediment Mixtures
Journal of Hydrologic Engineering, 2017Co-Authors: Guang-hui ZhangAbstract:AbstractSediment Transport Capacity of overland flows is crucial to understanding and modeling the erosion and deposition processes. The Transport Capacity for sediment mixtures is particularly complex because of the need for an algorithm to allocate the total available shear stress or stream power to individual size classes. The Transport Capacity for sediment mixtures may not even be unique, depending on whether the flow is eroding or depositing sediments unless all sediments are of a single size class. Using data from a set of flume experiments involving steep slopes and high sediment concentrations, a formula was proposed for the sediment Transport Capacity limited by available stream power and available accommodation space for sediments. Sediments used for the experiment were further sieved into five distinct size classes. The sediment Transport Capacity was measured for 25 flow-slope combinations to validate a new formula for individual size classes, and to propose and test a method to compute the s...
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Transport Capacity of overland flow with high sediment concentration
Journal of Hydrologic Engineering, 2015Co-Authors: Bofu Yu, Guang-hui Zhang, Xudong FuAbstract:The concept and estimation of sediment Transport Capacity of overland flows are pivotal to soil erosion, sediment Transport, and deposition modeling. There is a limited understanding of the effect of high sediment concentration on the Transport Capacity of overland flow, although sediments in suspension are known to affect turbulent mixing and settling velocity in rivers. A new functional relationship between a dimensionless parameter involving stream power and settling velocity and the volumetric concentration at the Transport limit was developed using a set of flume experiments with slope up to 46.6%, unit discharge up to 50 cm 2 ·s −1 , median particle size of 0.326 mm, and sediment concentration up to 1,140 kg · m −3 . The new relationship has two theoretical limits on sediment concentration at the Transport limit. Under low flow conditions, the sediment concentration is limited by the available stream power. At high stream power, the sediment concentration is limited by the space available in flow to accommodate sediments in motion. As a predictor of the sediment concentration at the Transport limit, the new relationship worked very well with the Nash-Sutcliffe coefficient of efficiency of 0.95 and was shown to be superior to empirical relationships based on stream power and other commonly used predictors of the Transport Capacity for rivers. The paper also shows that formulas for the Transport Capacity which have beenvalidated and widely used for rivers with high sediment concentrations are inaccurate and should not be used to predict the Transport Capacity of overland flow. DOI: 10.1061/(ASCE)HE.1943-5584.0000998. © 2014 American Society of Civil Engineers.
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Effects of sediment size on Transport Capacity of overland flow on steep slopes
Hydrological Sciences Journal, 2011Co-Authors: Guang-hui Zhang, Li-li Wang, Ke-ming Tang, Rong-ting Luo, Xunchang ZhangAbstract:Abstract Sediment Transport Capacity is a key concept in determining rates of detachment and deposition in process-based erosion models, yet limited studies have been conducted on steep slopes. We investigated the effects of sediment size on Transport Capacity of overland flow in a flume. Unit flow discharge ranged from 0.66 to 5.26 × 10-3 m2 s-1, and slope gradient varied from 8.7 to 42.3%. Five sediment size classes (median diameter, d 50, of 0.10, 0.22, 0.41, 0.69 and 1.16 mm) were used. Sediment size was inversely related to Transport Capacity. The ratios of average Transport Capacity of the finest class to those of the 0.22, 0.41, 0.69 and 1.16 mm classes were 1.09, 1.30, 1.55 and 1.92, respectively. Sediment Transport Capacity increased as a power function of flow discharge and slope gradient (R2 = 0.98), shear stress (R2 = 0.95), stream power (R2 = 0.94), or unit stream power (R2 = 0.76). Transport Capacity generally decreased as a power function of sediment size (exponent = −0.35). Shear stress an...
Jeffrey G. Andrews - One of the best experts on this subject based on the ideXlab platform.
