Rate-Splitting Multiple Access Scheme Based on Frequency Diverse Array
Bibliographic record
Abstract
A rate-splitting multiple access (RSMA) scheme based on frequency diverse array (FDA) is proposed in this letter. With the fixed frequency offsets, the proposed scheme adopts the 1-layer rate-splitting (RS) strategy to cancel the intra-group interference when multiple users exist within a user group. Based on the proposed scheme, a max-min fairness optimization problem (OP) is formulated to maximize the minimum achievable rate among users, where the user grouping, digital precoding matrix, and RS vector are jointly optimized. A suboptimal two-step algorithm is proposed to solve the above OP. First, a K-means-based user grouping method is designed by using the location information of each user. Second, given the user grouping result, the OP is reformulated and solved by the semidefinite relaxation method. Simulation results illustrate that the designed user grouping method is effective in the FDA-based wireless communication system, and RSMA can better cancel interference than space division multiple access and non-orthogonal multiple access.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".