Performance Analysis of Cognitive Wireless Powered Communication Networks Under Unsaturated Traffic Condition
Bibliographic record
Abstract
By improving the efficiency of wireless power transfer (WPT), wireless powered communication networks (WPCNs) are receiving increasing attention. WPCN provides untethered mobility and prolongs the network lifetime by eliminating the need for repetitive charging and replacement of the battery. In this paper, we consider a cognitive WPCN in which wireless powered secondary users (SUs) opportunistically exploit the spectrum licensed to the primary users (PUs). Each SU is associated with a power beacon (PB) node which is responsible for charging the corresponding SU and receiving its data over different frequency bands. SUs have unsaturated data traffic and can transmit if they are out of any guard zone which is defined around active PUs to prevent strong interference. Using tools from stochastic geometry and queueing theory, we characterize the effects of the randomness in data and energy availability of SUs on the interference among PUs and SUs. Then, we derive the service time distribution, mean waiting time, and queue stability criterion for a typical SU, as well as the outage probability of a typical PU. Finally, through extensive simulations, the analytical results are evaluated and the effects of different parameters on the network performance are studied.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".