Deep Reinforcement Learning for Robust RIS-Aided Over-the-Air Federated Learning in Cognitive Radio
Bibliographic record
Abstract
With the aim of exploring the integration of federated learning (FL) into wireless networks, particularly in light of the challenge of spectrum resource limitations, this paper considers the integration of over-the-air FL (OTA-FL) and cognitive radio networks (CRN) in the presence of uncertainties in channel gains. Furthermore, this study proposes the utilization of reconfigurable intelligent surface (RIS) to enhance OTA-FLwithin the secondary network (SN) of CRN, leveraging RIS capabilities to mitigate interference to the primary network (PN). Our focus lies in minimizing the mean squared error (MSE) of model aggregation, considering the uncertainty in channel estimation and total interference constraints. We formulate this optimization challenge as a Markov decision process (MDP) and exploit a deep reinforcement learning (DRL)-based approach to effectively address this complex problem. Finally, simulation outcomes validate the outperform of the proposed approach.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".