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

Abstract

Abstract-The explosive growth of global mobile traffic has lead to a rapid growth in the energy consumption in communication networks. In this paper, we focus on the energyaware 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. The problem is particularly challenging due to the two-timescale operations for the network selection (large timescale) and subchannel and power allocation (small timescale). Based on the two-timescale Lyapunov optimization technique, we first design an online Energy-Aware Network Selection and Resource Allocation (ENSRA) algorithm. The ENSRA algorithm 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 powerdelay 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.

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“…The energy efficiency of the system is studied and the energyefficient Pareto region is characterized. A Lyapunov optimization technique is employed in [61] to optimize the energy efficiency of WiFi networks with respect to network selection, sub-channel assignment, and transmit power. Energy efficiency optimization in MIMO-OFDM networks is performed in [62], considering the practical scenario in which the propagation channels vary dynamically over time (for example due to user mobility, fluctuations in the wireless medium, and changes in the users' loads).…”

Cross-Layer Scheduling and Beamforming in Smart-Grid Powered Cellular Networks With Heterogeneous Energy Coordination

“…Since the scheduled UE indictors are binary variables, the proposed schemes in [29]- [32] cannot obtain the optimal scheduled UE indicators. Yu et al [33] investigated the joint network selection, subchannel and power allocation problem in integrated cellular and Wi-Fi networks. Exhaustive search is used in [33] to solve the network selection subproblem, and greedy selection is used to solve the subchannel allocation subproblem.…”

“…Yu et al [33] investigated the joint network selection, subchannel and power allocation problem in integrated cellular and Wi-Fi networks. Exhaustive search is used in [33] to solve the network selection subproblem, and greedy selection is used to solve the subchannel allocation subproblem. However, exhaustive search is computational expensive, and greedy selection can lead to suboptimal solutions for the scheduled UE indicators when the number of UEs is large.…”

Energy Efficient Greedy Algorithm for Wireless Mesh Networks

“…The known traffic demands are satisfied while minimizing the overall energy usage of the network. Yu et al [4] have given energy-aware design of the network selection, sub channel, and power assignment in cellular and Wi-Fi networks. Researchers in [3][4] Research Article authors in [5] reduces energy consumption by accessing information of medium access layer via control packet.…”

“…Yu et al [4] have given energy-aware design of the network selection, sub channel, and power assignment in cellular and Wi-Fi networks. Researchers in [3][4] Research Article authors in [5] reduces energy consumption by accessing information of medium access layer via control packet. In [6] authors have enhanced the network life time by shutting down the unnecessary nodes.…”