DRL-Based User Fairness in Beyond Diagonal Reconfigurable Intelligent Surface-Assisted Extremely Large Antenna Array Systems
Bibliographic record
Abstract
This paper investigates the user fairness for multi-user downlink communications systems with beyond diagonal-reconfigurable intelligent surface (BDRIS) and extremely large antenna array (ELAA), which takes advantage of interconnected elements and spherical wavefront characteristics in the near field. We formulate a user fairness-oriented optimization problem and develop a deep reinforcement learning (DRL)-based algorithm to effectively maximize fairness among users. The proposed framework provides a fast converging solution and simultaneously designs the transmit beamformers of the ELAA and the reconfiguration matrix of the BDRIS. Simulation results show that the proposed system provides better performance with perfect and imperfect channel state information (CSI) than typical DRL algorithms and improves the min-rate performance by 3.86% and 13.6%, compared to the benchmark ELAA systems with the conventional reconfigurable intelligent surface (RIS) and without RIS, respectively.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".