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56
Complexity in geometric sinr
 In MobiHoc
, 2007
"... In this paper we study the problem of scheduling wireless links in the geometric SINR model, which explicitly uses the fact that nodes are distributed in the Euclidean plane. We present the first NPcompleteness proofs in such a model. In particular, we prove two problems to be NPcomplete: Scheduli ..."
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Cited by 77 (2 self)
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In this paper we study the problem of scheduling wireless links in the geometric SINR model, which explicitly uses the fact that nodes are distributed in the Euclidean plane. We present the first NPcompleteness proofs in such a model. In particular, we prove two problems to be NPcomplete: Scheduling and OneShot Scheduling. The first problem consists in finding a minimumlength schedule for a given set of links. The second problem receives a weighted set of links as input and consists in finding a maximumweight subset of links to be scheduled simultaneously in one shot. In addition to the complexity proofs, we devise an approximation algorithm for each problem.
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 50 (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
Coloring unstructured radio networks
, 2005
"... During and immediately after their deployment, ad hoc and sensor networks lack an efficient communication scheme rendering even the most basic network coordination problems difficult. Before any reasonable communication can take place, nodes must come up with an initial structure that can serve as ..."
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Cited by 49 (8 self)
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During and immediately after their deployment, ad hoc and sensor networks lack an efficient communication scheme rendering even the most basic network coordination problems difficult. Before any reasonable communication can take place, nodes must come up with an initial structure that can serve as a foundation for more sophisticated algorithms. In this paper, we consider the problem of obtaining a vertex coloring as such an initial structure. We propose an algorithm that works in the unstructured radio network model. This model captures the characteristics of newly deployed ad hoc and sensor networks, i.e. asynchronous wakeup, no collisiondetection, and scarce knowledge about the network topology. When modeling the network as a graph with bounded independence, our algorithm produces a correct coloring with O(∆) colors in time O( ∆ log n) with high probability, where n and ∆ are the number of nodes in the network and the maximum degree, respectively. Also, the number of locally used colors depends only on the local node density. Graphs with bounded independence generalize unit disk graphs as well as many other wellknown models for
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 43 (6 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
How Optimal are Wireless Scheduling Protocols?
 IN: PROC. OF THE 26 TH ANNUAL JOINT CONF. OF THE IEEE COMPUTER AND COMMUNICATIONS SOCIETIES (INFOCOM
, 2007
"... In wireless networks mutual interference impairs the quality of received signals and might even prevent the correct reception of messages. It is therefore of paramount importance to dispose of power control and scheduling algorithms, coordinating the transmission of communication requests. We propo ..."
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Cited by 34 (1 self)
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In wireless networks mutual interference impairs the quality of received signals and might even prevent the correct reception of messages. It is therefore of paramount importance to dispose of power control and scheduling algorithms, coordinating the transmission of communication requests. We propose a new measure disturbance in order to comprise the intrinsic difficulty of finding a short schedule for a problem instance. Previously known approaches suffer from extremely bad performance in certain network scenarios even if disturbance is low. To overcome this problem, we present a novel scheduling algorithm for which we give analytical worstcase guarantees on its performance. Compared to previously known solutions, the algorithm achieves a speed up, which can be exponential in the size of the network.
Topology control for maintaining network connectivity and maximizing network capacity under the physical model
 in INFOCOM 2008. The 27th Conference on Computer Communications. IEEE
"... Abstract—In this paper we study the issue of topology control under the physical SignaltoInterferenceNoiseRatio (SINR) model, with the objective of maximizing network capacity. We show that existing graphmodelbased topology control captures interference inadequately under the physical SINR mod ..."
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Cited by 24 (2 self)
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Abstract—In this paper we study the issue of topology control under the physical SignaltoInterferenceNoiseRatio (SINR) model, with the objective of maximizing network capacity. We show that existing graphmodelbased topology control captures interference inadequately under the physical SINR model, and as a result, the interference in the topology thus induced is high and the network capacity attained is low. Towards bridging this gap, we propose a centralized approach, called Spatial Reuse Maximizer (MaxSR), that combines a power control algorithm T4P with a topology control algorithm P4T. T4P optimizes the assignment of transmit power given a fixed topology, where by optimality we mean that the transmit power is so assigned that it minimizes the average interference degree (defined as the number of interferencing nodes that may interfere with the ongoing transmission on a link) in the topology. P4T, on the other hand, constructs, based on the power assignment made in T4P, a new topology by deriving a spanning tree that gives the minimal interference degree. By alternately invoking the two algorithms, the power assignment quickly converges to an operational point that maximizes the network capacity. We formally prove the convergence of MaxSR. We also show via simulation that the topology induced by MaxSR outperforms that derived from existing topology control algorithms by 50%110 % in terms of maximizing the network capacity. I.
