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54
A ConstantFactor Approximation for Wireless Capacity Maximization with Power Control in the SINR Model
 In Proc. of the 22nd annual ACMSIAM symposium on Discrete algorithms (SODA
, 2011
"... In modern wireless networks devices are able to set the power for each transmission carried out. Experimental but also theoretical results indicate that such power control can improve the network capacity significantly. We study this problem in the physical interference model using SINR constraints. ..."
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Cited by 49 (9 self)
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In modern wireless networks devices are able to set the power for each transmission carried out. Experimental but also theoretical results indicate that such power control can improve the network capacity significantly. We study this problem in the physical interference model using SINR constraints. In the SINR capacity maximization problem, we are given n pairs of senders and receivers, located in a metric space (usually a socalled fading metric). The algorithm shall select a subset of these pairs and choose a power level for each of them with the objective of maximizing the number of simultaneous communications. This is, the selected pairs have to satisfy the SINR constraints with respect to the chosen powers. We present the first algorithm achieving a constantfactor approximation in fading metrics. The best previous results depend on further network parameters such as the ratio of the maximum and the minimum distance between a sender and its receiver. Expressed only in terms of n, they are (trivial) Ω(n) approximations. Our algorithm still achieves an O(log n) approximation if we only assume to have a general metric space rather than a fading metric. Furthermore, existing approaches work well together with the algorithm allowing it to be used in singlehop and multihop scheduling scenarios. Here, we also get polylog n approximations. 1
Wireless scheduling with power control
 In Proc. 17th European Symposium on Algorithms (ESA
, 2009
"... 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 e ..."
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Cited by 44 (3 self)
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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 signaltointerferenceplusnoise (SINR) constraints. We give an algorithm that attains an approximation ratio of O(log n · log log Λ), where Λ is the ratio between the longest and the shortest linklength. Under the natural assumption that lengths are represented in binary, this gives the first polylog(n)approximation. 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 show this dependence on Λ to be unavoidable, giving a construction for which any oblivious power assignment results in a Ω(log log Λ)approximation. We also give a simple online algorithm that yields a O(log Λ)approximation, by a reduction to the coloring of unitdisc graphs. 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 2dimensional Euclidean plane to doubling metrics. 1
Distributed contention resolution in wireless networks
 In DISC
, 2010
"... We present and analyze simple distributed contention resolution protocols for wireless networks. In our setting, one is given n pairs of senders and receivers located in a metric space. Each sender wants to transmit a signal to its receiver at a prespecified power level, e. g., all senders use the s ..."
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Cited by 35 (5 self)
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We present and analyze simple distributed contention resolution protocols for wireless networks. In our setting, one is given n pairs of senders and receivers located in a metric space. Each sender wants to transmit a signal to its receiver at a prespecified power level, e. g., all senders use the same, uniform power level as it is typically implemented in practice. Our analysis is based on the physical model in which the success of a transmission depends on the SignaltoInterferenceplusNoiseRatio (SINR). The objective is to minimize the number of time slots until all signals are successfully transmitted. Our main technical contribution is the introduction of a measure called maximum average affectance enabling us to analyze random contentionresolution algorithms in which each packet is transmitted in each step with a fixed probability depending on the maximum average affectance. We prove that the schedule generated this way is only an O(log 2 n) factor longer than the optimal one, provided that the prespecified power levels satisfy natural monontonicity properties. By modifying the algorithm, senders need not to know the maximum average affectance in advance but only static information about the network. In addition, we extend our approach to multihop communication achieving the same appoximation factor.
Distributed Algorithms for Approximating Wireless Network Capacity
"... Abstract—In this paper we consider the problem of maximizing wireless network capacity (a.k.a. oneshot scheduling) in both the protocol and physical models. We give the first distributed algorithms with provable guarantees in the physical model, and show how they can be generalized to more complica ..."
