Cooperative Resource Allocation Games Under Spectral Mask and Total Power Constraints
Bibliographic record
Abstract
Resource allocation problems in multi-user systems, modeled as Nash bargaining (NB) cooperative games, are investigated under different constraints. Using the joint time division multiplexing and frequency division multiplexing (TDM/FDM) scheme as a manner of cooperation in the NB resource allocation game with only spectrum mask constraints (SMCs), it is shown that the efficiency of the corresponding solution depends on both the interference-to-signal and interference-to-noise ratios of the users. Sufficient conditions under which the joint TDM/FDM based NB solution outperforms a noncooperative Nash equilibrium solution are derived and shown to be mild. A semi-distributed algorithm is proposed for finding the NB solution of the corresponding game with SMCs only. The NB based resource allocation game with both SMCs and total power constraints (TPCs) is shown to be a nonconvex optimization problem of high complexity. For such a game, a classification of two-user systems into bandwidth- and power-dominant systems is proposed based on the concept of Pareto-optimality. This classification gives insights to the resource allocation problem by showing that the benefits of both users are guaranteed to increase simultaneously only when the dominant resource in the system increases. Using this classification, efficient suboptimal algorithms of low complexity are derived based on the idea of optimally allocating the dominant resource and suboptimally allocating the other resource. Simulation results demonstrate the efficiency of the proposed cooperative NB resource allocation strategies.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".