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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
A fast distributed approximation algorithm for minimum spanning trees
 IN PROCEEDINGS OF THE 20TH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING (DISC
, 2006
"... We present a distributed algorithm that constructs an O(log n)approximate minimum spanning tree (MST) in any arbitrary network. This algorithm runs in time Õ(D(G) + L(G, w)) where L(G, w) is a parameter called the local shortest path diameter and D(G) is the (unweighted) diameter of the graph. Our ..."
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Cited by 36 (8 self)
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We present a distributed algorithm that constructs an O(log n)approximate minimum spanning tree (MST) in any arbitrary network. This algorithm runs in time Õ(D(G) + L(G, w)) where L(G, w) is a parameter called the local shortest path diameter and D(G) is the (unweighted) diameter of the graph. Our algorithm is existentially optimal (up to polylogarithmic factors), i.e., there exists graphs which need Ω(D(G) + L(G, w)) time to compute an Happroximation to the MST for any H ∈ [1, Θ(log n)]. Our result also shows that there can be a significant time gap between exact and approximate MST computation: there exists graphs in which the running time of our approximation algorithm is exponentially faster than the timeoptimal distributed algorithm that computes the MST. Finally, we show that our algorithm can be used to find an approximate MST in wireless networks and in random weighted networks in almost optimal Õ(D(G)) time.
Secondary spectrum auctions for symmetric and submodular bidders
 In Proc. 13th Conf. Electronic Commerce (EC
, 2012
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Maximizing Capacity with Power Control under Physical Interference Model in Simplex Mode ⋆
"... Abstract. This paper addresses the join selection and power assignment of a largest set of given links which can communicate successfully at the same time under the physical interference model in the simplex mode. For the special setting in which all nodes have unlimited maximum transmission power, ..."
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Cited by 7 (1 self)
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Abstract. This paper addresses the join selection and power assignment of a largest set of given links which can communicate successfully at the same time under the physical interference model in the simplex mode. For the special setting in which all nodes have unlimited maximum transmission power, Kesselheim [8] developed an constant approximation algorithm. For the general setting in which all nodes have bounded maximum transmission power, the existence of constant approximation algorithm remains open. In this paper, we resolve this open problem by developing a constantapproximation algorithm for the general setting in which all nodes have bounded maximum transmission power. 1
Maximum Weighted Independent Set of Links under Physical Interference Model ⋆
"... Abstract. Interferenceaware scheduling for wireless communications is crucial to improve the network throughput. In this paper, we study the problem of Maximum Weighted Independent Set of Links (MWISL) under the physical interference model in wireless networks. Given a set of communication links di ..."
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Cited by 3 (2 self)
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Abstract. Interferenceaware scheduling for wireless communications is crucial to improve the network throughput. In this paper, we study the problem of Maximum Weighted Independent Set of Links (MWISL) under the physical interference model in wireless networks. Given a set of communication links distributed in a twodimensional Euclidean plane, assume each link is associated with a positive weight which represents the benefit of transmitting along the link, the objective is to seek an independent set of links subject to the physical interference constraints with maximum weighted sum. To the best of our knowledge, no algorithm for MWISL under physical interference model has been proposed. We focus on MWISL in the oblivious power assignment setting. 1
1The Case for Addressing the Ordering Effect in InterferenceLimited Wireless Scheduling
"... Abstract—Scheduling channel access for interference control is a basic building block of wireless networking. Despite much work in this area, the existing algorithms did not explicitly address the impact of link ordering (i.e., the order in which links are added to the schedule of a time slot) on re ..."
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Cited by 2 (1 self)
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Abstract—Scheduling channel access for interference control is a basic building block of wireless networking. Despite much work in this area, the existing algorithms did not explicitly address the impact of link ordering (i.e., the order in which links are added to the schedule of a time slot) on receiverside interference accumulation and thus on optimal scheduling. Towards understanding the importance of considering the ordering effect, we formulate the concept of interference budget, and, by modeling the scheduling problem as a knapsack problem, we propose the scheduling algorithm iOrder that maximizes the schedulability of future channel access when scheduling concurrent transmissions. When selecting concurrent transmitters for a time slot, more specifically, iOrder tries to maximize the additional interference that can be tolerated by all the receivers while satisfying the application requirement on link reliability. We analyze the approximation ratio of iOrder, and, through extensive simulation and testbedbased measurement, we observe that addressing the ordering effect can improve the performance of existing algorithms by a significant margin in the case of both backlogged and online traffic, for instance, improving the throughput and reducing the latency of the wellknown algorithm LQF by a factor up to 2 and 24 respectively. Thus our study demonstrates the importance of explicitly addressing the ordering effect in wireless scheduling, which opens up new avenues for future research and for optimizing wireless network performance. Index Terms—Interferenceoriented wireless scheduling; analysis; simulation; measurement I.
