Scaling Laws of Multicast Capacity for Power-Constrained Wireless Networks under Gaussian Channel Model

Abstract-In this paper, we investigate capacity scaling laws of wireless social networks under the social-based session formation. We model a wireless social network as a three-layered structure, consisting of the physical layer, social layer, and session layer ; and we introduce a cross-layer distance&density-aware model, called the population-based formation model, under which: 1) for each node v k , the number of its friends/followers, denoted by q k , follows a Zipf's distribution with degree clustering exponent γ; 2) q k anchor points are independently chosen according to a probability distribution with density function proportional to ( E k,X ) −β , where E k,X is the expected number of nodes (population) within the distance |v k − X| to v k , and β is the clustering exponent of friendship formation; 3) finally, q k nodes respectively nearest to those q k anchor points are selected as the friends of v k . We present the general density function of social relationship distribution, with general distribution of physical layer, serving as the basis for studying general capacity of wireless social networks. As the first step of addressing this issue, for the homogeneous physical layer, we derive the social-broadcast capacity under both generalized physical and protocol interference models, taking into account general clustering exponents of both friendship degree and friendship formation in a 2-dimensional parameter space, i.e., (γ, β) ∈ [0, ∞) 2 . Importantly, we notice that the adopted model with homogenous physical layer does not sufficiently reflect the advantages of the population-based formation model in terms of realistic validity and practicability. Accordingly, we introduce a random network model, called the center-clustering random model (CCRM) with node distribution exponent δ ∈ [0, ∞), highlighting the clustering and inhomogeneity property in real-life networks, and discuss how to further derive more general network capacity over 3-dimensional parameter space (δ, γ, β) ∈ [0, ∞) 3 based on our results over (γ, β) ∈ [0, ∞) 2 .

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“…Note that when δ = 0, the model degenerates into the homogeneous random extended network, [6], [25].…”

Section: Center-clustering Random Model (Ccrm)mentioning

confidence: 99%

Capacity Scaling of Wireless Social Networks

Wang

Shao

Zhong

et al. 2015

IEEE Trans. Parallel Distrib. Syst.

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“…1 This throughput scaling is achieved in such a way that data is delivered from a node to another node in a multihop fashion. There have been further studies on multihop in the literature [5,[23][24][25][26], while the total throughput scales far less than HðnÞ. Besides, the almost linear throughput scaling law Hðn 1À Þ for an arbitrarily small [ 0 was derived using a hierarchical cooperation scheme [27] in the Gaussian network model of unit area.…”

Section: Studies On the Capacity Scaling Lawmentioning

confidence: 99%

Capacity of large hybrid erasure networks with random node distribution

Shin

Jeong

2015

Wireless Netw

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The Gupta-Kumar's nearest-neighbor multihop routing with/without infrastructure support achieves the optimal capacity scaling in a large erasure network in which n wireless nodes and m relay stations are regularly placed. In this paper, a capacity scaling law is completely characterized for an infrastructure-supported erasure network where n wireless nodes are randomly distributed, which is a more feasible scenario. We use two fundamental path-loss attenuation models (i.e., exponential and polynomial power-laws) to suitably model an erasure probability. To show our achievability result, the multihop routing via percolation highway is used and the corresponding lower bounds on the total capacity scaling are derived. Cut-set upper bounds on the capacity scaling are also derived. Our result indicates that, under the random erasure network model with infrastructure support, the achievable scheme based on the percolation highway routing is order-optimal within a polylogarithmic factor of n for all values of m.

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“…In this paper, we focus on an ad hoc network with the random extended network model [4], [10], [19], [32], where n mobile nodes are distributed randomly and uniformly on a square region…”

Section: Network Modelmentioning

confidence: 99%

“…In this scenario, if the value of a given conditional expression is beyond the threshold, the transmitter can send data successfully to the receiver at a specific constant data rate; otherwise, it cannot send data at any rate, i.e., the transmission rate is assumed to be a binary function. Both the protocol model and the physical model defined in [1] belong to this type of models [19].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Rate Adaptation on Capacity-Delay Tradeoffs in Mobile Ad Hoc Networks

Wang

Jiang

et al. 2014

IEEE Trans. on Mobile Comput.

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Abstract-In this paper, we focus on the asymptotic capacity and delay, and their tradeoffs in mobile ad hoc networks (MANETs). As we all know, some fixed rate communication models such as the protocol model and the physical model have been studied in the past. However, our work aims to investigate the impact of an adaptive rate communication model on capacity-delay tradeoffs in MANETs under classical mobility models. Specifically, we adopt a well-known adaptive rate model called the generalized physical model (GphyM). The mobility of nodes is characterized by two broad classes of practical mobility models and they are hybrid random walk models and discrete random direction models. The two models generalize many mobility models studied in the literature, including the random walk, i.i.d., Brownian, and random way point models. For each mobility model, we derive the optimal delay for the optimal persession unicast capacity (that of constant order Θ(1)) under the generalized physical model, depending on the individual parameters of mobility models. In particular, we show that for the i.i.d. model, compared with those under the protocol and physical models, the adaptive feature of link rate under the generalized physical model results in a significant decrease in the optimal delay for the optimal capacity; more precisely, both the optimal capacity and optimal delay can be simultaneously achieved, while there is no improvement for the random way-point model.

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Scaling Laws of Multicast Capacity for Power-Constrained Wireless Networks under Gaussian Channel Model

Cited by 14 publications

References 36 publications

Capacity Scaling of Wireless Social Networks

Capacity Scaling of Wireless Social Networks

Capacity of large hybrid erasure networks with random node distribution

The Impact of Rate Adaptation on Capacity-Delay Tradeoffs in Mobile Ad Hoc Networks

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