Joint Reflection Coefficient Selection and Subcarrier Allocation for Backscatter Systems with NOMA
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Bibliographic record
Abstract
Non-orthogonal multiple access (NOMA) and backscatter communication are two emerging technologies that enable low power communication for the Internet of Things (IoT) devices. In this paper, we consider a multicarrier NOMA (MC-NOMA) backscatter communication system. The objective is to maximize the aggregate data rate of the system by jointly optimizing the reflection coefficients and subcarrier allocation. The formulated problem is nonconvex and exhibits hidden monotonicity structure. To obtain the optimal solution, we propose an algorithm based on discrete monotonic optimization. The proposed algorithm can be considered as a performance benchmark. We also transform the nonconvex problem to another problem by using difference of convex functions and successive convex approximation and propose an algorithm to obtain a suboptimal solution in polynomial time. Simulation results show that the suboptimal scheme achieves an aggregate data rate close to the proposed optimal scheme. Results also show that our proposed schemes provide a higher aggregate data rate than the orthogonal multiple access (OMA) scheme.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it