Sparse inverse fast Fourier transform‐based channel estimation for millimetre‐wave vector orthogonal frequency division multiplexing systems
Bibliographic record
Abstract
Millimetre‐wave propagation is a promising broadband transmission technology for future fifth generation mobile communication systems. For a vector orthogonal frequency division multiplexing system, the authors investigate the millimetre‐wave propagation through a sparse multipath channel in a sense that it has a large time delay spread but with only a few non‐zero taps. By exploiting the sparse nature of millimetre‐wave channel, any sparse multipath channel can be characterised by the multipath delays and their corresponding channel coefficients. They first study an ideal case that the pilot signals are transmitted through a sparse channel without noise, and an exactly sparse inverse fast Fourier transform (SIFFT) algorithm is performed to estimate the non‐zero channel taps with reduced complexity. Then, they consider a more practical scenario that the pilot signals through a sparse channel with noise interference, and an approximately SIFFT algorithm is employed to estimate the effective channel taps, while the remaining small coefficients interfered by noise can be wiped out. Through numerical analysis, they demonstrate that the proposed SIFFT algorithms can reduce the computational complexity while keeping the root mean squared error of channel estimation at a low level.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 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".