Maximizing Secondary Users’ Sum-Throughput in an In-Band Full-Duplex Cognitive Wireless Powered Backscatter Communication Network
Bibliographic record
Abstract
This article studies the secondary users’ sum-throughput in an in-band full-duplex (I-FD) cognitive wireless powered backscatter communication network. The secondary network consists of a hybrid access point (H-AP) and a finite number of geographically distributed secondary backscatter sensors (SBSs), each capable of using either the conventional or backscatter communication. The secondary network shares the primary network’s spectrum using an underlay spectrum sharing model. In this model, the H-AP and SBSs operate in I-FD mode to achieve improved spectral and time efficiency. Moreover, when an SBS has little available energy, it switches to the low-energy consumption backscatter method instead of the conventional transmission method. The goal is to maximize the sum-throughput of the SBSs. It is shown that such a problem is a convex optimization problem. Closed-form expressions for the optimal allocated time and energy to SBSs are derived and solved via a low complexity and efficient algorithm called joint optimal time and energy allocation (JOTEA). Numerical results illustrate that the JOTEA algorithm achieves a higher sum-throughput than the benchmark equal time allocation method. Furthermore, if the self-interference cancellation circuit considerably cancels self-interference in the H-AP, the I-FD mode achieves a higher sum-throughput performance than that of the half-duplex mode. Moreover, using backscatter communication results in a further increase in the sum-throughput.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".