{'fraction_non_alphanumeric': 0.030514939605848695, 'fraction_numerical': 0.01080737444373808, 'mean_word_length': 5.270916334661354, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Grant Prediction-based Dynamic Power Management for 5G to Reduce Mobile Device Energy Consumption

Reducing the energy consumption of mobile phones is an essential design goal. Whereas Dynamic Power Management (DPM) techniques have been proposed for cellular modems supporting the 5G NR protocol standard, these are purely reactive in nature. For the LTE protocol standard, there also exist approaches that predict grant-free idle intervals during which the modem can be switched off. In this paper, we investigate predictive DPM techniques for the novel 5G protocol standard by exploiting two particular opportunities: First, we may expect micro sleep states can be activated more often in comparison to reactive DPM techniques. Second, the 5G protocol standard offers additional data scheduling techniques using cross-slot and multi-slot scheduling. To select an appropriate prediction model for the resulting complex traffic scenarios, we evaluate six classifiers (Random Forest, Naive Bayes, Decision Tree, K-Nearest Neighbors, Feed-Forward Neural Network, and AdaBoost) for the grant prediction problem. Subsequently, each classifier is evaluated in terms of prediction accuracy and overall energy savings, taking into account the classifier's own energy requirement and timing constraints. As a result, Random Forest proved to be the superior classifier with achievable energy savings of up to 49 % for same-slot scheduling, up to 28 % for cross-slot scheduling, and up to 48 % for multi-slot scheduling, with False Negative Rates (FNRs) ranging between 0.001 - 0.12.