Enhanced Channel Tracking in THz Beamspace Massive MIMO: A Deep CNN Approach
Bibliographic record
Abstract
In this paper, we propose a novel model-driven deep learning approach to improve the performance of the channel tracking process in terahertz (THz) massive MIMO (m-MIMO) systems. Specifically, a recently introduced a priori aided (PA) channel tracking scheme which exploits the kinematics of the mobile users, is first used to obtain preliminary estimates of the THz m-MIMO channel. Then, a deep convolutional neural network (DCNN) based on the visual geometry group network architecture (VGGNet) is employed to refine these estimates, where the DCNN is trained offline to learn strong features of the non-linear map between PA-based channel estimates and the true channels. Simulation results demonstrate that the proposed DCNN-based approach significantly outperforms its traditional counterpart in terms of normalized mean square error. The resulting gains in accuracy can be traded to reduce the pilot overhead or required signal-to-noise ratio (SNR).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".