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.

Reducing the energy consumption is an important objective in the development of cellular modems for LTE and 5G standards. In addition to hardware optimizations, Dynamic Power Management (DPM) techniques that aim at powering down idle system components are crucial for achieving this goal. However, most DPM techniques proposed so far for mobile devices are purely reactive. More promising are recently proposed proactive approaches that predict whether communication with the cellular base station will occur in the next time-slot or not. Due to transition times between power states, however, such single-step approaches may not exploit deeper power-saving states of components. As a remedy, this paper proposes multi-step ahead prediction techniques that forecast periods of no communication lasting multiple time-slots. In this context, we define a formal fine-grained power model and introduce novel predictive power management policies. Furthermore, we explore the impact of the forecast period–the number of predicted time-slots–in terms of false negative error rate as well as achievable energy savings compared to a standard LTE-compliant reactive DPM. Finally, it is shown that this overall energy saving potential of the proposed multi-step predictive approach may be higher by up to a factor of 3 compared to a single-step predictive approach without incurring a higher false negative error rate. Alternatively, the multi-step approach may allow a reduction of the false negative error rate by a factor of 4.1 compared to the single-step approach without reduction of expectable energy savings. In fact, the observed low false negative error rate of 0.0083 may facilitate a realization in future modem solutions.

Reducing the energy consumption of mobile phones is a crucial design goal for cellular modem solutions for LTE and 5G NR standards. Most dynamic power management techniques targeting mobile devices proposed so far, however, are purely reactive in powering down and up system components. Promising approaches extend this, by predicting information from the cell and the communication protocol to take decisions proactively. In this paper, we present a complete proactive power management approach for the modem based on on-line grant prediction. In this context, we define proactive policies that allow a mobile device to go to sleep states more often compared to reactive power management systems, e.g., in time slots of predicted transmission inactivity in a cell. Furthermore, we propose and compare two algorithmic solutions to this proactive grant prediction problem, one a feed-forward neural network and one a SARSA-λ reinforcement agent. As the implementation of these machine learning techniques also creates additional energy and resource costs, both approaches are carefully designed, optimized, and evaluated not only in terms of prediction accuracy, but also in terms of overall energy savings. Notably, our predictor implementations are able to achieve up to 17 percent in overall energy savings on real-world traces.

Reducing the energy consumption of mobile phones is a crucial design goal for cellular modem solutions for LTE and 5G standards. Recent approaches for dynamic power management incorporate traffic prediction to power down components of the modem as often as possible. These predictive approaches have been shown to still provide substantial energy savings, even if trained purely on-line. However, a higher prediction accuracy could be achieved when performing predictor training off-line. Additionally, having pre-trained predictors opens up the ability to successfully employ predictive techniques also in less favorable situations such as short intervals of stable traffic patterns. For this purpose, we introduce a notion of similarity, based on which a clustering is performed to identify similar traffic patterns. For each resulting cluster, i.e., an identified traffic scenario, one predictor is designed and trained off-line. At run time, the system selects the pre-trained predictor with the lowest average short-term false negative rate allowing for energy-efficient and highly accurate on-line prediction. Through experiments, it is shown that the presented mixed static/dynamic approach is able to improve the prediction accuracy and energy savings compared to a state-of-the-art approach by factors of up to 2 and up to 1.9, respectively.

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.

Power consumption is a key challenge for LTE-Advanced or future 5G mobile devices and current power management systems successfully achieve significant power savings. However, these systems are driven by static rules and provide a posteriori responses to traffic and context changes. In this paper, we propose a smart dynamic power management system for cellular modems, extending existing power saving mechanisms by using machine learning-based traffic prediction. With the a priori knowledge of specific scheduling messages, internal device parameters can be finely tuned to improve the modem power consumption. In order to accurately estimate the power saving potential of several LTE use cases, we build a relevant data set of live network modem traces, as well as a power model of the baseband physical layer and radio frequency components. Subsequently, we propose an evaluation methodology and apply it to analyze the predictive power management performance in terms of error rate and global power consumption outcome. Our analysis results in maximal power savings of 12% for meaningful traffic scenarios as well as the identification of variables of interest to improve the proposed power manager.

Large scale many-core systems are able to execute concurrently changing mixes of different parallel applications. Hybrid application mapping combines the strengths of design-time exploration/analysis of resource constellations for task-to-core mappings with the flexibility of choosing concrete mappings at run time. However, state-of-the-art design space exploration (DSE) techniques so far ignore the problem of symmetries in modern heterogeneous architectures: not only recurring patterns in the architecture but the mapping of tasks to instances of the same processor type may unnecessarily increase the search space by redundant, symmetrical implementations which typically affects the quality of the DSE. As a remedy, we propose a novel meta-heuristic DSE approach that eliminates architectural symmetries by abstracting the problem to a clustering of tasks and their mapping to processor types. However, we demonstrate that simple task clustering and type mappings may again introduce encoding symmetries in our search space. Thus, we present a formulation of the task clustering and type mapping as a 0-1 integer linear program (ILP) which eliminates all architectural as well as encoding symmetries from the search space. We also contribute a formal feasibility check to ensure that only implementations with at least one feasible concrete mapping are considered. To further improve the search process for feasible solutions, we apply satisfiability modulo theories-like learning techniques: from each infeasible implementation, we extract conditions why the implementation is infeasible and enrich our 0-1 ILP by additional constraints continuously during the DSE. Experimental results show that a DSE equipped with the novel symmetry-eliminating search space and the proposed learning techniques clearly outperforms a state-of-the-art approach known from literature in terms of the quality of the gained implementation classes.

In embedded systems powered by batteries, power is undoubtedly a critical resource making power management an important topic in the design phase. Even though power management is a heavily researched topic, most approaches focus on improving the way the power manager reacts to outside control events. In this paper, we propose techniques that not only react but rather try to predict these outside control events in advance, thus, broadening the capabilities of any employed power manager by allowing for superior transition decisions and even saving redundant calculations. We present results on employing a predictive power management system that couples a classic dynamic power manager with a machine learning subsystem in the context of a mobile device in a Long Term Evolution (LTE) system, with emphasis on evaluating the potential of saving power as well as the handling of the induced prediction uncertainty. First, we examine the LTE communication protocol and showcase certain control data that has to be received periodically, but may contain no information for the receiver. Finally, we show a proof-of-concept based on real LTE traces and hardware simulation, that prediction of this information can be leveraged to allow for a far superior decision process compared to a non-predicting system. Here, we achieve a theoretical best case power saving of 15 % for an idealized prediction with 100 % accuracy and no additional power consumption.