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

TL;DR

This paper introduces predictive scheduling algorithms for cellular and Wi-Fi networks that optimize power consumption and traffic delay by leveraging user mobility and traffic predictions, improving efficiency over existing methods.

Contribution

It develops a novel predictive Lyapunov optimization technique and algorithms that incorporate traffic and mobility predictions for energy-efficient network management.

Findings

GP-ENSRA reduces traffic delay by 20-30% compared to ENSRA.

The algorithms achieve near-optimal power consumption with controllable delay.

Performance bounds of the predictive approach are theoretically characterized.

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 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. 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 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.