Performance modeling of QoS in a multicode multicarrier CDMA wireless network with fading
Bibliographic record
Abstract
Abstract For emerging wireless mesh networks, multicode multicarrier CDMA(MC‐CDMA) based technology is one of the most viable candidates. We perform stochastic modelling of the queues at the mobile station for uplink communication with multicode multicarrier CDMA system with two types of traffic, namely real‐time and non‐real‐time. Each traffic is assigned its own codes. However, the non‐real‐time traffic is allowed to use codes assigned to real‐time traffic, when real‐time traffic is not using its codes. Based on the probability of bit error for a multicode MC‐CDMA system, we first compute the probability of packet error. The packet in error will be inserted into the queue until it successfully gets through to the receiver. The packet arrival process at the input queue is modelled as Markov modulated Poisson processes (MMPP). The QoS performance in terms of packet loss for real‐time traffic and the occupancy distribution for non‐real‐time traffic is evaluated using matrix geometric techniques. We present numerical results for low and high load of real‐time traffic with varying loads of non‐real‐time traffic. We observe the binomial tweaking feature of occupancy distribution at higher loads due to batch departures. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".