Optimal Channel-Aware ALOHA Protocol for Random Access in WLANs With Multipacket Reception and Decentralized Channel State Information
Bibliographic record
Abstract
Perfect decentralized channel state information (CSI) is utilized to design an optimal distributed medium access control (MAC) protocol for wireless local area networks (WLANs) with the multipacket reception capability, which is available in CDMA systems for example. In particular, we consider the scenario where a finite number of users transmit packets to a common access point via a channel-aware ALOHA protocol. We analyze the structure of the optimal channel-aware transmission policies for both the spatially homogeneous WLAN system model, where users deploy identical transmission policies, and the spatially heterogeneous WLAN system model, where users are allowed to deploy different transmission policies. It is shown that the optimal transmission policy is nonrandomized and piecewise continuous with respect to the channel state. Furthermore, we prove for CDMA systems, which represent the most important example of networks with the MPR capability, that under a suitable condition, there exists a channel state threshold beyond which it is optimal not to transmit. Last, we propose a provably convergent stochastic approximation algorithm for estimating the optimal transmission policy for spatially homogeneous networked users. Numerical studies illustrate the performance of the algorithm and the degenerate, nonrandomized structure of the optimal transmission policy.
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.
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 | 0.009 |
| 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 | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".