Local Broadcasting in the Physical Interference Model
, 2008
"... In this work we analyze the complexity of local broadcasting in the physical interference model. We present two distributed randomized algorithms: one that assumes that each node knows how many nodes there are in its geographical proximity, and another, which makes no assumptions about topology know ..."
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Cited by 22 (2 self)
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In this work we analyze the complexity of local broadcasting in the physical interference model. We present two distributed randomized algorithms: one that assumes that each node knows how many nodes there are in its geographical proximity, and another, which makes no assumptions about topology knowledge. We show that, if the transmission probability of each node meets certain characteristics, the analysis can be decoupled from the global nature of the physical interference model, and each node performs a successful local broadcast in time proportional to the number of neighbors in its physical proximity. We also provide worstcase optimality guarantees for both algorithms and demonstrate their behavior in average scenarios through simulations.
Interference Cancellation: Better Receivers for a New Wireless MAC
"... We argue that carrier sense in 802.11 and other wireless protocols leads to scheduling decisions that are overly pessimistic and hence waste capacity. As an alternative, we propose interference cancellation, in which simultaneous signals are modeled and decoded together rather than treating all but ..."
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Cited by 22 (2 self)
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We argue that carrier sense in 802.11 and other wireless protocols leads to scheduling decisions that are overly pessimistic and hence waste capacity. As an alternative, we propose interference cancellation, in which simultaneous signals are modeled and decoded together rather than treating all but one as random noise. This method greatly expands the conditions under which overlapping transmissions can be successfully received, even by a single receiver. We demonstrate the practicality of these better receivers via a proofofconcept experiment with USRP software radios. We argue that supporting concurrent transmissions enables new and more effective wireless MACs in which carrier sense is disabled.
Physical interference driven dynamic spectrum management
 IN PROC. OF THE THIRD IEEE SYMPOSIUM ON NEW FRONTIERS IN DYNAMIC SPECTRUM ACCESS NETWORKS (DYSPAN 2008)
, 2008
"... Abstract — Dynamic spectrum management can drastically improve the performance of wireless networks struggling under increasing user demands. However, performing efficient spectrum allocation is a complex and difficult process. Current proposals make the problem tractable by simplifying interference ..."
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Cited by 17 (5 self)
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Abstract — Dynamic spectrum management can drastically improve the performance of wireless networks struggling under increasing user demands. However, performing efficient spectrum allocation is a complex and difficult process. Current proposals make the problem tractable by simplifying interference constraints as conflict graphs, but they face potential performance degradation from inaccurate interference estimation. In this paper, we show that conflict graphs, if optimized properly, can produce spectrum allocations that closely match those derived from the physical interference model. Thus we propose PLAN, a systematic framework to produce conflict graphs based on physical interference characteristics. PLAN first applies an analytical framework to derive the criterion for identifying conflicting neighbors, capturing the cumulative effect of interference. PLAN then applies a local conflict adjustment algorithm to address heterogeneous interference conditions and improve spectrum allocation efficiency. Through detailed analysis and experimental evaluations, we show that PLAN builds a conflict graph to effectively represent the complex interference conditions and allow the reuse of efficient graphbased spectrum allocation solutions. PLAN also significantly outperforms the conventional graph model based solutions. I.
Practical Conflict Graphs for Dynamic Spectrum Distribution
"... Most spectrum distribution proposals today develop their allocation algorithms that use conflict graphs to capture interference relationships. The use of conflict graphs, however, is often questioned by the wireless community because of two issues. First, building conflict graphs requires significan ..."
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Cited by 15 (1 self)
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Most spectrum distribution proposals today develop their allocation algorithms that use conflict graphs to capture interference relationships. The use of conflict graphs, however, is often questioned by the wireless community because of two issues. First, building conflict graphs requires significant overhead and hence generally does not scale to outdoor networks, and second, the resulting conflict graphs do not capture accumulative interference. In this paper, we use largescale measurement data as ground truth to understand just how severe these issues are in practice, and whether they can be overcome. We build “practical”conflict graphs using measurementcalibrated propagation models, whichremovetheneedforexhaustivesignal measurements by interpolating signal strengths using calibrated models. These propagation models are imperfect, and we study the impact of their errors by tracing the impact on multiple steps in the process, from calibrating propagation models to predicting signal strength and building conflict graphs. At each step, we analyze the introduction, propagation and final impact of errors, by comparing each intermediate result to its ground truth counterpart generated from measurements. Our work produces several findings. Calibrated propagation models generate locationdependent prediction errors, ultimately producing conservative conflict graphs. While these “estimated conflict graphs ” lose some spectrum utilization, their conservative nature improves reliability by reducing the impact of accumulative interference. Finally, we propose a graph augmentation technique that addresses any remaining accumulative interference, the last missing piece in a practical spectrum distribution system using measurementcalibrated conflict graphs.