Radio resource allocation for multicast transmissions over High Altitude Platforms
Bibliographic record
Abstract
In this paper, we study radio resource allocation (RRA) for multicasting in OFDMA based High Altitude Platforms (HAPs). We formulate an optimization problem for a scenario in which different sessions are multicasted to user terminals (UTs) across HAP service area. We then solve it to find the best allocation of HAP resources such as radio power, sub-channels, and time slots. The objective is to maximize the number of UTs that receive the requested multicast streams in the HAP service area in a given OFDMA frame. The optimization problem comes out to be a Mixed Integer Non-Linear Program (MINLP). Due to the high complexity of the problem and lack of special structures, we believe that breaking it into two easier subproblems and iterating between them to achieve convergence can lead to an acceptable solution. Subproblem 1 turns out to be a Binary Integer Linear Program (BILP) of no explicitly noticeable structure and therefore Lagrangian relaxation is used to dualize some constraints to get a BILP with some special structure that is easy to solve. The subgradient method is used to solve for the dual variables in the dual problem for three proposed methods to get the tightest bound in each. The obtained bounds can be used in a branch and bound (BnB) algorithm as its bounding subroutine at each node. Subproblem 2 turns out to be a simple linear program (LP) for which the simplex algorithm can be used to solve the subproblem to optimality. This paper focuses on subproblem 1 and its proposed solution techniques. In the results section of this paper, we compare the solution goodness for each method versus the well known bounding technique used in BnB which is linear program (LP) relaxation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".