Fluid Antenna-Assisted Uplink NOMA Networks Under Imperfect SIC
Bibliographic record
Abstract
This paper investigates the integration of fluid antennas (FAs) into uplink non-orthogonal multiple access networks suffering from imperfect successive interference cancellation (SIC). The dynamic reconfigurability of FAs offers significant potential for mitigating interference and enhancing network performance by adapting antenna positions in response to changing channel conditions. In this study, we propose a joint optimization framework to maximize the system's sum rate by optimizing key parameters, including FA positions, beamforming vector at the base station, and transmit power allocation for each user. The problem is formulated as a non-convex optimization task and solved using a new deep reinforcement learning (DRL)-based framework. The proposed DRL model incorporates a structured exploration strategy and reward shaping to efficiently learn optimal policies for resource allocation and antenna positioning in dynamic environments. Extensive simulations validate the effectiveness of the proposed approach, demonstrating that integrating FAs significantly improves the sum rate, particularly in scenarios with imperfect SIC.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".