Cooperative OFDM Channel Estimation in the Presence of Frequency Offsets
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Bibliographic record
Abstract
Channel estimation in the presence of frequency offsets is developed for cooperative orthogonal frequency-division multiplexing (OFDM) systems. A two-time-slot cooperative channel estimation protocol is proposed. The source broadcasts the training sequence to the relays and the destination (first time slot), and the relays retransmit the training sequence (second time slot). Pilot designs for amplify-and-forward (AF) and decode-and-forward (DF) relays are derived. These designs eliminate interrelay interference (IRI), which occurs due to the simultaneous relay retransmissions, and minimize the mean square error (MSE). Consequently, the number of AF and DF relays is constrained to be less than lfloorN/(2L - 1)rfloor and lfloorN/Lrfloor, respectively, where N is the total number of subcarriers, L is the channel order, and lfloorarfloor is the maximum integer part of alpha. The pairwise error probability (PEP) of orthogonal space-time coding in cooperative OFDM due to both frequency offset and channel-estimation errors is also evaluated. The optimal power allocation ratio between the source and the relays to minimize the PEP is derived for AF and DF relays. When L < 16, DF relays outperform AF relays in terms of PEP. With L = 4 and 16 active relays, the gap is 9 dB for a frequency offset error variance of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> , and this gap increases to about 11.3 dB when the variance increases to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> .
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it