Cooperative OFDM Channel Estimation with Frequency Offsets
Bibliographic record
Abstract
This paper discusses channel estimation in a cooperative orthogonal frequency-division multiplexing (OFDM) network in the presence of frequency offsets. Both the amplify- and-forward (AF) and decode-and-forward (DF) relaying modes are analyzed. In order to eliminate the multiple access interference (MAI), the maximum number of active AF and DF relays are [N/2L-1] and [N/L], respectively, where N is the total number of subcarriers, L is the channel order and [a] is the maximum integer part of a. The pairwise error probability (PEP) of orthogonal space-time coding in cooperative OFDM due to both the frequency offset and channel estimation errors is also evaluated. The optimal power allocation ratio between the source and the set of the relays to minimize the PEP is derived for both the relay modes. 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".