Cooperative OFDM Channel Estimation in the Presence of Frequency Offsets
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-3, and this gap increases to about 11.3 dB when the variance increases to 10-2.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".