On the Energy Efficiency of OFDMA Cellular Networks
Bibliographic record
Abstract
In this article, we study the energy efficiency (EE) of orthogonal frequency-division multiple access (OFDMA) cellular networks under the 5 G requirement of EE enhancement. We aim to present a power allocation scheme maximizing the EE of downlink cellular communications while avoiding numerical methods such as fractional programming. We focus on two EE forms; global EE and weighted sum EE. Therefore, we propose a novel explicit expression of the optimal power allocation related to each subcarrier. We also present the power control with limited power budget or/and minimal transmission rate constraint in both base station and subcarrier perspectives. As a result, we notice the occurrence of some transmission outage events depending on the constraints’ parameters. From another side, we extend our study to analyze the effect of the channel state information (CSI) unavailability on our proposed power scheme. In the numerical results, we show that our proposed power control improves the EE, especially at high power budget regime and low minimal rate regime. We also show that having more subcarriers enhances the OFDMA EE. Finally, we show that EE degradation due to CSI unavailability is very small showing the robustness of the proposed scheme against CSI imperfectness.1
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".