SE-Attention Enhanced Sensing-Aided CSI Feedback for mMIMO Systems
Why this work is in the frame
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Bibliographic record
Abstract
Massive multiple input multiple output (mMIMO) systems require accurate channel state information (CSI) feedback but face high overhead. Existing deep learning methods ignore environmental information, while sensing-assisted frameworks reduce dimensionality at the cost of noise amplification in channel reconstruction. To address this, we enhance the RENet recovery network and propose RENet+, which incorporates a squeeze-and-excitation (SE) attention mechanism to adaptively recalibrate channel features. This design suppresses redundant components while emphasizing critical angular-spread information. As the first work integrating SE attention into sensing-aided CSI recovery, the proposed method significantly improves reconstruction accuracy under low feedback overhead. Evaluated on Saleh-Valenzuela channel models with 5 scatterers, the jointly trained JNet-Joint+ achieves NMSE gains of 4.7 dB and 6.4 dB over CsiNet at compression ratios (CR) of 64 and 128, respectively, and outperforms the original JNet-Joint by 0.9 dB (CR=64) and 0.7 dB (CR=128).
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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