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. Index Terms-Energy-aware communication, joint network selection and resource allocation, cellular and Wi-Fi integration, stochastic optimization.
Yu et al. REF investigated the joint network selection, subchannel and power allocation problem in integrated cellular and Wi-Fi networks.
Power-Delay Tradeoff With Predictive Scheduling in Integrated Cellular and Wi-Fi Networks
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications
Computer Science