Voice Capacity of Cognitive Radio Networks for Both Centralized and Distributed Channel Access Control
Bibliographic record
Abstract
As an emerging networking technology, cognitive radio networks (CRNs) have drawn immense attention in the wireless networking community. Since multimedia services have become widely popular among wireless communication services users, supporting those services over CRNs has become an interesting research topic in recent years. However, due to the random nature of the resource availability in CRNs, providing quality-of-service (QoS) guarantees for multimedia services is a challenging task. In this paper, we consider a secondary system operating over a time-slotted primary system and secondary users accessing the channels at the spectrum holes without interfering with primary users. As the capacity analysis is one of the basic steps to guarantee QoS, we analyze the constant-rate voice capacity of multi-channel fully-connected CRNs under different generic channel access schemes with centralized and distributed control, respectively. The capacity is represented in terms of the number of simultaneous independent voice calls that the secondary system can support, providing stochastic delay guarantee. It is shown that the analytical results closely match with the simulation results, and the number of voice packets that can be simultaneously transmitted in a time-slot per channel has a significant impact on the capacity of the system. With proper medium access control, capacity analysis can help to develop a call admission control policy for QoS provisioning in CRNs.
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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".