Performance Analysis of a Backscatter-assisted Full-Duplex Wireless-Powered Cognitive Radio Network
Bibliographic record
Abstract
To prolong the lifespan of low-power cognitive sensors, we consider a backscatter-assisted wireless-powered cognitive radio network (WPCRN) in which the sensors must harvest sufficient energy to support conventional data transmission. When the sensors experience low energy levels, they switch to the backscatter communication mode to reduce the latency prior to transmitting sensed data. Additionally, we use the full-duplex model to achieve improved spectral and time efficiency. However, due to channel uncertainty, designing robust conventional and backscatter data transmission is challenging. Thus, the goal is to design a robust data combining vector against channel inaccuracies and, consequently, maximize the sum-throughput of all the sensors. We propose an efficient and low-complexity algorithm named Joint Optimal Time and Energy Allocation with Robust Beamforming (JOTEA-R). Numerical results show that the JOTEA-R algorithm improves the sum-throughput by approximately 15% compared to the benchmark equal time allocation algorithm with robust beamforming. The proposed system model can be used in a remote environmental monitoring network in which the sensors must continuously monitor and transmit information such as temperature, humidity, air quality, and pollution levels data to a cloud data center.
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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 | 0.002 | 0.004 |
| 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) | 0.002 | 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".