Optimal throughput-delay scaling in wireless networks - part I: the fluid model

Gamal, Abbas El; Mammen, J.; Prabhakar, Balaji; Shah, Devavrat

doi:10.1109/tit.2006.874379

Cited by 319 publications

(465 citation statements)

References 22 publications

(50 reference statements)

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“…There has been some recent interesting work on characterizing the delay performance of scheduling algorithms, or formulating scheduling and routing policies in random networks that attain order-optimal delay [6], [7], [20], [14], [16], [17]. Most of these works do not consider queuing delay, and focus on attaining order-optimal packet delivery delay in presence of node mobility.…”

Section: Related Workmentioning

confidence: 99%

Delay Guarantees for Throughput-Optimal Wireless Link Scheduling

Kar

Sarkar

Ghavami

et al. 2012

IEEE Trans. Automat. Contr.

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Abstract-We consider the question of obtaining tight delay guarantees for throughout-optimal link scheduling in arbitrary topology wireless ad-hoc networks. We consider two classes of scheduling policies: 1) a maximum queue-length weighted independent set scheduling policy, and 2) a randomized independent set scheduling policy where the independent set scheduling probabilities are selected optimally. Both policies stabilize all queues for any set of feasible packet arrival rates, and are therefore throughput-optimal. For these policies and i.i.d. packet arrivals, we show that the average packet delay is bounded by a constant that depends on the chromatic number of the interference graph, and the overall load on the network. We also prove that this upper bound is asymptotically tight in the sense that there exist classes of topologies where the expected delay attained by any scheduling policy is lower bounded by the same constant. Through simulations we examine the scaling of the average packet delay with respect to the overall load on the network, and the chromatic number of the link interference graph.

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Section: Related Workmentioning

confidence: 99%

Delay Guarantees for Throughput-Optimal Wireless Link Scheduling

Kar

Sarkar

Ghavami

et al. 2012

IEEE Trans. Automat. Contr.

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“…In [7], it was first shown that mobility improves capacity. Subsequently, communication delay has been studied under various node mobility models such as Markov [9], [10], random way point [11], and Brownian [12]. General observation has been that increasing node speed improves communication delay.…”

Section: A Related Workmentioning

confidence: 99%

“…Thus, the variable v is the maximum speed that vehicles in the network can possibly achieve. Random waypoint and Markov mobility are two examples of such motion models [9], [11], [16], [17].…”

Section: A Mobility Modelmentioning

confidence: 99%

Speed limits in autonomous vehicular networks due to communication constraints

Talak

Karaman

Modiano

2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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Abstract-Increasing applications and decreasing sizes of autonomous vehicles is likely to result in a dense network of heterogeneous autonomous vehicles, each moving around to perform a separate task. Autonomous vehicles need to be aware of other vehicles in it's vicinity in order to successfully perform their tasks. Such network awareness is ensured by exchanging location and control information over wireless radio channels. However, wireless interference constraints limit the number of messages that can be exchanged between the vehicles. In this paper, we study the impact of such communication constraints on maximum speed in dense autonomous vehicular networks. We define hazard rate to be the fraction of times a vehicle enters a region, call it 'uncertainty region', where there is a positive chance of other vehicles being present. We show that such a performance measure follows a threshold behavior with respect to maximum speed v as the network density n increases to infinity. We show that, for a planar network, the hazard rate tends to 1, if the maximum speed v decreases slower than n −3/2 , and tends to 0, if v decreases faster than n −3/2 . For the network hazard rate, which is fraction of times any vehicle enters it's uncertainty region, the threshold is n −2 . For the spatial network, however, these thresholds turn out to be larger. This implies that it is better to plan autonomous vehicular networks, such as UAV networks, over a three dimensional space rather than a two dimensional one.

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“…In Part I of this two-part paper, capacity scaling laws for underwater networks are analyzed in an extended network [5,4,10,25,26] of unit node density, which is one of fundamentally different network models. Part II [27] shows the analysis for a dense network [1,6,10] of unit area used as another extreme network realization. 4 Unlike the work in [23], the information-theoretic notion of network capacity is adopted in terms of characterizing the model for successful transmission.…”

Section: (Ii) F (X) = O (G(x)) Means That Lim X→∞ F (X)mentioning

confidence: 99%

“…They showed that the total throughput scales as Θ √ n/ log n when a multi-hop (MH) routing strategy is used for n source-destination (S-D) pairs randomly distributed in a unit area. 1 MH schemes are then further developed and analyzed in [3][4][5][6][7][8][9], while their throughput per S-D pair scales far slower than Θ (1). Recent results [10,11] have shown that an almost linear throughput in the radio network, i.e.…”

Section: Introductionmentioning

confidence: 99%

On the Effects of Frequency Scaling Over Capacity Scaling in Underwater Networks—Part I: Extended Network Model

ShinWon-Yong¹,

LucaniDaniel²,

MédardMuriel³

et al. 2012

Wireless Pers Commun

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In this two-part paper, information-theoretic capacity scaling laws are analyzed in an underwater acoustic network with n regularly located nodes on a square, in which both bandwidth and received signal power can be limited significantly. Parts I and II deal with an extended network of unit node density and a dense network of unit area, respectively. In both cases, a narrow-band model is assumed where the carrier frequency is allowed to scale as a function of n, which is shown to be crucial for achieving the order optimality in multi-hop (MH) mechanisms. We first characterize an attenuation parameter that depends on the frequency scaling as well as the transmission distance. Upper and lower bounds on the capacity scaling are then derived. In Part I, we show that the upper bound on capacity for extended networks is inversely proportional to the attenuation parameter, thus resulting in a highly power-limited network. Interestingly, it is shown that the upper bound is intrinsically related to the attenuation parameter but not the spreading factor. propose an achievable communication scheme based on the nearest-neighbor MH transmission, which is suitable due to the low propagation speed of acoustic channel, and show that it is order-optimal for all operating regimes of extended networks. Finally, these scaling results are extended to the case of random node deployments providing fundamental limits to more complex scenarios of extended underwater networks.

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Optimal throughput-delay scaling in wireless networks - part I: the fluid model

Cited by 319 publications

References 22 publications

Delay Guarantees for Throughput-Optimal Wireless Link Scheduling

Delay Guarantees for Throughput-Optimal Wireless Link Scheduling

Speed limits in autonomous vehicular networks due to communication constraints

On the Effects of Frequency Scaling Over Capacity Scaling in Underwater Networks—Part I: Extended Network Model

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