RIS-Assisted Energy- and Spectrum-Efficient Symbiotic Transmission in NOMA Systems
Bibliographic record
Abstract
Reconfigurable intelligent surface (RIS) is able to create favorable reflecting channels for different users and piggyback additional data in the reflected signals. The former brings benefits to non-orthogonal multiple access (NOMA), while the latter enables a mechanism of symbiotic radio (SR). Inspired by these unique advantages, we consider a general SR-NOMA system model where an RIS is deployed to assist both the NOMA in an uplink multi-channel system and the Internet-of-Things (IoT) data transmission. This general model also allows for different performance objectives from the NOMA users. In particular, the users can be either energy-efficiency oriented or spectrum-efficiency oriented. To strike the performance trade-off between these two types of users, a performance metric called resource efficiency (RE) is leveraged to formulate the optimization problem. We jointly design the time-frequency resource allocation, multi-user power control and RIS phase shifts to maximize the weighted sum-RE of the system, subject to the quality-of-service constraints of the SR-NOMA system. An efficient alternating optimization framework with a series of algorithms, including matching theory, fractional programming method, and inner majorization-minimization method, is developed to solve this highly complex and non-convex problem.
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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 | 0.000 | 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 | 0.000 | 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".