Deep-Learning-Assisted Channel Estimation for Adaptive Parameter Selection in mMIMO-SEFDM
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Bibliographic record
Abstract
This article introduces a massive multiple-input—multiple-output (mMIMO) system that utilizes spectrally efficient frequency division multiplexing (SEFDM) and incorporates a deep neural network (DNN) for enhanced SEFDM channel estimation. Unlike existing studies on DNN-based channel estimation, this research employs estimated channel feedback to dynamically adjust SEFDM signal characteristics at the transmitter, thereby improving the system’s adaptability. This adaptive mechanism optimizes the SEFDM compression value and modulation order based on real-time channel conditions, significantly enhancing the symbol error rate (SER). Detailed simulations demonstrate that higher modulation techniques experience substantial performance degradation with increased subcarrier compression in SEFDM. The proposed DNN-based channel estimation and adaptive parameter selection outperform traditional linear schemes, utilizing a more stable SEFDM system to achieve significant spectral efficiency (SE) compared to conventional orthogonal frequency division multiplexing (OFDM).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it