Wireless Multicast Using Relays: Incentive Mechanism and Analysis
Bibliographic record
Abstract
In wireless multicast systems, cooperative multicast, in which successful users help to relay received packets to unsuccessful users, has been shown to be effective in combating channel fading and improving system performance. However, this mechanism requires the users' voluntary contributions, which cannot be guaranteed since users are selfish and only care about their own performance. Furthermore, users may have heterogeneous costs (which are their private information) to forward packets, and they may lie about their costs if cheating can improve their utilities. To address these problems, in this paper, we model the interaction among users in the wireless multicast system as a multiseller multibuyer payment-based game, where users pay to receive relay service and get paid if they forward packets to others. A simplified case with homogeneous users that have the same cost to forward packets is investigated first. Then, for the case with heterogeneous users, to encourage users to tell their true costs, we use the second-price sealed-bid auction, which is a truth-telling auction, since bidding the true cost is a weakly dominant strategy. To analyze the multiseller multibuyer payment-based game, we observe that under different selected prices, the game can converge to different equilibria, resulting in different user free-riding probabilities and system throughput. We also study the price selection problem and derive the optimal price that maximizes the system throughput. Simulation results show the effectiveness of our proposed mechanism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".