Joint Reflection Coefficient Selection and Subcarrier Allocation for Backscatter Systems with NOMA
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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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 | 0.000 | 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".