CDMA-Based Dynamic Power and Bandwidth Allocation (DPBA) Scheme for Multiclass EPON: A Weighted Fair Queuing Approach
Bibliographic record
Abstract
Multiclass systems with different quality of service (QoS) requirements are essential in today’s Ethernet passive optical networks (EPONs). In this paper, we propose a code division multiple-access-enabled dynamic power and bandwidth allocation (DPBA) algorithm for a multiclass system. The novelty of the proposed algorithm is the resource allocation components: power control and bandwidth allocation. Both resources are related and optimized through the weights of the weighted round-robin scheduler in a way to meet the physical layer signal to interference ratio and the network layer packet delay requirements for every class of users. Our objective is to offer a differentiated class of services for all optical network units while optimizing network performance and guaranteeing fairness between different classes. A closed-form solution for the optimal power and bandwidth allocation using the DPBA algorithm is analytically derived. It is shown that the proposed algorithm can radically enhance the network performance in terms of packet delay, throughput, queue size management, transmission cycle time, and class of service fairness while guaranteeing the QoS requirements for all classes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".