IRS Element Selection Using LSTM-Based Deep Learning for UAV Communications
Bibliographic record
Abstract
This letter proposes using deep learning (DL) for intelligent reflecting surface (IRS) element selection to reduce the bit error rate (BER) of unmanned aerial vehicle (UAV) communications affected by imperfect phase estimation and compensation. In the presence of phase errors, increasing the number of elements does not necessarily reduce the BER. In contrast, muting certain IRS elements can improve BER. However, solving the optimization problem heuristically is extremely complex because it requires evaluating BER expressions numerically for an enormous number of cases. Consequently, a long shortterm memory (LSTM)-based element selection (ES) technique is proposed to reduce the substantial complexity inherent in the conventional solution. A supervised learning approach with offline training is adopted where the decision of ES is made based on the phase estimation error parameter j. The obtained results show that the computation time of the proposed technique is 100 times less than that of state-of-the-art algorithms.
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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 | 0.000 |
| Open science | 0.003 | 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".