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120
A tutorial on crosslayer optimization in wireless networks
 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
, 2006
"... This tutorial paper overviews recent developments in optimization based approaches for resource allocation problems in wireless systems. We begin by overviewing important results in the area of opportunistic (channelaware) scheduling for cellular (singlehop) networks, where easily implementable my ..."
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Cited by 248 (29 self)
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This tutorial paper overviews recent developments in optimization based approaches for resource allocation problems in wireless systems. We begin by overviewing important results in the area of opportunistic (channelaware) scheduling for cellular (singlehop) networks, where easily implementable myopic policies are shown to optimize system performance. We then describe key lessons learned and the main obstacles in extending the work to general resource allocation problems for multihop wireless networks. Towards this end, we show that a cleanslate optimization based approach to the multihop resource allocation problem naturally results in a “loosely coupled” crosslayer solution. That is, the algorithms obtained map to different layers (transport, network, and MAC/PHY) of the protocol stack are coupled through a limited amount of information being passed back and forth. It turns out that the optimal scheduling component at the MAC layer is very complex and thus needs simpler (potentially imperfect) distributed solutions. We demonstrate how to use imperfect scheduling in the crosslayer framework and describe recently developed distributed algorithms along these lines. We conclude by describing a set of open research problems.
Crosslayer congestion control, routing and scheduling design in ad hoc wireless networks
 PROC. IEEE INFOCOM
, 2006
"... This paper considers jointly optimal design of crosslayer congestion control, routing and scheduling for ad hoc wireless networks. We first formulate the rate constraint and scheduling constraint using multicommodity flow variables, and formulate resource allocation in networks with fixed wireless ..."
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Cited by 151 (10 self)
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This paper considers jointly optimal design of crosslayer congestion control, routing and scheduling for ad hoc wireless networks. We first formulate the rate constraint and scheduling constraint using multicommodity flow variables, and formulate resource allocation in networks with fixed wireless channels (or singlerate wireless devices that can mask channel variations) as a utility maximization problem with these constraints. By dual decomposition, the resource allocation problem naturally decomposes into three subproblems: congestion control, routing and scheduling that interact through congestion price. The global convergence property of this algorithm is proved. We next extend the dual algorithm to handle networks with timevarying channels and adaptive multirate devices. The stability of the resulting system is established, and its performance is characterized with respect to an ideal reference system which has the best feasible rate region at link layer. We then generalize the aforementioned results to a general model of queueing network served by a set of interdependent parallel servers with timevarying service capabilities, which models many design problems in communication networks. We show that for a general convex optimization problem where a subset of variables lie in a polytope and the rest in a convex set, the dualbased algorithm remains stable and optimal when the constraint set is modulated by an irreducible finitestate Markov chain. This paper thus presents a step toward a systematic way to carry out crosslayer design in the framework of “layering as optimization decomposition ” for timevarying channel models.
Joint congestion control, routing and MAC for stability and fairness in wireless networks
 IEEE Journal on Selected Areas in Communications
, 2006
"... In this work, we describe and analyze a joint scheduling, routing and congestion control mechanism for wireless networks, that asymptotically guarantees stability of the buffers and fair allocation of the network resources. The queue lengths serve as common information to different layers of the ne ..."
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Cited by 126 (23 self)
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In this work, we describe and analyze a joint scheduling, routing and congestion control mechanism for wireless networks, that asymptotically guarantees stability of the buffers and fair allocation of the network resources. The queue lengths serve as common information to different layers of the network protocol stack. Our main contribution is to prove the asymptotic optimality of a primaldual congestion controller, which is known to model different versions of TCP well.
Efficient InterferenceAware TDMA Link Scheduling for Static Wireless Networks
 In ACM MobiCom
, 2006
"... We study efficient link scheduling for a multihop wireless network to maximize its throughput. Efficient link scheduling can greatly reduce the interference effect of closeby transmissions. Unlike the previous studies that often assume a unit disk graph model, we assume that different terminals cou ..."
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Cited by 85 (12 self)
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We study efficient link scheduling for a multihop wireless network to maximize its throughput. Efficient link scheduling can greatly reduce the interference effect of closeby transmissions. Unlike the previous studies that often assume a unit disk graph model, we assume that different terminals could have different transmission ranges and different interference ranges. In our model, it is also possible that a communication link may not exist due to barriers or is not used by a predetermined routing protocol, while the transmission of a node always result interference to all nonintended receivers within its interference range. Using a mathematical formulation, we develop synchronized TDMA link schedulings that optimize the networking throughput. Specifically, by assuming known link capacities and link traffic loads, we study link scheduling under the RTS/CTS interference model and the protocol interference model with fixed transmission power. For both models, we present both efficient centralized and distributed algorithms that use time slots within a constant factor of the optimum. We also present efficient distributed algorithms whose performances are still comparable with optimum, but with much less communications. Our theoretical results are corroborated by extensive simulation studies.
Joint asynchronous congestion control and distributed scheduling for multihop wireless networks
 in the Proceedings IEEE Infocom
"... Abstract — We consider a multihop wireless network shared by many users. For an interference model that only constrains a node to either transmit or receive at a time, but not both, we propose an architecture for fair resource allocation that consists of a distributed scheduling algorithm operating ..."
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Cited by 60 (16 self)
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Abstract — We consider a multihop wireless network shared by many users. For an interference model that only constrains a node to either transmit or receive at a time, but not both, we propose an architecture for fair resource allocation that consists of a distributed scheduling algorithm operating in conjunction with an asynchronous congestion control algorithm. We show that the proposed joint congestion control and scheduling algorithm supports at least onethird of the throughput supportable by any other algorithm, including centralized algorithms. I.
