Reinforcement Learning for Power-Efficient Grant Prediction in LTE

Abstract

Reducing the energy consumption of mobile phones is a major concern in the design of cellular modem solutions for LTE and 5G standards. Apart from optimizing hardware for power efficiency, dynamic power management, i.e., powering down idle system components, is a crucial means to achieve this goal. The techniques proposed so far, however, are reactive rather than proactive. This leads to the inability to exploit a significant amount of opportunities to power down components, as the opportunity is recognized too late. We propose a dynamic power management technique that is capable of exploiting said opportunities through the application of reinforcement learning prediction techniques for proactive power management. However, the additional computational effort for prediction algorithms must be carefully analyzed and taken into account. Therefore, we investigate which conditions have to be met in order to achieve net energy savings. The proposed technique has been implemented and evaluated for potential savings on simulated traces of LTE data. The resulting predictor is designed to be trained online, without any prior system knowledge. For a fair evaluation and comparison, the power consumption of the training phase is also considered in the analysis. It is shown that energy savings of up to 23.9 % may be obtained on a modem for scenarios such as HTTP streaming.