Deep Learning for Compressed Sensing Based Channel Estimation in Millimeter Wave Massive MIMO
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Bibliographic record
Abstract
Channel estimation is considered for multi-user millimeter wave (mmWave) massive multi-input multi-output system. A deep learning compressed sensing (DLCS) channel estimation scheme is proposed, and it consists of beamspace channel amplitude estimation and channel reconstruction. The neural network (NN) for the DLCS scheme is trained offline using simulated environments according to the mmWave channel model. Then the correlation between the received signal vectors and the measurement matrix is input into the trained NN to predict the beamspace channel amplitude. Afterwards, the channel is reconstructed based on the obtained indices of dominant beamspace channel entries. Simulation results demonstrate that the proposed DLCS channel estimation scheme outperforms the existing schemes including the orthogonal matching pursuit and the distributed grid matching pursuit in terms of the normalized mean-squared error and the spectral efficiency.
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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 | 0.000 |
| 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