Channel access-aware user association in two-tier cellular networks
Bibliographic record
Abstract
The diverse transmit powers of the base-stations (BSs) in a multi-tier cellular network lead to uneven distribution of the traffic loads among different BSs and thus cause underutilization of the available resources at low power BSs. In this context, this paper proposes a channel access-aware (CAA) user association scheme that can simultaneously enhance the system spectral efficiency and balance the traffic loads among different BSs. The CAA scheme is a network-assisted user association scheme that requires the traffic load informations from different BSs in addition to the channel quality indicators. Also, in this paper, we develop a tractable mathematical framework to characterize the spectral efficiency of downlink transmission to a user who associates to a BS using CAA scheme. Numerical results demonstrate the performance gains of CAA scheme over conventional received signal power-based association and biased-received signal power-based association. The derived expressions provide approximate solutions of reasonable accuracy when compared to the results obtained by Monte-Carlo simulations. Moreover, the impact of state-of-the-art almost blank sub-frames (ABS)-based interference coordination scheme on the proposed CAA scheme is also investigated using Monte-Carlo simulations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".