QoE‐ and energy‐efficient resource optimization in OFDMA networks with bidirectional relaying
Bibliographic record
Abstract
Abstract Because of the surging demands of multimedia services, quality‐of‐experience (QoE) is becoming an important metric to evaluate network quality from users' perspective. In this paper, resource optimisation to achieve optimal tradeoff between QoE and energy consumption in bidirectional orthogonal frequency‐division multiple‐access relaying networks is addressed so as to provide satisfactory multimedia delivery quality and support green communications. We first formulate a QoE‐energy efficiency tradeoff optimisation where QoE requirements and relaying traffic balance are considered and prove that QoE‐energy efficiency is quasiconcave on QoE, which suggests the existence of a unique global optimal tradeoff point. We then propose an optimisation framework to achieve the optimal tradeoff efficiently. With the framework, we develop resource allocation approaches for two specific relaying strategies, that is, two‐phase decode‐and‐forward relaying with dynamic XOR network coding and compute‐and‐forward relaying with physical network coding via structured codes. Numerical results validate theoretical findings and demonstrate the effectiveness of the proposed optimisation solution for achieving the tradeoff between QoE and energy consumption. Copyright © 2015 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".