Socially-optimal multi-hop secondary communication under arbitrary primary user mechanisms
Bibliographic record
Abstract
In a cognitive radio system, licensed primary users can lease idle spectrum to secondary users for monetary remuneration. Secondary users acquire available spectrum for their data delivery needs, with the goal of achieving high throughput and low spectrum charges. Maximizing such a net utility (throughput utility minus spectrum cost) is a central problem faced by a multihop secondary network. Optimal decision making is challenging, since it involves multiple data flows, cross-layer coordination, and economic constraints (budgets of sources). The picture is further complicated by the inter-play between secondary data communication and primary spectrum leasing mechanisms. This work is the first to investigate the full spectrum of socially optimal secondary user communication. We design a social welfare maximization framework for multi-session multi-hop secondary data dissemination based on Lyapunov optimization techniques. A salient feature of the framework is that it takes any given primary user mechanism as input, and produces correspondingly a dynamic, distributed rate control, routing, and spectrum allocation and pricing protocol that can achieve longterm maximization of the overall system utility. Through rigorous theoretical analysis, we prove that our online protocol can achieve a social welfare that is arbitrarily close to the offline optimum, with only finite buffer space requirement at each secondary user, and guarantee of no buffer overflow. Empirical studies are conducted to examine the performance of the protocol.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".