Improved OFDMA uplink Frequency offset Estimation via Cooperative Relaying: AF or DcF?
Bibliographic record
Abstract
This paper evaluates the performance improvement in orthogonal frequency-division multiplexing access (OFDMA) uplink frequency offset estimation achieved with cooperative relaying. The transmission of each source node (node S) can be improved by optimizing the diversity gain through exploiting the cooperation of the other nodes (cooperative relays), and the relays can operate in either the amplify-and-forward (AF) or decode-and-compensation-and-forward (DcF) mode. One or more than one geographically closely located mobile nodes comprise a cooperative group (CG), and the nodes of the same CG cooperate with each other. In each transmission, the role of the relay is to "help" the source node transmit its training sequence, or, in other words, the relay creates a parallel route between the source node and the destination terminal to improve the reliability of the transmission of S. In the proposed cooperative scheme, the total power used to transmit each training sequence, including that consumed in node S and the relay, is kept constant. Based on the interference analysis, the signal-to-interference-plus-noise ratio (SINR) in both the relay and the destination terminal are derived. The AF mode's cooperative scheme always outperforms that of the DcF mode in terms of frequency offset estimation accuracy due to the estimation error propagation in the latter.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".