Capacity scaling laws of information dissemination modalities in wireless ad hoc networks

Wang, Zheng; Sadjadpour, Hamid R.; Garcia-Luna-Aceves, J.J.

doi:10.1109/allerton.2008.4797692

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…Observe that the results from [27] indicate that the throughput order in Theorem 1 is achievable under the physical model, which can always serve as a lower bound to the Gaussian channel model. For completeness of presentation, we outline our proof of Theorem 1 here.…”

Section: E Our Main Contributionsmentioning

confidence: 92%

Multicast Capacity of Wireless Ad Hoc Networks

2009

IEEE/ACM Trans. Networking

112

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Abstract-We study the multicast capacity of large-scale random extended multihop wireless networks, where a number of wireless nodes are randomly located in a square region with side length a = p n, by use of Poisson distribution with density 1. All nodes transmit at a constant power P , and the power decays with attenuation exponent > 2. The data rate of a transmission is determined by the SINR as B log(1 + SINR), where B is the bandwidth. There are n s randomly and independently chosen multicast sessions. Each multicast session has k randomly chosen terminals.We show that when k 1 n (log n) and n s 2 n 1=2+ , the capacity that each multicast session can achieve, with high probability, is at least c 8 p n n p k , where 1 , 2 , and c 8 are some special constants and > 0 is any positive real number. We also show that for k = O( n log n ), the per-flow multicast capacity under Gaussian channel is at most O( p n n p k ) when we have at least n s = (log n) random multicast flows. Our result generalizes the unicast capacity for random networks using percolation theory.

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Section: E Our Main Contributionsmentioning

confidence: 92%

Multicast Capacity of Wireless Ad Hoc Networks

2009

IEEE/ACM Trans. Networking

112

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“…Recently, Wang et al [25] studied the multicast capacity under Gaussian model and show that the per-flow bound Ω( √ n n s √ k ) still applies when k = O( n log α+1 n ). Wang et al [26] studied capacity scaling laws for random wireless ad hoc networks under (n, m, k)-cast formulation, where n, m, and k denote the number of nodes in the network, the number of destinations for each communication group, and the actual number of communication group members that receive information (i.e., k ≤ m ≤ n), respectively and when nodes are endowed with multi-packet transmission (MPT) or multi-packet reception (MPR) capabilities.…”

Section: Definitionmentioning

confidence: 99%

Multicast Capacity of Wireless Ad Hoc Networks Under Gaussian Channel Model

Tang

2010

IEEE/ACM Trans. Networking

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Abstract-In this paper, we study the multicast capacity of a large scale random wireless network. We consider extended multihop networks, where a number of wireless nodes are randomly located in a square region with side-length a = √ n, by use of Poisson distribution with density 1. All nodes transmit at a constant power P , and the power decays along the path with attenuation exponent α > 2. The data rate of a transmission is determined by the SINR as B log(1 + SINR), where B is the bandwidth. There are n s randomly and independently chosen multicast sessions. Each multicast session has k randomly chosen terminals. We show that, when k ≤ θ 1 n (log n) 2α+6 , and ns ≥ θ2n 1/2+β , the capacity that each multicast session can achieve, with high probability, is at least c8, where θ1, θ2, and c 8 are some special constants and β > 0 is any positive real number. We also show that for k = O( n log 2 n ), the per-flow multicast capacity under Gaussian channel is at most O(when we have at least n s = Ω(log n) random multicast flows. Our result generalizes the unicast capacity [3] for random networks using percolation theory.

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