Resource Allocation for STAR-RIS Assisted NOMA-SR With Hybrid Active-Passive Communication
Bibliographic record
Abstract
The Internet of Things (IoT) employing symbiotic radio (SR) technology encounters challenges such as low throughput and susceptibility to double fading. To address these challenges, this paper integrates non-orthogonal multiple access (NOMA) with simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) technology in an SR system, introducing a novel transmission model termed STAR-RIS-assisted NOMA-SR with hybrid active-passive communication. The proposed model operates in three phases. In the first two phases, when the primary system’s licensed spectrum is occupied, the backscatter devices (BDs) utilize backscatter communication (BC) to establish a symbiotic relationship with the primary system. Specifically, in Phase 1, STAR-RIS enhances the energy harvesting (EH) of BDs via the reflection mode, while in Phase 2, it aids both the primary and secondary systems via the transmission mode. In Phase 3, when the licensed spectrum is idle, STAR-RIS facilitates the active communication (AC) of BDs via the transmission mode. To maximize the total throughput of BDs while guaranteeing the primary system’s target throughput, we formulate a non-convex optimization problem and develop a block coordinate descent (BCD)-based resource allocation scheme. The problem is decomposed into subproblems and solved using successive convex approximation (SCA), variable substitution, and semi-definite relaxation (SDR) to jointly optimize transmission time, beamforming, STAR-RIS reflection and transmission coefficients, as well as BDs’ power allocation and reflection coefficients. Numerical results show that the proposed scheme enhances the total throughput of BDs by 14.36%, 43.43%, 67.78%, and 439.69% compared to four baseline schemes.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".