An algorithm for maximal resource utilization in wireless multimedia CDMA communications
Bibliographic record
Abstract
A mathematical programming problem was developed in Soleimanipour et al. (1998) for resource allocation problems in wireless multimedia CDMA systems. Using a comprehensive wireless multimedia model, the resource allocation strategy is formulated to maximize the total profit gained by a service provider while supporting a wide range of multimedia applications and satisfying various service quality requirements. In the mathematical model, a nonlinear problem is to be solved for a large set of assignments. The objective of this paper is to provide an exact solution for the problem using a centralized algorithm. An equivalent linear programming problem is developed which convexifies the problem and removes local maxima. Using signal-to-interference ratio (SIR) per bit and handoff constraints, the set of feasible assignments is reduced to a computationally-reasonable size. The outcome of this research provides an estimate of the ideal performance of the network for further evaluation of practical solutions based on less computational complexity and reasonable approximations. For a single-cell system, however, efficient practical solutions are provided in this paper. The numerical results for different scenarios illustrate the capabilities of the model and the advantages of the resource allocation algorithm.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".