Efficient Access Control for Broadband Power Line Communications in Home Area Networks
Bibliographic record
Abstract
In this paper, we address the problem of improving the medium access control (MAC) layer efficiency in indoor broadband power line communication networks. Several overheads in the MAC layer, like random back-offs and collision recovery, degrade the MAC efficiency. To reduce these overheads, we apply in-band full-duplexing (IBFD), which enables medium-aware transmission at all the network nodes. Specifically, we propose two new schemes called contention-free pre-sensing and mutual preamble detection to minimize the time spent during contentions and collisions. Considering the non-idealities of IBFD, we analytically show the feasibility of our solutions. We further design a comprehensive simulation model with multiple priority data frames and Poisson network traffic arrival to emulate a real in-home network traffic. We then present numerical results for both the classical saturated network model and the comprehensive traffic model, to show through OMNeT++ simulations that our presented solutions achieve over 95% of the optimum MAC efficiency that can only be attained in the idealized case of no contentions or collisions.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.006 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".