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345
The impact of imperfect scheduling on crosslayer congestion control in wireless networks
, 2005
"... In this paper, we study crosslayer design for congestion control in multihop wireless networks. In previous work, we have developed an optimal crosslayer congestion control scheme that jointly computes both the rate allocation and the stabilizing schedule that controls the resources at the under ..."
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Cited by 350 (33 self)
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In this paper, we study crosslayer design for congestion control in multihop wireless networks. In previous work, we have developed an optimal crosslayer congestion control scheme that jointly computes both the rate allocation and the stabilizing schedule that controls the resources at the underlying layers. However, the scheduling component in this optimal crosslayer congestion control scheme has to solve a complex global optimization problem at each time, and is hence too computationally expensive for online implementation. In this paper, we study how the performance of crosslayer congestion control will be impacted if the network can only use an imperfect (and potentially distributed) scheduling component that is easier to implement. We study both the case when the number of users in the system is fixed and the case with dynamic arrivals and departures of the users, and we establish performance bounds of crosslayer congestion control with imperfect scheduling. Compared with a layered approach that does not design congestion control and scheduling together, our crosslayer approach has provably better performance bounds, and substantially outperforms the layered approach. The insights drawn from our analyses also enable us to design a fully distributed crosslayer congestion control and scheduling algorithm for a restrictive interference model.
Fairness and optimal stochastic control for heterogeneous networks
 Proc. IEEE INFOCOM, March 2005. TRANSACTIONS ON NETWORKING, VOL
, 2008
"... Abstract — We consider optimal control for general networks with both wireless and wireline components and time varying channels. A dynamic strategy is developed to support all traffic whenever possible, and to make optimally fair decisions about which data to serve when inputs exceed network capaci ..."
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Cited by 266 (64 self)
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Abstract — We consider optimal control for general networks with both wireless and wireline components and time varying channels. A dynamic strategy is developed to support all traffic whenever possible, and to make optimally fair decisions about which data to serve when inputs exceed network capacity. The strategy is decoupled into separate algorithms for flow control, routing, and resource allocation, and allows each user to make decisions independent of the actions of others. The combined strategy is shown to yield data rates that are arbitrarily close to the optimal operating point achieved when all network controllers are coordinated and have perfect knowledge of future events. The cost of approaching this fair operating point is an endtoend delay increase for data that is served by the network.
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 (30 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.
Fair Resource Allocation in Wireless Networks using Queuelengthbased Scheduling and Congestion Control
"... We consider the problem of allocating resources (time slots, frequency, power, etc.) at a base station to many competing flows, where each flow is intended for a different receiver. The channel conditions may be timevarying and different for different receivers. It is wellknown that appropriate ..."
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Cited by 200 (49 self)
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We consider the problem of allocating resources (time slots, frequency, power, etc.) at a base station to many competing flows, where each flow is intended for a different receiver. The channel conditions may be timevarying and different for different receivers. It is wellknown that appropriately chosen queuelength based policies are throughputoptimal while other policies based on the estimation of channel statistics can be used to allocate resources fairly (such as proportional fairness) among competing users. In this paper, we show that a combination of queuelengthbased scheduling at the base station and congestion control implemented either at the base station or at the end users can lead to fair resource allocation and queuelength stability.
Capacity and delay tradeoffs for adhoc mobile networks
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 2005
"... We consider the throughput/delay tradeoffs for scheduling data transmissions in a mobile adhoc network. To reduce delays in the network, each user sends redundant packets along multiple paths to the destination. Assuming the network has a cell partitioned structure and users move according to a s ..."
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Cited by 192 (12 self)
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We consider the throughput/delay tradeoffs for scheduling data transmissions in a mobile adhoc network. To reduce delays in the network, each user sends redundant packets along multiple paths to the destination. Assuming the network has a cell partitioned structure and users move according to a simplified independent and identically distributed (i.i.d.) mobility model, we compute the exact network capacity and the exact endtoend queueing delay when no redundancy is used. The capacity achieving algorithm is a modified version of the GrossglauserTse 2hop relay algorithm and provides O(N) delay (where N is the number of users). We then show that redundancy cannot increase capacity, but can significantly improve delay. The following necessary tradeoff is established: delay/rate ≥ O(N). Two protocols that use redundancy and operate near the boundary of this curve are developed, with delays of O(√ N) and O(log(N)), respectively. Networks with noni.i.d. mobility are also considered and shown through simulation to closely match the performance of i.i.d. systems in the O(√ N) delay regime.
