Adaptive Predictive Power Management for Mobile LTE Devices - Brand, Peter; Falk, Joachim; Ah Sue, Jonathan; Brendel, Johannes; Hasholzner, Ralph; Teich, Jürgen (IEEE Transactions on Mobile Computing, 2021)

A Predictive Dynamic Power Management for LTE-Advanced Mobile Devices - Ah Sue, Jonathan; Brand, Peter; Brendel, Johannes; Hasholzner, Ralph; Falk, Joachim; Teich, Jürgen (2018 IEEE Wireless Communications and Networking Conference (WCNC'18), 2018)
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 manage

Clustering-Based Scenario-Aware LTE Grant Prediction - Brand, Peter; Sabih, Muhammad; Falk, Joachim; Ah Sue, Jonathan; Teich, Jürgen (Proceedings of the 2020 IEEE Wireless Communications and Networking Conference (WCNC), 2020)

Exploiting Predictability in Dynamic Network Communication for Power-Efficient Data Transmission in LTE Radio Systems - Brand, Peter; Ah Sue, Jonathan; Brendel, Johannes; Falk, Joachim; Hasholzner, Ralph; Teich, Jürgen; Wildermann, Stefan (20th International Workshop on Software and Compilers for Embedded Systems (SCOPES’17), 2017)
Keywords: Dynamic Network Communication; LTE Radio Systems; Machine Learning; Predictability; Power Efficient Data Transmission

Grant Prediction-based Dynamic Power Management for 5G to Reduce Mobile Device Energy Consumption - Brand, Peter; Hackenberg, Benjamin; Falk, Joachim; Teich, Jürgen (International Wireless Communications and Mobile Computing Conference (IWCMC 2022), 2022)

Multi-Step Ahead Grant Prediction for Dynamic Power Management in Cellular Modems - Brand, Peter; Falk, Joachim; Potwigin, Eduard; Teich, Jürgen (Proceedings of the 2021 International Symposium on Networks, Computers and Communications (ISNCC 2021), 2021)
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.<br

Optimizing Exploratory Workflows for Embedded Platform Trace Analysis and its Application to Cellular Modems - Ah Sue, Jonathan; Brand, Peter; Falk, Joachim; Hasholzner, Ralph; Teich, Jürgen (HCII 2019 Late Breaking Work Papers Proceedings, 2019)
As 5G wireless communication technology will be deployed in the next few years, an increasing amount of data becomes available from mobile devices tested out in the field. Exploiting this data, recent investigations show the potential to improve the wireless modem design and performance using data- centric approaches, e.g., power optimization with machine learning (ML) algorithms [1]. As depicted in Figure 1, such data-centric workflows are exploratory and iterative by nature. For instance, time pattern identification is performed by domain experts to derive assumptions on potential optimizations and these assumptions are continuously refined during multiple iterations of data collection and exploration. In this context, we propose 3 ideas to increase the exploration speed: (i) a methodology to minimize the data pre-processing duration in each iteration, (ii) a novel entropy-based data interaction technique for visual event sequence exploration and (iii) a similarity measure to perform subsequence matching in order to identify frequent modem behaviors to be optimized.<br
Keywords: 5G; LTE; machine learning; visualization

Reduction of Mobile Device Modem Energy via Adapative and Power-efficient Hybrid Online/Offline Grant Prediction for the Protocol Standards LTE and 5G - Brand, Peter (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2023)

Reinforcement Learning for Power-Efficient Grant Prediction in LTE - Brand, Peter; Falk, Joachim; Ah Sue, Jonathan; Brendel, Johannes; Hasholzner, Ralph; Teich, Jürgen (21st International Workshop on Software and Compilers for Embedded Systems (SCOPES’18), 2018)
Keywords: Dynamic Network Communication; LTE Radio Systems; Machine Learning; Predictability; Power Efficient Data Transmission

Simulating Realistic IoT Network Traffic Using Similarity-based DSE - Brand, Peter; Falk, Joachim; Maier, Tanja; Teich, Jürgen (2021 International Conference on Computational Science and Computational Intelligence (CSCI), 2021)
CRIS-Team WoS Importer:2022-08-26

Symmetry-eliminating Design Space Exploration for Hybrid Application Mapping on Many-Core Architectures - Schwarzer, Tobias; Weichslgartner, Andreas; Glaß, Michael; Wildermann, Stefan; Brand, Peter; Teich, Jürgen (IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2018)