Pilot Insertion Rate for SC-FDE Systems Employing Subspace-Based Channel Estimation
Bibliographic record
Abstract
Blind or semi-blind channel estimation techniques, such as subspace decomposition, often use second order statistics which can provide channel estimates that are multiplied by an unknown complex constant, known as the ambiguity. Determination of this ambiguity can be achieved by the insertion of a small number of pilot symbols within a data block. The goal of this paper is to investigate the bit error rate performance of single carrier frequency domain equalization (SC-FDE) based transmissions using different pilot insertion rates, with the goal of determining the ambiguity without sacrificing spectral efficiency. Using Monte Carlo simulation, our results show, for the conditions presented in this paper, the insertion of 4 pilot symbols per 64 symbol block (or a ratio of 1 pilot per 15 information symbols) achieves a BER of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> with a power loss of 1dB or 1.5 dB (depending on the channel order) compared to a system with perfect ambiguity resolution.
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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) | 0.000 | 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 | 0.001 |
| 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 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".