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Understanding the capacity region of the greedy maximal scheduling algorithm in multihop wireless networks
 Proc. of IEEE INFOCOM
, 2008
"... Abstract—In this paper, we characterize the performance of an important class of scheduling schemes, called Greedy Maximal Scheduling (GMS), for multihop wireless networks. While a lower bound on the throughput performance of GMS is relatively wellknown in the simple nodeexclusive interference mo ..."
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Cited by 123 (9 self)
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Abstract—In this paper, we characterize the performance of an important class of scheduling schemes, called Greedy Maximal Scheduling (GMS), for multihop wireless networks. While a lower bound on the throughput performance of GMS is relatively wellknown in the simple nodeexclusive interference model, it has not been thoroughly explored in the more general Khop interference model. Moreover, empirical observations suggest that the known bounds are quite loose, and that the performance of GMS is often close to optimal. In this paper, we provide a number of new analytic results characterizing the performance limits of GMS. We first provide an equivalent characterization of the efficiency ratio of GMS through a topological property called the localpooling factor of the network graph. We then develop an iterative procedure to estimate the localpooling factor under a large class of network topologies and interference models. We use these results to study the worstcase efficiency ratio of GMS on two classes of network topologies. First, we show how these results can be applied to tree networks to prove that GMS achieves the full capacity region in tree networks under theKhop interference model. Second, we show that the worstcase efficiency ratio of GMS in geometric network graphs is between 1 6
Distributed link scheduling with constant overhead
 In Proceedings of ACM Sigmetrics
, 2007
"... This paper proposes a new class of simple, distributed algorithms for scheduling in wireless networks. The algorithms generate new schedules in a distributed manner via simple local changes to existing schedules. The class is parameterized by integers k ≥ 1. We show that algorithm k of our class ach ..."
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Cited by 100 (3 self)
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This paper proposes a new class of simple, distributed algorithms for scheduling in wireless networks. The algorithms generate new schedules in a distributed manner via simple local changes to existing schedules. The class is parameterized by integers k ≥ 1. We show that algorithm k of our class achieves k/(k +2) of the capacity region, for every k ≥ 1. The algorithms have small and constant worstcase overheads: in particular, algorithm k generates a new schedule using (a) time less than 4k + 2 roundtrip times between neighboring nodes in the network, and (b) at most three control transmissions by any given node, for any k. The control signals are explicitly specified, and face the same interference effects as normal data transmissions. Our class of distributed wireless scheduling algorithms are the first ones guaranteed to achieve any fixed fraction of the capacity region while using small and constant overheads that do not scale with network size. The parameter k explicitly captures the tradeoff between control overhead and scheduler throughput performance and provides a tuning knob protocol designers can use to harness this tradeoff in practice. 1.
Adaptive network coding and scheduling for maximizing througput in wireless networks
 In Proceedings of ACM Mobicom
, 2007
"... Recently, network coding emerged as a promising technology that can provide significant improvements in throughput and energy efficiency of wireless networks, even for unicast communication. Often, network coding schemes are designed as an autonomous layer, independent of the underlying Phy and ..."
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Cited by 64 (1 self)
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Recently, network coding emerged as a promising technology that can provide significant improvements in throughput and energy efficiency of wireless networks, even for unicast communication. Often, network coding schemes are designed as an autonomous layer, independent of the underlying Phy and MAC capabilities and algorithms. Consequently, these schemes are greedy, in the sense that all opportunities of broadcasting combinations of packets are exploited. We demonstrate that this greedy design principle may in fact reduce the network throughput. This begets the need for adaptive network coding schemes. We further show that designing appropriate MAC scheduling algorithms is critical for achieving the throughput gains expected from network coding. In this paper, we propose a general framework to develop optimal and adaptive joint network coding and scheduling schemes. Optimality is shown for various Phy and MAC constraints. We apply this framework to two different network coding architectures: COPE, a scheme recently proposed in [7], and XORSym, a new scheme we present here. XORSym is designed to achieve a lower implementation complexity than that of COPE, and yet to provide similar throughput gains.
Improved bounds on the throughput efficiency of greedy maximal scheduling in wireless networks
 in Proc. ACM MOBIHOC’09
, 2009
"... Due to its low complexity, Greedy Maximal Scheduling (GMS), also known as Longest Queue First (LQF), has been studied extensively for wireless networks. However, GMS can result in degraded throughput performance in general wireless networks. In this paper, we prove that GMS achieves 100 % throughput ..."