Alternative Distributed Algorithms for Network Utility Maximization: Framework and Applications
 IEEE Transactions on Automatic Control
, 2007
"... Abstract—Network utility maximization (NUM) problem formulations provide an important approach to conduct network resource allocation and to view layering as optimization decomposition. In the existing literature, distributed implementations are typically achieved by means of the socalled dual deco ..."
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Cited by 59 (7 self)
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Abstract—Network utility maximization (NUM) problem formulations provide an important approach to conduct network resource allocation and to view layering as optimization decomposition. In the existing literature, distributed implementations are typically achieved by means of the socalled dual decomposition technique. However, the span of decomposition possibilities includes many other elements that, thus far, have not been fully exploited, such as the use of the primal decomposition technique, the versatile introduction of auxiliary variables, and the potential of multilevel decompositions. This paper presents a systematic framework to exploit alternative decomposition structures as a way to obtain different distributed algorithms, each with a different tradeoff among convergence speed, message passing amount and asymmetry, and distributed computation architecture. Several specific applications are considered to illustrate the proposed framework, including resourceconstrained and directcontrol rate allocation, and rate allocation among QoS classes with multipath routing. For each of these applications, the associated generalized NUM formulation is first presented, followed by the development of novel alternative decompositions and numerical experiments on the resulting new distributed algorithms. A systematic enumeration and comparison of alternative vertical decompositions in the future will help complete a mathematical theory of network architectures. Index Terms—Congestion control, distributed algorithm, mathematical programming/optimization, network control by pricing, network utility maximization (NUM), rate control, resource allocation.
What is the right model for wireless channel interference
 in Proc. QShine
, 2006
"... Abstract—In wireless communications, the desired wireless signal is typically decoded by treating the sum of all the other ongoing signal transmissions as noise. In the networking literature, this phenomenon is typically abstracted using a wireless channel interference model. The level of detail in ..."
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Cited by 59 (16 self)
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Abstract—In wireless communications, the desired wireless signal is typically decoded by treating the sum of all the other ongoing signal transmissions as noise. In the networking literature, this phenomenon is typically abstracted using a wireless channel interference model. The level of detail in the interference model, evidently determines the accuracy of the results based upon the model. Several works in the networking literature have made use of simplistic interference models, e.g., fixed ranges for communication and interference, the capture threshold model (used in the ns2 network simulator), the protocol model, and so on. At the same time, fairly complex interference models such as those based on the SINR (signaltointerferenceandnoise ratio) have also been proposed and used. We investigate the impact of the choice of the interference model, on the conclusions that can be drawn regarding the performance of wireless networks, by comparing different wireless interference models. We find that both in the case of random access networks, as well as in the case of scheduled networks (where node transmissions are scheduled to be completely conflictfree), different interference models can produce significantly different results. Therefore, a lot of caution should be exercised before accepting or interpreting results based on simplified interference models. Further, we feel that an SINRbased model is the minimum level of detail that should be employed to model wireless channel interference in a networking context. Index Terms—Channel Modeling, interference, SINR, conflict graphs, conflict sets.
Utilityoptimal randomaccess control
 IEEE Trans. on Wireless Communications
, 2007
"... Abstract — This paper designs medium access control (MAC) protocols for wireless networks through the network utility maximization (NUM) framework. A networkwide utility maximization problem is formulated, using a collision/persistenceprobabilistic model and aligning selfish utility with total soci ..."
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Cited by 53 (10 self)
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Abstract — This paper designs medium access control (MAC) protocols for wireless networks through the network utility maximization (NUM) framework. A networkwide utility maximization problem is formulated, using a collision/persistenceprobabilistic model and aligning selfish utility with total social welfare. By adjusting the parameters in the utility objective functions of the NUM problem, we can also control the tradeoff between efficiency and fairness of radio resource allocation. We develop two distributed algorithms to solve the utilityoptimal randomaccess control problem, which lead to random access protocols that have slightly more message passing overhead than the current exponentialbackoff protocols, but significant potential for efficiency and fairness improvement. We provide readilyverifiable sufficient conditions under which convergence of the proposed algorithms to a global optimality of network utility can be guaranteed, and numerical experiments that illustrate the value of the NUM approach to the complexityperformance tradeoff in MAC design. Index Terms — Wireless network, medium access control (MAC), mathematical programming/optimization, network utility maximization, network control by pricing.
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 44 (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.
Calderbank, “Jointly optimal congestion and contention control in wireless ad hoc networks
 IEEE Communication Letters
, 2006
"... Abstract — We study joint endtoend congestion control and perlink medium access control (MAC) in adhoc networks. We use a network utility maximization formulation, in which by adjusting the types of utility functions, we can accommodate multiclass services as well as exploit the tradeoff between ..."
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Cited by 40 (11 self)
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Abstract — We study joint endtoend congestion control and perlink medium access control (MAC) in adhoc networks. We use a network utility maximization formulation, in which by adjusting the types of utility functions, we can accommodate multiclass services as well as exploit the tradeoff between efficiency and fairness of resource allocation. Despite the inherent difficulties of nonconvexity and nonseparability of the optimization problem, we show that, with readilyverifiable sufficient conditions, we can develop a distributed algorithm that converges to the globally and jointly optimal rate allocation and persistence probabilities. Index Terms — Congestion control, medium access control, adhoc wireless network, network utility maximization, optimization. I. INTRODUCTION AND RELATED WORK SINCE the publication of the seminal paper [1], the network utility maximization (NUM) framework has found