Energy optimal control for time varying wireless networks
 IEEE Trans. Inform. Theory
, 2006
"... Abstract — We develop a dynamic control strategy for minimizing energy expenditure in a time varying wireless network with adaptive transmission rates. The algorithm operates without knowledge of traffic rates or channel statistics, and yields average power that is arbitrarily close to the minimum p ..."
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Cited by 180 (51 self)
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Abstract — We develop a dynamic control strategy for minimizing energy expenditure in a time varying wireless network with adaptive transmission rates. The algorithm operates without knowledge of traffic rates or channel statistics, and yields average power that is arbitrarily close to the minimum possible value achieved by an algorithm optimized with complete knowledge of future events. Proximity to this optimal solution is shown to be inversely proportional to network delay. We then present a similar algorithm that solves the related problem of maximizing network throughput subject to peak and average power constraints. The techniques used in this paper are novel and establish a foundation for stochastic network optimization.
Joint rate control and scheduling in multihop wireless networks
 in Proceedings of IEEE Conference on Decision and Control
, 2004
"... Abstract — We study the joint problem of allocating data rates and finding a stabilizing scheduling policy in a multihop wireless network. We propose a dual optimization based approach through which the rate control problem and the scheduling problem can be decomposed. We demonstrate via both analyt ..."
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Cited by 161 (16 self)
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Abstract — We study the joint problem of allocating data rates and finding a stabilizing scheduling policy in a multihop wireless network. We propose a dual optimization based approach through which the rate control problem and the scheduling problem can be decomposed. We demonstrate via both analytical and numerical results that the proposed mechanism can fully utilize the capacity of the network, maintain fairness, and improve the quality of service to the users. I.
Crosslayer congestion control, routing and scheduling design in ad hoc wireless networks
 Proc. IEEE Infocom
, 2006
"... Abstract — 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 ..."
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Cited by 149 (10 self)
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Abstract — 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. I.
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 123 (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.
Rate performance objectives of multihop wireless networks
 IEEE TRANS. MOB. COMPUT
, 2004
"... We consider the question of what performance metric to maximize when designing ad hoc wireless network protocols such as routing or MAC. We focus on maximizing rates under batterylifetime and power constraints. Commonly used metrics are total capacity (in the case of cellular networks) and transpo ..."
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Cited by 86 (1 self)
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We consider the question of what performance metric to maximize when designing ad hoc wireless network protocols such as routing or MAC. We focus on maximizing rates under batterylifetime and power constraints. Commonly used metrics are total capacity (in the case of cellular networks) and transport capacity (in the case of ad hoc networks). However, it is known in traditional wired networking that maximizing total capacity conflicts with fairness, and this is why fairnessoriented rate allocations, such as maxmin fairness, are often used. We review this issue for wireless ad hoc networks. Indeed, the mathematical model for wireless networks has a specificity that makes some of the findings different. It has been reported in the literature on Ultra Wide Band that gross unfairness occurs when maximizing total capacity or transport capacity, and we confirm by a theoretical analysis that this is a fundamental shortcoming of these metrics in wireless ad hoc networks, as it is for wired networks. The story is different for maxmin fairness. Although it is perfectly viable for a wired network, it is much less so in our setting. We show that, in the limit of long battery lifetimes, the maxmin allocation of rates always leads to strictly equal rates, regardless of the MAC layer, network topology, channel variations, or choice of routes and power constraints. This is due to the “solidarity” property of the set of feasible rates. This results in all flows receiving the rate of the worst flow, and leads to severe inefficiency. We show numerically that the problem persists when batterylifetime constraints are finite. This generalizes the observation reported in the literature that, in heterogeneous settings, 802.11 allocates the worst rate to all stations, and shows that this is inherent to any protocol that implements maxmin fairness. Utility fairness is an alternative to maxmin fairness, which approximates rate allocation performed by TCP in the Internet. We analyze by numerical