Fluid Antenna-Assisted Uplink NOMA Networks Under Imperfect SIC
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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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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 it