Power Allocation in Cellular Networks Based on Outage Probability and Normalized SINR
Bibliographic record
Abstract
In this paper, power allocation in cellular networks is proposed based on the outage probability and normalized signal to interference plus noise ratio (SINR). Upper and lower bounds on the outage probability are determined using the normalized SINR considering path loss, shadowing, and fading. The problems of minimizing the user power subject to outage probability and target SINR constraints are then considered as power allocation problems. These problems are solved using Perron-Frobenius theory and geometric programming (GP). The objectives are to efficiently provide users with flexible date rates and reduce the outage probability and user transmit power. Typically, only the path loss is considered in determining the outage probability whereas path loss, multipath fading and lognormal shadowing are considered in this paper along with the interference from other users. Results are presented which show that the proposed power allocation schemes provide better performance than the target SINR tracking power control (TPC), opportunistic power control (OPC), and temporary removal and feasibility check power control (DFC) algorithms.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".