E-MICE Energy-Efficient Efficient Concurrent Exploitation of Multiple Wi-Fi Wi Radios
Abstract: The concurrent use of multiple Wi Wi-Fi Fi radios in individual frequency channels is a solution readily available today to the increase of a mobile station's communication capacity, but at the expense of occasional performance deterioration (when the heterogene heterogeneity ity of capacity between interfaces gets severe) and additional power consumption. This paper proposes a mobile-side mobile solution for the concurrent use of multiple radios in a performance-aware performance and energy-efficient efficient manner, with which a mobile station activates and deactivates radio interfaces dynamically according to traffic demands and a predicted capacity gain. To this end, the proposed solution is composed of multiple prediction algorithms and a control algorithm. Prediction when activating an additional radio io interface is relatively difficult since no information of the disabled interface's current status (and the corresponding frequency channel's) is available at the time of prediction. Our experiments show that, despite different types and used channels, different ifferent radio interfaces have a strong correlation of received signal strengths and used PHY rates between them. Based on this observation, the proposed solution learns a correlation pattern between interfaces whenever multiple interfaces are active and m makes akes prediction of the coverage, expected PHY rate and capacity impact of an inactive interface based on the learned correlation with a currently active interface. The design of the prediction algorithms are based on a simple or machine machine-learning learning technique (SVM). The