Opportunistic power scheduling for dynamic multi-server wireless systems

Lee, Jang-Won; Mazumdar, Ravi R.; Shroff, Ness B.

doi:10.1109/twc.2006.1638671

Cited by 98 publications

(98 citation statements)

References 12 publications

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“…This extends the Lyapunov stability results of [14]- [21], and is closely related to stochastic gradient optimization (see, for example, [22] for an application to data networks). To demonstrate the technique, consider a system with a vector process Z(t) representing a set of queue states that evolve according to some probability law.…”

Section: A Performance Optimal Lyapunov Schedulingsupporting

confidence: 67%

Intelligent Packet Dropping for Optimal Energy-Delay Tradeoffs in Wireless Downlinks

Neely

2009

IEEE Trans. Automat. Contr.

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Abstract-We explore the advantages of intelligently dropping a small fraction of packets that arrive for transmission over a time varying wireless downlink. Without packet dropping, the optimal energy-delay tradeoff conforms to a square root tradeoff law, as shown by Berry and Gallager (2002). We show that intelligently dropping any non-zero fraction of the input rate dramatically changes this relation from a square root tradeoff law to a logarithmic tradeoff law. Further, we demonstrate an innovative algorithm for achieving this logarithmic tradeoff without requiring a-priori knowledge of arrival rates or channel probabilities. The algorithm can be implemented in real time and easily extends to yield similar performance for multi-user systems.

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Section: A Performance Optimal Lyapunov Schedulingsupporting

confidence: 67%

Intelligent Packet Dropping for Optimal Energy-Delay Tradeoffs in Wireless Downlinks

Neely

2009

IEEE Trans. Automat. Contr.

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“…However, the channels' bandwidths are assumed to be fixed, i.e., the random behaviors of primary users are still neglected. Our work is partially inspired by Lee et al [24]. However, our paper differs from theirs in three crucial aspects.…”

Section: Related Workmentioning

confidence: 99%

“…First, by targeting the stochastic traffic engineering problem, our model differs from the joint power scheduling and rate control work in [25]. Second, in [24], [25], Lee et al only consider a single-path scenario while our work extends to a multipath routing network where the network traffic can be steered. Third and most importantly, Lee et al [24], [25] require that the current system state is fully observable at the decision maker.…”

Section: Related Workmentioning

confidence: 99%

“…Second, in [24], [25], Lee et al only consider a single-path scenario while our work extends to a multipath routing network where the network traffic can be steered. Third and most importantly, Lee et al [24], [25] require that the current system state is fully observable at the decision maker. To achieve this, the authors assume a centralized mechanism which knows all the channel states of all the links over the network.…”

Section: Related Workmentioning

confidence: 99%

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Stochastic Traffic Engineering in Multihop Cognitive Wireless Mesh Networks

Song

Zhang

Fang

2010

IEEE Trans. on Mobile Comput.

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Abstract-In this work, the stochastic traffic engineering problem in multihop cognitive wireless mesh networks is addressed. The challenges induced by the random behaviors of the primary users are investigated in a stochastic network utility maximization framework. For the convex stochastic traffic engineering problem, we propose a fully distributed algorithmic solution which provably converges to the global optimum with probability one. We next extend our framework to the cognitive wireless mesh networks with nonconvex utility functions, where a decentralized algorithmic solution, based on learning automata techniques, is proposed. We show that the decentralized solution converges to the global optimum solution asymptotically.

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“…For example, [3][5] [6][2] [7] focus on throughput/utility maximization with energy constraints in wireless networks. In particular, these works share one assumption that, when making power/rate allocation decisions, current channel states are always known with negligible cost.…”

Section: Introductionmentioning

confidence: 99%

Energy-optimal scheduling with dynamic channel acquisition in wireless downlinks

Neely

2007

2007 46th IEEE Conference on Decision and Control

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Abstract-We consider a wireless base station serving L users through L time-varying channels. It is well known that opportunistic scheduling algorithms with full channel state information (CSI) can stabilize the system and achieve the full capacity region. However, opportunistic scheduling algorithms with full CSI may not be energy efficient when the cost of channel acquisition is high and traffic rates are low. In particular, under the low traffic rate regime, it may be sufficient and more energy-efficient to transmit data with no CSI, i.e., to transmit data blindly, since no power for channel acquisition is consumed. In general, we show that purely channel-aware or purely channel-blind strategies are not necessarily optimal, and we must consider mixed strategies. We derive a unified scheduling algorithm that dynamically chooses to transmit data with full or no CSI based on queue backlog and channel statistics. Through Lyapunov analysis, we show that the unified algorithm can stabilize the downlink with optimal power consumption.

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Opportunistic power scheduling for dynamic multi-server wireless systems

Cited by 98 publications

References 12 publications

Intelligent Packet Dropping for Optimal Energy-Delay Tradeoffs in Wireless Downlinks

Intelligent Packet Dropping for Optimal Energy-Delay Tradeoffs in Wireless Downlinks

Stochastic Traffic Engineering in Multihop Cognitive Wireless Mesh Networks

Energy-optimal scheduling with dynamic channel acquisition in wireless downlinks

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