Power-Delay Tradeoff With Predictive Scheduling in Integrated Cellular and Wi-Fi Networks

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

39 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Article number7437421
Pages (from-to)735-742
Journal / PublicationIEEE Journal on Selected Areas in Communications
Volume34
Issue number4
Online published21 Mar 2016
Publication statusPublished - Apr 2016
Externally publishedYes

Abstract

The explosive growth of global mobile traffic has led to rapid growth in the energy consumption in communication networks. In this paper, we focus on the energy-aware design of the network selection, subchannel, and power allocation in cellular and Wi-Fi networks, while taking into account the traffic delay of mobile users. Based on the two-timescale Lyapunov optimization technique, we first design an online Energy-Aware Network Selection and Resource Allocation (ENSRA) algorithm, which yields a power consumption within O (1/V) bound of the optimal value, and guarantees an O (V) traffic delay for any positive control parameter V. Motivated by the recent advancement in the accurate estimation and prediction of user mobility, channel conditions, and traffic demands, we further develop a novel predictive Lyapunov optimization technique to utilize the predictive information, and propose a Predictive Energy-Aware Network Selection and Resource Allocation (P-ENSRA) algorithm. We characterize the performance bounds of P-ENSRA in terms of the power-delay tradeoff theoretically. To reduce the computational complexity, we finally propose a Greedy Predictive Energy-Aware Network Selection and Resource Allocation (GP-ENSRA) algorithm, where the operator solves the problem in P-ENSRA approximately and iteratively. Numerical results show that GP-ENSRA significantly improves the power-delay performance over ENSRA in the large delay regime. For a wide range of system parameters, GP-ENSRA reduces the traffic delay over ENSRA by 20-30% under the same power consumption.

Research Area(s)

  • cellular and Wi-Fi integration, Energy-aware communication, joint network selection and resource allocation, Stochastic optimization