Uplink Power Allocation for Throughput and Energy Efficiency Over Nakagami-<i>m</i> Fading Channels
Bibliographic record
Abstract
In this article, power allocation in cellular networks considering Nakagami- m fading is proposed. The objective is to optimize the network energy efficiency and throughput subject to user outage probability constraints. The moment generating function (MGF) is used to derive the exact outage probability over Nakagami- m fading channels. Further, tight upper and lower bounds on the outage probability are derived using the Weierstrass, Bernoulli and exponential inequalities. These bounds are used to characterize the relationship between outage probability and normalized signal to interference plus noise ratio (SINR) in Nakagami- m fading. Power allocation algorithms for throughput maximization and energy efficiency are proposed. The throughput maximization problem has a logarithmic form so a differential method is used to solve this problem. The proposed energy efficiency problem has a nonconvex fractional program form so a parametric transformation is used to convert it to a subtractive optimization problem which can be solved iteratively. Simulation results are presented which show that the proposed schemes provide better performance than existing methods in terms of power consumption, throughput, energy efficiency and outage probability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".