Prioritized Cell Association and Power Control in Uplink Heterogeneous Networks
Bibliographic record
Abstract
A heterogeneous network (HetNet) is a mix of macrocell base stations (MBSs) underlaid by a diverse set of small cell base stations (SBSs) such as microcells, picocells and femtocells. These networks are employed to enhance network capacity, improve network coverage, and reduce power consumption. However, HetNet performance can be limited by the disparity of power levels in the different tiers. Further, conventional cell association approaches cause MBS overloading, SBS underutilization, excessive user interference and wasted resources. Power control and cell association (CAPC) should be determined based on user priority, channel condition and BS traffic load. However, ensuring priority user (PU) requirements while satisfying as many normal users (NUs) as possible is not considered in existing power control algorithms. In this paper, prioritized CAPC is proposed to solve the load balancing problem between MBSs and SBSs and meet the needs of all PUs. Performance results in Additive white Gaussian noise (AWGN) and Rayleigh fading channels are presented which show that the proposed scheme is a fair and efficient solution which reduces power consumption and has faster convergence than other CAPC schemes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".