Hierarchical Sparse Estimation of Non-Stationary Channel for Uplink Massive MIMO Systems
Bibliographic record
Abstract
This paper proposes a hierarchical sparse estimation of spatial non-stationarity channel for uplink massive multiple-input multiple-output (MIMO) systems without prior information. Especially, the non-zero rows of non-stationarity channel matrix are estimated according to the in-row correlation in the first layer; while the non-zero elements of the estimated non-zero rows are further refined in the second layer. A row-wise sparse adaptive matching pursuit (SAMP) is used to find the non-zero rows in the first layer of the proposed algorithms, and multiple non-zero rows can be estimated in one iteration, which has higher precision and lower complexity, compared to the conventional SAMP. Different from the existing two-layer iteration algorithms, a threshold is designed to estimate the non-zero elements replacing the iterative algorithm in the second layer. Further, the computation complexity is analyzed and compared. The simulation results demonstrate that the proposed threshold-enhanced hierarchical spatial non-stationary channel estimation algorithms achieve better performance compared to various state-of-the-art baselines in terms of channel coefficient estimation, and computational efficiency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".