Throughput-based incentives for residential femtocells
Bibliographic record
Abstract
In this paper, a) we investigate a simple throughput based incentive mechanism that could convince home users to install residential femtocells (by offering them a rate which is α > 1 times higher than the average rate received by a user associated with the macro base station), avoid most of the curse of free riders (i.e., selfish users who gets improved service just because others are investing in femto technology) and, at the end, be beneficial to both the users and the operator, and b) we propose a heuristic to compute the resource allocation supporting this incentive scheme. We show that it is cost-effective for a network operator to allocate a pool K of its licensed channels to a certain number of femto cells, even under our incentive mechanism. We first study a static scenario with fixed numbers of macro and femto users. We identify the range of values of the incentive parameter α that can be supported to reward femto users without harming macro users and we quantify the throughput gains for both macro and femto users in different situations. Then, we consider a dynamic scenario, where the number of users and the number of active femtocells change continuously, and we propose a simple and very accurate heuristic for computing K with a minimal amount of information to be collected at the macro base station. Our numerical results show that by using our proposed approach both macro and femto users are better off than in a pure macrocellular scenario.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".