Resource Allocation in STAR-RIS-Aided Networks: OMA and NOMA
Bibliographic record
Abstract
Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is a promising technology that aids in achieving full-space coverage on both sides of the surface, by splitting the incident signal into transmitted and reflected signals. This paper investigates the resource allocation problem in a STAR-RIS-assisted multi-carrier communication networks. To maximize the system sum-rate, a joint optimization problem comprising of the channel assignment, power allocation, and transmission and reflection beamforming at the STAR-RIS for orthogonal multiple access (OMA) is first formulated. To solve this challenging problem, we first propose a channel assignment scheme utilizing matching theory and then invoke the alternating optimization-based method to optimize the resource allocation policy and beamforming vectors iteratively. Furthermore, the sum-rate maximization problem for non-orthogonal multiple access (NOMA) with flexible decoding orders is investigated. To efficiently solve it, we first propose a location-based matching algorithm to determine the sub-channel assignment, where a transmitted user and a reflected user are grouped on a sub-channel. Based on thistransmission-and-reflectionsub-channel assignment strategy, a three-step approach is proposed, which involves the optimization of decoding orders, beamforming-coefficient vectors, and power allocation, by employing semidefinite programming, convex upper bound approximation, and geometry programming, respectively. Numerical results unveil that: 1) For OMA, a general design that includes the same-side user-pairing for channel assignment is preferable, whereas for NOMA, the proposed transmission-and-reflection scheme can achieve comparable performance to the exhaustive search-based algorithm. 2) The STAR-RIS-aided NOMA network significantly outperforms networks employing conventional RISs and OMA.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".