On the Power of Uniform Power: Capacity of Wireless Networks with Bounded Resources

Avin, Chen; Lotker, Zvi; Pignolet, Yvonne Anne

doi:10.1007/978-3-642-04128-0_34

Cited by 20 publications

(14 citation statements)

References 13 publications

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“…Then there exists a subset L ′ ⊆ L such that L ′ is feasible under a uniform power assignment and |L ′ | ≥ |L| · Ω(1/ log L max ) . This lemma was proved by [3] only for the onedimensional setting. Indeed, at the end of their paper, the authors pose an open question regarding extending this result to the two-dimensional case.…”

Section: A From Constrained Diameter To Constrained Powermentioning

confidence: 85%

“…A recent work of [3] considers the capacity gap induced by the ability to adjust the transmission power (a.k.a. power control) if the available resources are bounded.…”

Section: A From Constrained Diameter To Constrained Powermentioning

confidence: 99%

“…power control) if the available resources are bounded. We start by stating the following Theorem from [3]. Lemma 4.4 ([3]): Let L be a feasible requests set with a non-uniform power assignment vector Ψ * .…”

Section: A From Constrained Diameter To Constrained Powermentioning

confidence: 99%

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Distributed power control in the SINR model

Lotker

Parter

Peleg

et al. 2011

2011 Proceedings IEEE INFOCOM

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The power control problem for wireless networks in the SINR model requires determining the optimal power assignment for a set of communication requests such that the SINR threshold is met for all receivers. If the network topology is known to all participants, then it is possible to compute an optimal power assignment in polynomial time. In realistic environments, however, such global knowledge is usually not available to every node. In addition, protocols that are based on global computation cannot support mobility and hardly adapt when participants dynamically join or leave the system. In this paper we present and analyze a fully distributed power control protocol that is based on local information. For a set of communication pairs, each consisting of a sender node and a designated receiver node, the algorithm enables the nodes to converge to the optimal power assignment (if there is one under the given constraints) quickly with high probability. Two types of bounded resources are considered, namely, the maximal transmission energy and the maximum distance between any sender and receiver. It is shown that the restriction to local computation increases the convergence rate by only a multiplicative factor of O(log n + log log Ψmax), where Ψmax is the maximal power constraint of the network. If the diameter of the network is bounded by Lmax then the increase in convergence rate is given by O(log n + log log Lmax).

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Section: A From Constrained Diameter To Constrained Powermentioning

confidence: 85%

“…A recent work of [3] considers the capacity gap induced by the ability to adjust the transmission power (a.k.a. power control) if the available resources are bounded.…”

Section: A From Constrained Diameter To Constrained Powermentioning

confidence: 99%

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Distributed power control in the SINR model

Lotker

Parter

Peleg

et al. 2011

2011 Proceedings IEEE INFOCOM

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“…Fanghänel et al [2009b] used a different approach and gave a randomized algorithm for PC-Scheduling that uses O(OPT log + log 2 n) slots. Finally, Avin et al [2009] show that the assumption of α > 2 used by all previous work may not be necessary, in that the ratio between optimal nonoblivious and oblivious capacity is O(log ), at least in the 1-dimensional metric. Fanghänel et al [2009a] gave a construction that shows that any schedule based on any oblivious power assignment can be a factor of n from optimal.…”

Section: Related Workmentioning

confidence: 97%

Wireless scheduling with power control

Halldórsson

2012

ACM Trans. Algorithms

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We consider the scheduling of arbitrary wireless links in the physical model of interference to minimize the time for satisfying all requests. We study here the combined problem of scheduling and power control, where we seek both an assignment of power settings and a partition of the links so that each set satisfies the signal-to-interference-plus-noise (SINR) constraints.We give an algorithm that attains an approximation ratio of O(log n · log log ), where n is the number of links and is the ratio between the longest and the shortest link length. Under the natural assumption that lengths are represented in binary, this gives the first approximation ratio that is polylogarithmic in the size of the input. The algorithm has the desirable property of using an oblivious power assignment, where the power assigned to a sender depends only on the length of the link. We give evidence that this dependence on is unavoidable, showing that any reasonably behaving oblivious power assignment results in a (log log )-approximation.These results hold also for the (weighted) capacity problem of finding a maximum (weighted) subset of links that can be scheduled in a single time slot. In addition, we obtain improved approximation for a bidirectional variant of the scheduling problem, give partial answers to questions about the utility of graphs for modeling physical interference, and generalize the setting from the standard 2-dimensional Euclidean plane to doubling metrics. Finally, we explore the utility of graph models in capturing wireless interference.

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“…Finally, using [4], the convexity and the improved fatness bound imply an approximate point location scheme for SINR+Voronoi zones whose preprocessing time and memory requirements are significantly more efficient than those obtained in [9]. For a recent work on batched point location tasks see [1].…”

Section: (B)-(c))mentioning

confidence: 99%

Nonuniform SINR+Voroni Diagrams Are Effectively Uniform

Kantor

Lotker

Parter

et al. 2015

Lecture Notes in Computer Science

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This paper concerns the behavior of an SINR diagram of wireless systems, composed of a set S of n stations embedded in R d , when restricted to the corresponding Voronoi diagram imposed on S. The diagram obtained by restricting the SINR zones to their corresponding Voronoi cells is referred to hereafter as an SINR+Voronoi diagram. While uniform SINR diagrams (where all stations transmit with the same power) are simple and nicely structured (e.g., the station reception zones are convex and "fat") [3], nonuniform SINR diagrams might be complex (e.g., the reception zones might be fractured and their boundaries might contain many singular points) [9]. In this paper, we establish the (perhaps surprising) fact that a nonuniform SINR+Voronoi diagram is topologically almost as nice as a uniform SINR diagram. In particular, it is convex and effectively 4 fat. This holds for every power assignment, every path-loss parameter α and every dimension d ≥ 1. The convexity property also holds for every SINR threshold β > 0, and the effective fatness holds for any β > 1. These fundamental properties provide a theoretical justification to engineering practices basing zonal tessellations on the Voronoi diagram, and helps to explain the soundness and efficacy of such practices. We also consider two algorithmic applications. The first concerns the Power Control with Voronoi Diagram (PCVD) problem, where given n stations embedded in some polygon P, it is required to find the power assignment that optimizes the SINR threshold of the transmission station si for any given reception point p ∈ P in its Voronoi cell Vor(si). The second application is approximate point location; we show that for SINR+Voronoi zones, this task can be solved considerably more efficiently than in the general non-uniform case.

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On the Power of Uniform Power: Capacity of Wireless Networks with Bounded Resources

Cited by 20 publications

References 13 publications

Distributed power control in the SINR model

Distributed power control in the SINR model

Wireless scheduling with power control

Nonuniform SINR+Voroni Diagrams Are Effectively Uniform

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