Uplink power allocation schemes for heterogeneous cellular networks
Bibliographic record
Abstract
With the increasing number of small cell nodes, the power consumption in Heterogeneous Networks (HetNets) has increased significantly resulting in problems such as higher interference among nodes, higher outage, and decreased capacity. In this paper, we propose various power allocation schemes where we allocate power among the heterogeneous user equipments. First, we propose a scheme which jointly minimizes outage probability and total power consumption in the network. Second, we devise a scheme which jointly maximizes total throughput and minimizes total power consumption. These schemes formulate multi-objective optimization problems, which are then solved using weighted sum approach. Third, we investigate a scheme to maximize energy efficiency defined as system throughput achieved per unit power consumption. Simulation results clarify the need for tradeoff among various performance parameters in practice based on device's remaining battery capacity, system outage target and quality of service (QoS) requirement in terms of signal-to-interference-plus-noise ratio (SINR). The results also show that the proposed power allocation schemes are highly efficient in attaining such tradeoffs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".