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Cited by 45 (8 self)
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Due to its low complexity, Greedy Maximal Scheduling (GMS), also known as Longest Queue First (LQF), has been studied extensively for wireless networks. However, GMS can result in degraded throughput performance in general wireless networks. In this paper, we prove that GMS achieves 100 % throughput in all networks with eight nodes or less, under the twohop interference model. Further, we obtain performance bounds that improve upon previous results for larger networks up to a certain size. We also provide a simple proof to show that GMS can be implemented using only local neighborhood information in networks of any size.
A distributed optimization algorithm for multihop cognitive radio networks
 IEEE INFOCOM
, 2008
"... Cognitive radio (CR) is a revolution in radio technology and is viewed as an enabling technology for dynamic spectrum access. This paper investigates how to design distributed algorithm for a multihop CR network, with the objective of maximizing data rates for a set of user communication sessions. ..."
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Cited by 39 (1 self)
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Cognitive radio (CR) is a revolution in radio technology and is viewed as an enabling technology for dynamic spectrum access. This paper investigates how to design distributed algorithm for a multihop CR network, with the objective of maximizing data rates for a set of user communication sessions. We study this problem via a crosslayer optimization approach, with joint consideration of power control, scheduling, and routing. For the centralized problem, we show that this optimization problem is in the form of mixed integer nonlinear program (MINLP), which cannot be solved in polynomial time. To develop a performance benchmark for the distributed optimization algorithm, we first develop a tight upper bound on the objective function via relaxation on the MINLP problem. Subsequently, we develop a distributed optimization algorithm that iteratively increases the data rate among user communication sessions. During each iteration, there are two separate processes, a Conservative Iterative Process (CIP) and an Aggressive Iterative Process (AIP). Both CIP and AIP incorporates routing, minimalist scheduling, and power control/scheduling modules. Via simulation results, we compare the performance of the distributed optimization algorithm with the upper bound and validate its efficacy.
Combinatorial network optimization with unknown variables: Multiarmed bandits with linear rewards and individual observations
 IEEE/ACM Transactions on Networking (TON
"... Abstract—We formulate the following combinatorial multiarmed bandit (MAB) problem: There are random variables with unknown mean that are each instantiated in an i.i.d. fashion over time. At each time multiple random variables can be selected, subject to an arbitrary constraint on weights associated ..."
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Cited by 37 (4 self)
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Abstract—We formulate the following combinatorial multiarmed bandit (MAB) problem: There are random variables with unknown mean that are each instantiated in an i.i.d. fashion over time. At each time multiple random variables can be selected, subject to an arbitrary constraint on weights associated with the selected variables. All of the selected individual random variables are observed at that time, and a linearly weighted combination of these selected variables is yielded as the reward. The goal is to find a policy that minimizes regret, defined as the difference between the reward obtained by a genie that knows the mean of each random variable, and that obtained by the given policy. This formulation is broadly applicable and useful for stochastic online versions of many interesting tasks in networks that can be formulated as tractable combinatorial optimization problems with linear objective functions, such as maximum weighted matching,
Arbitrary Throughput Versus Complexity Tradeoffs in Wireless Networks using Graph Partitioning
, 2007
"... Several policies have recently been proposed for attaining the maximum throughput region, or a guaranteed fraction thereof, through dynamic link scheduling. Among these policies, the ones that attain the maximum throughput region require a computation time which is linear in the network size, and t ..."
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Cited by 32 (8 self)
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Several policies have recently been proposed for attaining the maximum throughput region, or a guaranteed fraction thereof, through dynamic link scheduling. Among these policies, the ones that attain the maximum throughput region require a computation time which is linear in the network size, and the ones that require constant or logarithmic computation time attain only certain fractions of the maximum throughput region. In contrast, in this paper we propose policies that can attain any desirable fraction of the maximum throughput region using a computation time that is largely independent of the network size. First, using a combination of graph partitioning techniques and lyapunov arguments, we propose a simple policy for tree topologies under the primary interference model that requires each link to exchange only 1 bit information with its adjacent links and approximates the maximum throughput region using a computation time that depends only on the maximum degree of nodes and the approximation factor. Then we develop a framework for attaining arbitrary close approximations for the maximum throughput region in arbitrary networks, and use this framework to obtain any desired tradeoff between throughput guarantees and computation times for a large class of networks and interference models. Specifically, given any ɛ> 0, the maximum throughput region can be approximated in these networks within a factor of 1 − ɛ using a computation time that depends only on the maximum node degree and ɛ.
