Sparse channel estimation of pulse‐shaping multiple‐input–multiple‐output orthogonal frequency division multiplexing systems with an approximate gradient <i>l</i> <sub>2</sub> <i>−</i> <i>Sl</i> <sub>0</sub> reconstruction algorithm
Bibliographic record
Abstract
Most of the existing compressed channel‐sensing methods for multiple‐input–multiple‐output orthogonal frequency division multiplexing (MIMO‐OFDM) systems did not take into account the pulse‐shaping filter in the transmitter and matched filter in the receiver. However, these two filters are commonly used in digital communication systems. The compressed channel‐sensing problem of pulse‐shaping MIMO‐OFDM systems is first formulated. A new signal‐reconstruction algorithm in the compressed sensing framework is then proposed. The algorithm is based on minimising a smoothed l 0 ‐norm regularised least‐square (LS) ( l 2 − Sl 0 ) objective function, and the unconstrained optimisation involved is performed by an approximate gradient method. Further, the proposed l 2 − Sl 0 algorithm is applied to reconstruct the channel impulse response. A number of computer simulation‐based experiments are conducted, showing a better reconstruction accuracy of the l 2 − Sl 0 algorithm as compared with the smoothed l 0 ‐norm ( Sl 0 ) algorithm. The proposed channel estimation approach can save nearly 25% pilot signals to maintain the same mean square error and bit error rate performances as given by the conventional LS method.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".