Self-Organizing TDMA: A Distributed Contention-Resolution MAC Protocol
Bibliographic record
Abstract
This paper presents a self-organizing time division multiple access (SO-TDMA) protocol for contention resolution aiming to support delay-sensitive applications. The proposed SO-TDMA follows a cognition cycle where each node independently observes the operation environment, learns about the network traffic load, and then makes decisions to adapt the protocol for smart coexistence. Channel access operation in SO-TDMA is similar to carrier-sense multiple-access (CSMA) in the beginning, but then quickly converges to TDMA with an adaptive pseudo-frame structure. This approach has the benefits of TDMA in a high-load traffic condition, and overcomes its disadvantages in low-load, heterogeneous traffic scenarios. Furthermore, it supports distributed and asynchronous channel-access operation. These are achieved by adapting the transmission-opportunity duration to the common idle/busy channel state information acquired by each node, without any explicit message passing among nodes. The process of adjusting the transmission duration is modeled as a congestion control problem to develop an additive-increase-multiplicative-decrease (AIMD) algorithm, which monotonically converges to fairness. Furthermore, the initial access phase of SO-TDMA is modeled as a Markov chain with one absorbing state and its required convergence time is studied accordingly. Performance of SO-TDMA in terms of effective capacity, system throughput, collision probability, delay-outage probability and fairness is investigated. Simulation results illustrate its effectiveness in performance improvement, approaching the ideal case that needs complete and precise information about the queue length and the channel conditions of all nodes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".