Multihop local pooling for distributed throughput maximization in wireless networks
 in IEEE INFOCOM
, 2008
"... Abstract—Efficient operation of wireless networks requires distributed routing and scheduling algorithms that take into account interference constraints. Recently, a few algorithms for networks with primary or secondaryinterference constraints have been developed. Due to their distributed operatio ..."
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Cited by 31 (5 self)
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Abstract—Efficient operation of wireless networks requires distributed routing and scheduling algorithms that take into account interference constraints. Recently, a few algorithms for networks with primary or secondaryinterference constraints have been developed. Due to their distributed operation, these algorithms can achieve only a guaranteed fraction of the maximum possible throughput. It was also recently shown that if a set of conditions (known as Local Pooling) is satisfied, simple distributed scheduling algorithms achieve 100 % throughput. However, previous work conditions and on networks with singlehop interference or singlehop traffic. In this paper, we identify several graph classes that satisfy the Local Pooling conditions, thereby enabling the use of such graphs in network design algorithms. Then, we study the multihop implications of Local Pooling. We show that in many cases, as the interference degree increases, the Local Pooling conditions are more likely to hold. Consequently, although increased interference reduces the maximum achievable throughput of the network, it tends to enable distributed algorithms to achieve 100 % of this throughput. Regarding multihop traffic, we show that if the network satisfies only the singlehop Local Pooling conditions, distributed joint routing and scheduling algorithms are not guaranteed to achieve maximum throughput. Therefore, we present new conditions for Multihop Local Pooling, under which distributed algorithms achieve 100 % throughout. Finally, we identify network topologies in which the conditions hold and discuss the algorithmic implications of the results.
Superimposed Code Based Channel Assignment in MultiRadio MultiChannel Wireless Mesh Networks
, 2007
"... Motivated by the observation that channel assignment for multiradio multichannel mesh networks should support both unicast and local broadcast 1, should be interferenceaware, and should result in low overall switching delay, high throughput, and low overhead, we propose two flexible localized chan ..."
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Cited by 24 (4 self)
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Motivated by the observation that channel assignment for multiradio multichannel mesh networks should support both unicast and local broadcast 1, should be interferenceaware, and should result in low overall switching delay, high throughput, and low overhead, we propose two flexible localized channel assignment algorithms based on sdisjunct superimposed codes. These algorithms support the local broadcast and unicast effectively, and achieve interferencefree channel assignment under certain conditions. In addition, under the primary interference constraints 2, the channel assignment algorithm for unicast can achieve 100 % throughput with a simple scheduling algorithm such as the maximal weight independent set scheduling, and can completely avoid hidden/exposed terminal problems under certain conditions. Our algorithms make no assumptions on the underlying network and therefore are applicable to a wide range of MRMC mesh network settings. We conduct extensive theoretical performance analysis to verify our design.
Performance Limits of Greedy Maximal Matching in Multihop Wireless Networks
"... In this paper, we characterize the performance limits of an important class of scheduling schemes, called Greedy Maximal Matching (GMM), for multihop wireless networks. For simplicity, we focus on the wellestablished nodeexclusive interference model, although many of the stated results can be rea ..."
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Cited by 17 (1 self)
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In this paper, we characterize the performance limits of an important class of scheduling schemes, called Greedy Maximal Matching (GMM), for multihop wireless networks. For simplicity, we focus on the wellestablished nodeexclusive interference model, although many of the stated results can be readily extended to more general interference models. The study of the performance of GMM is intriguing because although a lower bound on its performance is well known, empirical observations suggest that this bound is quite loose, and that the performance of GMM is often close to optimal. In fact, recent results have shown that GMM achieves optimal performance under certain conditions. In this paper, we provide new analytic results that characterize the performance of GMM through the topological properties of the underlying graphs. To that end, we generalize a recently developed topological notion called the local pooling condition to a far weaker condition called the σlocal pooling. We then define the localpooling factor on a graph, as the supremum of all σ such that the graph satisfies σlocal pooling. We show that for a given graph, the efficiency ratio of GMM (i.e., the ratio of the throughput of GMM to that of the optimal) is equal to its localpooling factor. Further, we provide results on how to estimate the localpooling factor for arbitrary graphs and show that the efficiency ratio of GMM is no smaller than d ∗ /(2d ∗ −1) in a network topology of maximum nodedegree d ∗. We also identify specific network topologies for which the efficiency ratio of GMM is strictly less than 1. I.