1Throughput Optimizing Localized Link Scheduling for Multihop Wireless Networks Under Physical Interference Model
"... Abstract—We study throughputoptimum localized link scheduling in wireless networks. The majority of results on link scheduling assume binary interference models that simplify interference constraints in actual wireless communication. While the physical interference model reflects the physical reali ..."
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Cited by 2 (0 self)
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Abstract—We study throughputoptimum localized link scheduling in wireless networks. The majority of results on link scheduling assume binary interference models that simplify interference constraints in actual wireless communication. While the physical interference model reflects the physical reality more precisely, the problem becomes notoriously harder under the physical interference model. There have been just a few existing results on link scheduling under the physical interference model, and even fewer on more practical distributed or localized scheduling. In this paper, we tackle the challenges of localized link scheduling posed by the complex physical interference constraints. By integrating the partition and shifting strategies into the pickandcompare scheme, we present a class of localized scheduling algorithms with provable throughput guarantee subject to physical interference constraints. The algorithm in the oblivious power setting is the first localized algorithm that achieves at least a constant fraction of the optimal capacity region subject to physical interference constraints. The algorithm in the uniform power setting is the first localized algorithm with a logarithmic approximation ratio to the optimal solution. Our extensive simulation results demonstrate performance efficiency of our algorithms. Index Terms—localized link scheduling, physical interference model, maximum weighted independent set of links (MWISL), capacity region. I.
Simple and Effective Scheduling in Wireless Networks under the Physical Interference Model
"... Abstract—In this paper, we study the problem of maximizing the number of concurrent requests and the problem of minimizing the number of timeslots needed to schedule all requests in wireless networks under the physical interference model. It has been proved that both problems are NPcomplete in [7] ..."
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Abstract—In this paper, we study the problem of maximizing the number of concurrent requests and the problem of minimizing the number of timeslots needed to schedule all requests in wireless networks under the physical interference model. It has been proved that both problems are NPcomplete in [7]. Thus either approximation algorithms with guaranteed approximation factors or effective heuristics with practically good performances are desirable. We focus on the latter and present simple and effective heuristic algorithms for these two problems. Extensive experiments show that our algorithm for the first problem outperforms the best approximation algorithm by 62 % − 72% on average, and our two algorithms for the second problem give the best results among existing algorithms. I.
Computing Capacity and Connectivity in Cognitive Radio AdHoc Networks
 INTERNATIONAL SYMPOSIUM ON PERVASIVE SYSTEMS, ALGORITHMS AND NETWORKS
, 2012
"... We present some unique challenges in cognitive radio adhoc networks (CRAHNs) that are not present in conventional singlechannel or multichannel wireless adhoc networks. We first briefly survey these challenges and their potential impact on the design of efficient algorithms for several fundamen ..."
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We present some unique challenges in cognitive radio adhoc networks (CRAHNs) that are not present in conventional singlechannel or multichannel wireless adhoc networks. We first briefly survey these challenges and their potential impact on the design of efficient algorithms for several fundamental problems in CRAHNs. Then, we describe our recent contributions to the capacity maximization problem [29] and the connectivity problem [32]. The capacity maximization problem is to maximize the overall throughput utility among multiple unicast sessions; the connectivity problem is to find a connected subgraph from the given cognitive radio network where each secondary node is equipped with multiple radios. By assuming the physical interference model and asynchronous communications, we reformulate the above two problems where the capacity maximization problem is to find the maximum number of simultaneously transmitting links in secondary networks, and the connectivity problem is to construct a spanning tree over secondary networks using the fewest timeslots. We discuss the challenging issues for designing distributed approximation algorithms and give a preliminary framework for solving these two problems.