Energy-Aware Download Method in LTE Based Smartphone
Bibliographic record
Abstract
Mobile traffic is experiencing tremendous growth, and this growing wave is no doubt increasing the use of radio component of mobile devices, resulting in shorter battery lifetime. In this paper, we present an Energy-Aware Download Method (EDM) based on the Markov Decision Process (MDP) to optimize the data download energy for mobile applications. Unlike the previous download schemes in literature that focus on the energy efficiency by simply delaying the download requests, which often leads to a poor user experience, our MDP model learns off-line from a set of training download workloads for different user patterns. The model is then integrated into the mobile application to deal the download request at runtime, taking into account the current battery level, LTE reference signal receiving power (RSRP), reference signal signal to noise radio (RSSNR) and task size as input of the decision process, and maximizes the reward which refers to the expected battery life and user experience. We evaluate how the EDM can be used in the context of a real file downloading application over the LTE network. We obtain, on average, 20.3%, 15% and 45% improvement respectively for energy consumption, latency, and performance of energy-delay trade off, when compared to the Android default download policy (Minimum Delay).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".