Load Management, Power and Admission Control in Downlink Cellular OFDMA Networks
Bibliographic record
Abstract
We present a resource management framework for load-coupled downlink cellular OFDMA networks considering the load factor of an individual base station (BS) per resource block (RB), i.e., the number of adjacent sub-carriers (SCs), as the variable of interest in the resource management problem. The load factor of a BS per RB, which corresponds to the fraction of active SCs in the BS per RB, is an indicator of the level of resource consumption, and it affects the interference caused to that RB reused in other BSs, and thereby, results in a load-coupled OFDMA system. We first propose two distributed schemes to minimize: (i) the total load factor of the BSs (which would in turn increase the number of supportable users in the system), and (ii) the total downlink transmit power level of the BSs. Then, we derive the necessary and sufficient conditions for checking the feasibility of given target-rate requirements (also referred to as demand vector) for users. Accordingly, an iterative and distributed scheme is proposed to check the feasibility of a given demand vector. Next, for a priority-based load-coupled network, we propose a priority-based gradual removal algorithm to support the maximal number of low-priority users while satisfying the demands of the high-priority users. To evaluate the performance of our proposed schemes for resource management and admission control in load-coupled OFDMA networks, the theoretical investigations are complemented with 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 | 0.002 |
| 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".