MAC coding for QoS guarantees in multi-hop mobile wireless networks
Bibliographic record
Abstract
The coding theory goal of finding codes with the largest possible distance among its constituent code-words in an ever smaller dimensional space is analogous to the goal of finding separate yet efficient ways for the nodes of a network to transmit in a multiple access system. A Medium Access Control (MAC) strategy is proposed referred to as MAC coding that leverages the use of codes traditionally used for channel coding purposes. It is shown how these codes can be utilized to device a scheduling strategy that has the potential to guarantee a minimum level of performance for the nodes of a multi-hop mobile wireless ad hoc network in an efficient manner. Additionally, coding theory results are used to derive simple expressions for the minimum throughput and delay of nodes when using Reed-Solomon and Hermitian error correcting codes as MAC scheduling codes.The average performance of a large family of MAC scheduling codes is analytically compared to the one obtained by slotted-ALOHA, and a code-selection algorithm is proposed that can improve the average throughput of MAC coding when the number of nodes in the network is greater than the number of code-words available in a given code.
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 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 | 0.001 | 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".