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Cited by 20 (2 self)
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Abstract—In this paper we consider the problem of maximizing wireless network capacity (a.k.a. oneshot scheduling) in both the protocol and physical models. We give the first distributed algorithms with provable guarantees in the physical model, and show how they can be generalized to more complicated metrics and settings in which the physical assumptions are slightly violated. We also give the first algorithms in the protocol model that do not assume transmitters can coordinate with their neighbors in the interference graph, so every transmitter chooses whether to broadcast based purely on local events. Our techniques draw heavily from algorithmic game theory and machine learning theory, even though our goal is a distributed algorithm. Indeed, our main results allow every transmitter to run any algorithm it wants, so long as its algorithm has a learningtheoretic property known as noregret in a gametheoretic setting. I.
Nearly optimal bounds for distributed wireless scheduling in the sinr model. Arxiv preprint arXiv:1104.5200
, 2011
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Topology Control for Effective Interference Cancellation in MultiUser MIMO Networks
"... Abstract—In MultiUser MIMO networks, receivers decode multiple concurrent signals using Successive Interference Cancellation (SIC). With SIC a weak target signal can be deciphered in the presence of stronger interfering signals. However, this is only feasible if each strong interfering signal satis ..."
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Cited by 13 (2 self)
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Abstract—In MultiUser MIMO networks, receivers decode multiple concurrent signals using Successive Interference Cancellation (SIC). With SIC a weak target signal can be deciphered in the presence of stronger interfering signals. However, this is only feasible if each strong interfering signal satisfies a signaltonoiseplusinterference ratio (SINR) requirement. This necessitates the appropriate selection of a subset of links that can be concurrently active in each receiver’s neighborhood; in other words, a subtopology consisting of links that can be simultaneously active in the network is to be formed. If the selected subtopologies are of small size, the delay between the transmission opportunities on a link increases. Thus, care should be taken to form a limited number of subtopologies. We find that the problem of constructing the minimum number of subtopologies such that SIC decoding is successful with a desired probability threshold, is NPhard. Given this, we propose MUSIC, a framework that greedily forms and activates subtopologies, in a way that favors successful SIC decoding with a high probability. MUSIC also ensures that the number of selected subtopologies is kept small. We provide both a centralized and a distributed version of our framework. We prove that our centralized version approximates the optimal solution for the considered problem. We also perform extensive simulations to demonstrate that (i) MUSIC forms a small number of subtopologies that enable efficient SIC operations; the number of subtopologies formed is at most 17 % larger than the optimum number of topologies, discovered through exhaustive search (in small networks). (ii) MUSIC outperforms approaches that simply consider the number of antennas as a measure for determining the links that can be simultaneously active. Specifically, MUSIC provides throughput improvements of up to 4 times, as compared to such an approach, in various topological settings. The improvements can be directly attributable to a significantly higher probability of correct SIC based decoding with MUSIC.
Online Capacity Maximization in Wireless Networks ∗
"... In this paper we study a dynamic version of capacity maximization in the physical model of wireless communication. In our model, requests for connections between pairs of points in Euclidean space of constant dimension d arrive iteratively over time. When a new request arrives, an online algorithm n ..."
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Cited by 13 (4 self)
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In this paper we study a dynamic version of capacity maximization in the physical model of wireless communication. In our model, requests for connections between pairs of points in Euclidean space of constant dimension d arrive iteratively over time. When a new request arrives, an online algorithm needs to decide whether or not to accept the request and to assign one out of k channels and a transmission power to the channel. Accepted requests must satisfy constraints on the signaltointerferenceplusnoise (SINR) ratio. The objective is to maximize the number of accepted requests. Using competitive analysis we study algorithms using distancebased power assignments, for which the power of a request relies only on the distance between the points. Such assignments are inherently local and particularly useful in distributed settings. We first focus on the case of a single channel. For request sets with spatial lengths in [1, ∆] and duration in [1, Γ] we derive a lower bound of Ω(Γ · ∆ d/2) on the competitive ratio of any deterministic online algorithm using a distancebased power assignment. Our main“ result is a nearoptimal deterministic algorithm that is O Γ · ∆ (d/2)+εcompetitive, for any constant ε> 0. Our algorithm for a single channel can be generalized to k channels. “ It can be adjusted to yield a competitive ratio of O k · Γ 1/k′ · ∆ (d/2k′ ′ ”
A constant approximation algorithm for link scheduling in arbitrary networks under physical interference model
 Proc. ACM FOWANC
, 2009
"... Link scheduling is crucial in improving the throughput in wireless networks and it has been widely studied under various interference models. In this paper, we study the link scheduling problem under physical interference model where all senders of the links transmit at a given power P and a link ca ..."
