A Game Theoretical Approach for Transmission Strategies in Slotted ALOHA Networks with Multi-Packet Reception
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper we consider finite-size slotted ALOHA sensor networks with multiple packet reception capability and selfish sensors. Each sensor wishes to maximize its individual expected reward. We exploit decentralized channel state information (CSI) to obtain transmission policies that are optimal for each sensor The problem is formulated as a finite player finite action, non-cooperative stochastic game where each sensor is a selfish but rational player We prove for the first time that under the signal to interference noise ratio (SINR) threshold reception model the optimal transmission policy for each player belongs to the class of threshold policies. As a result, there exists a Nash equilibrium at which all players adopt pure strategies. The optimality of threshold policies greatly simplifies the estimation of optimal transmission schemes. We present a provably convergent algorithm for finding the threshold for each sensor and illustrate its performance via numerical examples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 it