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Random Access Transport Capacity
IEEE Transactions on Wireless Communications, 2010Co-Authors: Jeffrey G. Andrews, Steven Weber, Marios Kountouris, M. HaenggiAbstract:We develop a new metric for quantifying end-to-end throughput in multihop wireless networks, which we term random access Transport Capacity, since the interference model presumes uncoordinated transmissions. The metric quantifies the average maximum rate of successful end-to-end transmissions, multiplied by the communication distance, and normalized by the network area. We show that a simple upper bound on this quantity is computable in closed-form in terms of key network parameters when the number of retransmissions is not restricted and the hops are assumed to be equally spaced on a line between the source and destination. We also derive the optimum number of hops and optimal per hop success probability and show that our result follows the well-known square root scaling law while providing exact expressions for the preconstants, which contain most of the design-relevant network parameters. Numerical results demonstrate that the upper bound is accurate for the purpose of determining the optimal hop count and success (or outage) probability.
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random access Transport Capacity
arXiv: Information Theory, 2009Co-Authors: Jeffrey G. Andrews, Steven Weber, Marios Kountouris, Martin HaenggiAbstract:We develop a new metric for quantifying end-to-end throughput in multihop wireless networks, which we term random access Transport Capacity, since the interference model presumes uncoordinated transmissions. The metric quantifies the average maximum rate of successful end-to-end transmissions, multiplied by the communication distance, and normalized by the network area. We show that a simple upper bound on this quantity is computable in closed-form in terms of key network parameters when the number of retransmissions is not restricted and the hops are assumed to be equally spaced on a line between the source and destination. We also derive the optimum number of hops and optimal per hop success probability and show that our result follows the well-known square root scaling law while providing exact expressions for the preconstants as well. Numerical results demonstrate that the upper bound is accurate for the purpose of determining the optimal hop count and success (or outage) probability.
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A simple upper bound on random access Transport Capacity
2009 47th Annual Allerton Conference on Communication Control and Computing (Allerton), 2009Co-Authors: Jeffrey G. Andrews, Steven Weber, Marios Kountouris, Martin HaenggiAbstract:We attempt to quantify end-to-end throughput in multihop wireless networks using a metric that measures the maximum density of source-destination pairs that can successfully communicate over a specified distance at certain data rate. We term this metric the random access Transport Capacity, since it is similar to Transport Capacity but the interference model presumes uncoordinated transmissions. A simple upper bound on this quantity is derived in closed-form in terms of key network parameters when the number of retransmissions is not restricted and the hops are assumed to be equally spaced on a line between the source and destination. We also derive the optimum number of hops — which is small and finite — and optimal per hop success probability for integer path loss exponents. We show that our result follows the well-known square root scaling law while providing exact expressions for the preconstants as well.
Zhi Ding - One of the best experts on this subject based on the ideXlab platform.
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Transport Capacity Analysis of Wireless In-Band Full Duplex Ad Hoc Networks
IEEE Transactions on Communications, 2017Co-Authors: Dongrun Qin, Zhi DingAbstract:This paper investigates the Transport Capacity of full-duplex ad hoc networks based on stochastic geometry. Unlike the more traditional half duplex nodes, full duplex wireless equipment can exchange data simultaneously over the same spectrum band. While full duplex transmission represents a promising mechanism to improve spectrum efficiency, the inevitable rise of interference from more transmitting nodes can also lower the rate of successful transmissions in ad hoc full duplex networks. We study the Transport Capacity of ad hoc full duplex networks by analyzing the successful packet decoding rate in both the physical link model and the protocol link model. We derive a new upper bound and a new lower bound for the network Transport Capacity, which can be used to lower complexity approximate analysis of the network Transport Capacity. We further determine the optimal transmission probability for maximizing network throughput and quantify the potential benefit of full duplex nodes. In both physical and protocol link models, our analysis shows that full duplex networks can nearly double the Transport Capacity against half duplex only with relatively small paired link distance. As the paired link distance grows, the Transport Capacity gain of full duplex networks begins to degrade.