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Cited by 12 (5 self)
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Link scheduling is crucial in improving the throughput in wireless networks and it has been widely studied under various interference models. In this paper, we study the link scheduling problem under physical interference model where all senders of the links transmit at a given power P and a link can transmit successfully if and only if the SignaltoInterferenceplusNoiseRatio (SINR) at the corresponding receiver is at least a certain threshold. The link scheduling problem is to find a maximum “independent set ” (MIS) of links, i.e., the maximum number of links that can transmit successfully in one timeslot, given a set of input links. This problem has been shown to be NPhard [10]. Here we propose the first link scheduling algorithm with a constant approximation ratio for arbitrary background noise N ≥ 0. When each link l has a weight w(l)> 0, we propose a method for weighted MIS with approximation ratio O(min(log maxl∈L w(l) minl∈L w(l) , log maxl∈L ‖l‖)), where‖l‖ minl∈L ‖l‖ is the Euclidean length of a link l.
F.: Maximum independent set of links under physical interference model
"... Abstract. This paper addresses the following optimization problem in a plane multihop wireless networks under the physical interference model: From a given a set of communication links whose senders transmit at a fixed uniform power level, select a maximum set of independent links. This problem is k ..."
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Cited by 10 (2 self)
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Abstract. This paper addresses the following optimization problem in a plane multihop wireless networks under the physical interference model: From a given a set of communication links whose senders transmit at a fixed uniform power level, select a maximum set of independent links. This problem is known to be NPhard. The existing approximation algorithms which were claimed to have constant approximation bounds are either valid only in the absence of background noise or simply incorrect in the presence of background noise. In this paper, we develop a new approximation algorithm with constant approximation bound regardless of the value of the background noise. In addition, our approximation bound valid in general is significantly smaller than all the known bounds which are only valid under certain special assumptions. 1
Scheduling in Wireless Networks with RayleighFading Interference
"... We study algorithms for wireless spectrum access of n communication requests when interference conditions are given by the Rayleighfading model. This model extends the recently popular deterministic interference model based on the signaltointerferenceplusnoise ratio (SINR) using stochastic prop ..."
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Cited by 9 (3 self)
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We study algorithms for wireless spectrum access of n communication requests when interference conditions are given by the Rayleighfading model. This model extends the recently popular deterministic interference model based on the signaltointerferenceplusnoise ratio (SINR) using stochastic propagation to address fading effects observed in reality. We consider worstcase approximation guarantees for the two standard problems of capacity maximization (maximize the expected number of successful transmissions in a single slot) and latency minimization (minimize the expected number of slots until all transmissions were successful). Our main result is a generic reduction of Rayleigh fading to the deterministic SINR model. It allows to apply existing algorithms for the nonfading model in the Rayleighfading scenario while losing only a factor of O(log ∗ n) in the approximation guarantee. This way, we obtain the first approximation guarantees for Rayleigh fading and, more fundamentally, show that nontrivial stochastic fading effects can be successfully handled using existing and future techniques for the nonfading model. Using a more detailed argument, a similar result applies even for distributed and gametheoretic capacity maximization approaches. For example, it allows to show that regret learning yields an O(log ∗ n)approximation with uniform power assignments. Our analytical treatment is supported by simulations illustrating the performance of regret learning and, more generally, the relationship between both models.