Game Theoretic Cross-Layer Transmission Policies in Multipacket Reception Wireless Networks
Bibliographic record
Abstract
We study the structure of the optimal transmission policies for noncooperating nodes in a finite-size random access wireless network, where the medium access control (MAC) protocol is a variant of the time-slotted ALOHA protocol. It is assumed that the network has the multipacket reception capability and every node knows its channel state information (CSI), which is continuously distributed, perfectly at the beginning of each transmission time slot. The objective of each node in the network is to find a transmission policy mapping CSI to transmission probabilities to maximize its individual utility. The problem is formulated as a noncooperative game of a finite number of rational players and actions with a continuous channel state space. We prove that if the probability of success of a node is a nondecreasing function of its CSI, there exists a threshold transmission policy that maximizes its utility. It is then shown that there exists a Nash equilibrium at which every node adopts a threshold policy. The optimality of threshold policies strongly simplifies the problem of optimizing the transmission policy for a node. We propose a stochastic-gradient-based algorithm that exhibits the best response dynamic adjustment process for the transmission game. The theoretical results of the paper as well as the performance of the proposed algorithm are illustrated via numerical examples
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".