Exploiting wireless channel state information for throughput maximization

Tsibonis, V.; Georgiadis, Leonidas

doi:10.1109/infcom.2003.1208682

Cited by 40 publications

(22 citation statements)

References 11 publications

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“…Probabilistic analysis for this problem was given by Tsibonis et al [6] and Borst [3]. Arora and Choi were the first ones to study this problem from a worst-case perspective [1].…”

Section: 667mentioning

confidence: 99%

An Improved Algorithm for Online Rectangle Filling

Stee

2011

Approximation and Online Algorithms

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We consider the problem of scheduling resource allocation where a change in allocation results in a changeover penalty of one time slot. We assume that we are sending packets over a wireless channel of uncertain and varying capacity. In each time slot, a bandwidth of at most the current capacity can be allocated, but changing the capacity has a cost, which is modeled as an empty time slot. Only the current bandwidth and the bandwidth of the immediately following slot are known. We give an online algorithm with competitive ratio 1.753 for this problem, improving over the previous upper bound of 1.848. The main new idea of our algorithm is that it attempts to avoid cases where a single time slot with a nonzero allocation is immediately followed by an empty time slot. Additionally, we improve the lower bound for this problem to 1.6959, and give a better randomized lower bound. Our results significantly narrow the gap between the best known upper and lower bound.

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“…Probabilistic analysis for this problem was given by Tsibonis et al [6] and Borst [3]. Arora and Choi were the first ones to study this problem from a worst-case perspective [1].…”

Section: 667mentioning

confidence: 99%

An Improved Algorithm for Online Rectangle Filling

Stee

2011

Approximation and Online Algorithms

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“…Channel-aware or opportunistic scheduling strategies offer an effective mechanism for improving throughput performance by exploiting such rate variations (Knopp & Humblet 1995), and these have been intensively examined at the packet level for a static user configuration (e.g. Borst & Whiting 2001, 2003Agrawal & Subramanian 2002;Neely et al 2002Neely et al , 2003Neely et al , 2005Viswanath et al 2002;Liu et al 2003;Tsibonis et al 2003;Andrews et al 2004Andrews et al , 2005Bonald 2004;Kushner & Whiting 2004;Stolyar 2004Stolyar , 2005aEryilmaz & Srikant 2005;Lin & Shroff 2005). We refer to the excellent survey by Georgiadis et al (2006) for a comprehensive overview and additional references.…”

Section: Introductionmentioning

confidence: 99%

Flow-level performance and user mobility in wireless data networks

Borst¹

2008

Phil. Trans. R. Soc. A.

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Channel conditions in wireless networks exhibit huge variations across space and time, giving rise to vast fluctuations in the transmission rates. Channel-aware scheduling strategies provide an effective mechanism for improving throughput performance by exploiting such rate variations, and these have been extensively examined at the packet level for a static user configuration. In this paper, we discuss the performance implications at the flow level for a dynamic user population, taking into account variations on a slower time scale and wide-range user mobility as well. First of all, we present simple necessary conditions for flow-level stability and prove that these are in fact (near) sufficient for a wide family of utility-based scheduling strategies. It is further shown how the flow-level performance of the proportional fair scheduling strategy may be evaluated by means of a processor-sharing model with a state-dependent service rate. In addition, we examine the impact of variations on a slower time scale, and establish that the so-called fluid and quasistationary regimes yield explicit, insensitive performance bounds. Finally, we turn our attention to a network of several base stations (BSs) with handoffs of active sessions governed by wide-range user mobility. It is demonstrated that mobility increases the capacity, not only in the case of globally optimal scheduling but also when each of the BSs adheres to a local fair-sharing discipline.

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“…Fast adaptation increases the number of mode-change messages transmitted over the channel, consuming bandwidth, and time resources [1]. While many aspects of scheduling transmissions over time-varying wireless channels have been studied (see, for example, [2][3][4] and the references therein), the penalty induced by LA has not been considered. Moreover, predicting the future channel quality may also consume significant amount of system resources (e.g., time, bandwidth and power), since it may involve transmission of training-sequences, pilot tones, or feedback messages carrying the CSI [1].…”

Section: Introductionmentioning

confidence: 99%

Optimized Channel Utilization in Multi-carrier Wireless Mobile Networks

Arora

Jin

Choi

2006

Wireless Algorithms, Systems, and Applications

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Abstract. This paper considers the problem of maximizing throughput in a multi-carrier wireless network that employs predictive link adaptation. The contributions of this paper are the following. We explicitly consider the time-penalty induced by the transmission-mode changes in wireless networks. We study the trade-offs between the channel-state prediction quality and the throughput. We provide sound offline problem analysis, with optimal or approximate solutions to the extent possible. We consider online version of the wireless channel scheduling problem, and provide a lower bound and propose algorithms that guarantee competitive ratios. We also extend our algorithms to accommodate the model when the actual channel capacity is different from as predicted earlier. Our results show that a modest consumption of resources for channel prediction and link adaptation may result in a significant throughput improvement, with only marginal gains through further enhancement of the prediction quality.

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Exploiting wireless channel state information for throughput maximization

Cited by 40 publications

References 11 publications

An Improved Algorithm for Online Rectangle Filling

An Improved Algorithm for Online Rectangle Filling

Flow-level performance and user mobility in wireless data networks

Optimized Channel Utilization in Multi-carrier Wireless Mobile Networks

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