Bounds for the capacity of wireless multihop networks imposed by topology and demand

Keshavarz‐Haddad, Alireza; Riedi, Rudolf H.

doi:10.1145/1288107.1288142

Cited by 58 publications

(45 citation statements)

References 28 publications

(67 reference statements)

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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%

“…Obviously, we can use such upper bounds as that for SaN whatever strategy is adopted by PhN, because under Gaussian channel model PhN and SaN always have negative influence (interference) on each other under the noncooperative communication scheme as long as they share the same spectrum at the same time. To compute such upper bounds, we exploit the homogeneity property and randomness property of network topology, [15].…”

Section: Introductionmentioning

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%

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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show abstract

“…The former model is simpler, thus more convenient for the analysis for many issues, besides capacity, of wireless networks, such as localization [23], [24], coverage [25], and lifetime [26] problems in wireless sensor networks. Gaussian Channel model captures better the nature of wireless medium, [3], [27].…”

Section: Literature Reviewsmentioning

confidence: 99%

“…In [3], such threshold of n d was improved to n d = O( n (log n) α+1 ), and the corresponding upper bounds were proposed. 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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“…Keshavarz-Haddad et al [5] introduced the concept of transmission arena. Based on that definition, they presented a method to compute the upper bound of the capacity for different traffic patterns and different topologies of the network.…”

Section: Introductionmentioning

confidence: 99%

The Capacity of Ad Hoc Networks with Heterogeneous Traffic Using Cooperation

Ji¹,

Wang²,

Sadjadpour³

et al. 2010

2010 Proceedings IEEE INFOCOM

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Abstract-We study the scaling laws for wireless ad hoc networks in which the distribution of n nodes in the network is homogeneous but the traffic they carry is heterogeneous. More specifically, we consider the case in which a given node is the data-gathering sink for k sources sending different information to it, while the rest of the s = n − k nodes participate in unicast sessions with random destinations chosen uniformly. We present a separation theorem for heterogeneous traffic showing that the optimum order throughput capacity can be attained in a wireless network in which traffic classes are distributed uniformly by endowing each node with multiple radios, each operating in a separate orthogonal channel, and by allocating a radio per node to each traffic class. Based on this theorem, we show how this order capacity can be attained for the unicast and data-gathering traffic classes by extending cooperative communication schemes that have been proposed previously.

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Bounds for the capacity of wireless multihop networks imposed by topology and demand

Cited by 58 publications

References 28 publications

Multicast capacity scaling for cognitive networks: General extended primary network

Multicast capacity scaling for cognitive networks: General extended primary network

Multicast Throughput of Hybrid Wireless Networks Under Gaussian Channel Model

The Capacity of Ad Hoc Networks with Heterogeneous Traffic Using Cooperation

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