2015
DOI: 10.1049/iet-net.2014.0026
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Delay‐sensitive user scheduling and power control in heterogeneous networks

Abstract: Delay-sensitive traffic services, such as live streaming video, voice over IP and multimedia teleconferencing, requires low end-to-end delay in order to maintain its interactive and streaming nature. In recent years, the popularity of delay-sensitive applications has been rapidly growing. In this study, the authors consider the delay-sensitive user scheduling and power control design for heterogeneous networks (HetNets). They model the problem as an infinite-horizon average cost Markov decision process MDP. Ba… Show more

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Cited by 7 publications
(3 citation statements)
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“…e delay of this model mainly considers the queuing delay, processing delay, and link transmission delay. We take the i-th SFC as an example; Q i (t) represents its queue length in time slot t; P i (t) represents the size of the data packet, and it is assumed that the size of the data packet obeys the exponential distribution of parameter P; and A i (t) represents the data packet arrival process of the i-th SFC, and it is assumed that the arrival of data packets obeys the Poisson distribution with a parameter of λ i [48]. e update process of the queue is expressed as follows:…”
Section: Delaymentioning
confidence: 99%
“…e delay of this model mainly considers the queuing delay, processing delay, and link transmission delay. We take the i-th SFC as an example; Q i (t) represents its queue length in time slot t; P i (t) represents the size of the data packet, and it is assumed that the size of the data packet obeys the exponential distribution of parameter P; and A i (t) represents the data packet arrival process of the i-th SFC, and it is assumed that the arrival of data packets obeys the Poisson distribution with a parameter of λ i [48]. e update process of the queue is expressed as follows:…”
Section: Delaymentioning
confidence: 99%
“…CS with respect to received packets is designed to support QoS for various traffic types and achieves high system throughput [14]. In [15], the authors modeled a problem as an infinite-horizon average cost Markov decision process based on the dynamics of channel state information and queue state information (QSI) to perform adaptive user scheduling to minimize the average delay of a network with heterogeneous base stations.…”
Section: Related Workmentioning
confidence: 99%
“…By minimizing the average packet queuing delay, the optimal spectrum allocation pattern was obtained for each BS. Similarly, Cheng et al [24] optimized the average queuing delay of network subjected to the BS power constraints. They formulated a Markov decision process (MDP) problem based on the instantaneous channel state information and queue state information, and proposed an adaptive user scheduling and power control policy.…”
Section: A Related Work and Motivationsmentioning
confidence: 99%