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GLOBECOM - On Transport Capacity of Full Duplex Ad Hoc Networks
2015 IEEE Global Communications Conference (GLOBECOM), 2015Co-Authors: Dongrun Qin, Zhi DingAbstract:This work studies the Transport Capacity of the emerging full duplex wireless ad hoc networks. Unlike in isolated point-to- point transmission, in ad hoc networks, multiple nodes access the shared spectrum. The inevitable co-channel interference among neighboring nodes capable of full duplex transmission do not allow Capacity doubling over half-duplex nodes even after canceling self interference. To assess the Capacity gain, we extend the concept of Transport Capacity to full duplex ad hoc networks. We derive both an upper and a lower bounds for the network Transport Capacity in consideration of co-channel interference. Based on the analysis, we present numerical results to demonstrate the limit of Transport Capacity improvement between full duplex and half duplex networks. We also provide some helpful design guidelines for deploying ad hoc networks of full-duplex capable nodes.
Steven Weber - One of the best experts on this subject based on the ideXlab platform.
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Random Access Transport Capacity
IEEE Transactions on Wireless Communications, 2010Co-Authors: Jeffrey G. Andrews, Steven Weber, Marios Kountouris, M. HaenggiAbstract:We develop a new metric for quantifying end-to-end throughput in multihop wireless networks, which we term random access Transport Capacity, since the interference model presumes uncoordinated transmissions. The metric quantifies the average maximum rate of successful end-to-end transmissions, multiplied by the communication distance, and normalized by the network area. We show that a simple upper bound on this quantity is computable in closed-form in terms of key network parameters when the number of retransmissions is not restricted and the hops are assumed to be equally spaced on a line between the source and destination. We also derive the optimum number of hops and optimal per hop success probability and show that our result follows the well-known square root scaling law while providing exact expressions for the preconstants, which contain most of the design-relevant network parameters. Numerical results demonstrate that the upper bound is accurate for the purpose of determining the optimal hop count and success (or outage) probability.
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random access Transport Capacity
arXiv: Information Theory, 2009Co-Authors: Jeffrey G. Andrews, Steven Weber, Marios Kountouris, Martin HaenggiAbstract:We develop a new metric for quantifying end-to-end throughput in multihop wireless networks, which we term random access Transport Capacity, since the interference model presumes uncoordinated transmissions. The metric quantifies the average maximum rate of successful end-to-end transmissions, multiplied by the communication distance, and normalized by the network area. We show that a simple upper bound on this quantity is computable in closed-form in terms of key network parameters when the number of retransmissions is not restricted and the hops are assumed to be equally spaced on a line between the source and destination. We also derive the optimum number of hops and optimal per hop success probability and show that our result follows the well-known square root scaling law while providing exact expressions for the preconstants as well. Numerical results demonstrate that the upper bound is accurate for the purpose of determining the optimal hop count and success (or outage) probability.
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A simple upper bound on random access Transport Capacity
2009 47th Annual Allerton Conference on Communication Control and Computing (Allerton), 2009Co-Authors: Jeffrey G. Andrews, Steven Weber, Marios Kountouris, Martin HaenggiAbstract:We attempt to quantify end-to-end throughput in multihop wireless networks using a metric that measures the maximum density of source-destination pairs that can successfully communicate over a specified distance at certain data rate. We term this metric the random access Transport Capacity, since it is similar to Transport Capacity but the interference model presumes uncoordinated transmissions. A simple upper bound on this quantity is derived in closed-form in terms of key network parameters when the number of retransmissions is not restricted and the hops are assumed to be equally spaced on a line between the source and destination. We also derive the optimum number of hops — which is small and finite — and optimal per hop success probability for integer path loss exponents. We show that our result follows the well-known square root scaling law while providing exact expressions for the preconstants as well.
