Multicast capacity of large homogeneous multihop wireless networks

Keshavarz‐Haddad, Alireza; Riedi, Rudolf H.

doi:10.1109/wiopt.2008.4586053

Cited by 32 publications

(45 citation statements)

References 29 publications

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“…According to Assumption 3, SaN is indeed dense scaling [6], [14], [15], [21], [22], while PhN is an extended network [4], [5], [12], [23]- [25]. More discussions about two types of scaling networks can be found in Section II-B of our technical report [26].…”

Section: A Network Topologymentioning

confidence: 99%

“…Our model has three novel points relative to most existing works: (1) Since multicast capacity can be regarded as the general result of unicast and broadcast capacity [6], we directly study the multicast capacity for cognitive networks in order to enhance the generality of this study. (2) Since pure ad hoc networks and BS-based networks (static cellular networks) can be regarded as the special case of hybrid networks in terms of the number of BSs [4], [5], [7]- [11], we consider the case that the primary network is a hybrid network, which further increases the generality of our model.…”

Section: Introductionmentioning

confidence: 99%

“…Like in the single random dense network [6], when the number of destination nodes of each multicast session is beyond some threshold, to be specific, = Ω( (log ) 2 ), the multicast throughput derived by the multicast strategy based on the FHs and SHs cooperatively is not optimal in order sense. For this case, the multicast strategy based only on SHs can derive larger throughput.…”

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confidence: 99%

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Multicast capacity scaling for cognitive networks: General extended primary network

Wang

Tang

et al. 2010

The 7th IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (IEEE MASS 2010)

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Abstract-We study the capacity scaling laws for the cognitive network that consists of the primary hybrid network (PhN) and secondary ad hoc network (SaN). PhN is further comprised of an ad hoc network and a base station based (BS-based) network. SaN and PhN are overlapping in the same deployment region, operate on the same spectrum, but are independent with each other in terms of communication requirements. The primary users (PUs), i.e., the ad hoc nodes in PhN, have the priority to access the spectrum. The secondary users (SUs), i.e., the ad hoc nodes in SaN, are equipped with cognitive radios, and have the functionalities to sense the idle spectrum and obtain the necessary information of primary nodes in PhN. We assume that PhN adopts one out of three classical types of strategies, i.e., pure ad hoc strategy, BS-based strategy, and hybrid strategy. We aim to directly derive multicast capacity for SaN to unify the unicast and broadcast capacity under two basic principles: (1) The throughput for PhN cannot be undermined in order sense due to the presence of SaN. (2) The protocol adopted by PhN does not alter in the interest of SaN, anyway. Depending on which type of strategy is adopted in PhN, we design the optimal-throughput strategy for SaN. We show that there exists a threshold of the density of SUs according to the density of PUs beyond which it can be proven that: (1) when PhN adopts the pure ad hoc strategy or hybrid strategy, SaN can achieve the multicast capacity of the same order as it is stand-alone; (2) when PhN adopts the BS-based strategy, SaN can asymptotically achieve the multicast capacity of the same order as if PhN were absent, if some conditions of the relations among the number of SUs, PUs, the destinations of each multicast sessions in SaN, and the base stations in PhN hold.

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Section: A Network Topologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Multicast capacity scaling for cognitive networks: General extended primary network

Wang

Tang

et al. 2010

The 7th IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (IEEE MASS 2010)

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“…KeshavarzHaddad et al [27] proposed a technique called arena to study upper bounds of capacity. They [31] devised a scheme and computed the achievable throughput for random dense networks.…”

Section: Under Gaussian Channel Modelmentioning

confidence: 99%

Multicast Throughput of Hybrid Wireless Networks Under Gaussian Channel Model

Wang

Tang

et al. 2009

2009 29th IEEE International Conference on Distributed Computing Systems

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Abstract-We study the multicast capacity for hybrid wireless networks consisting of ordinary ad hoc nodes and base stations under Gaussian Channel model, which generalizes both the unicast capacity and broadcast capacity for hybrid wireless networks. Assume that all ordinary ad hoc 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 (Signal to Interference plus Noise Ratio) at the receiver as B log(1 + SINR). The ordinary ad hoc nodes are placed in the square region A(a) of area a according to a Poisson point process of intensity n/a. Then, m additional base stations (BSs) acting as the relaying communication gateway are placed regularly in the region A(a), and are connected by a high-bandwidth wired network. Let a = n and a = 1, we construct the hybrid extended network (HEN) and hybrid dense network (HDN), respectively. We choose randomly and independently ns ordinary ad hoc nodes to be the sources of multicast sessions. We assume that each multicast session has n d randomly chosen terminals. Three broad categories of multicast strategies are proposed. The first one is the hybrid strategy, i.e., the multihop scheme with BSsupported, which further consists of two types of strategies called connectivity strategy and percolation strategy respectively. The second one is the ordinary ad hoc strategy, i.e., the multihop scheme without any BS-supported. The third one is the classical BSbased strategy under which any communications between ordinary ad hoc node pairs are relayed by some specific BSs. According to the different scenarios in terms of m, n and n d , we select the optimal scheme from the three categories of strategies, and derive the achievable multicast throughput based on the optimal decision.

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“…It was shown in [15] that such successful communication condition for the physical model in random networks can be translated into the successful communication criterion for the protocol model in an arbitrary network when β = (1 + ∆) α . In (n, m, m)-cast communication, when a node transmits a packet, we can assume two different approaches to compute the capacity [16]. We can either assume that, for each transmission, only a single node receives the packet or multiple nodes within an area of transmission range receive the packet.…”

Section: Lemma 41mentioning

confidence: 99%