Multiple CFO Mitigation in Amplify-and-Forward Cooperative OFDM Transmission
Bibliographic record
Abstract
In cooperative orthogonal frequency division multiplexing (OFDM) systems, accurate frequency synchronization is critical to achieving any potential gains brought by the cooperative operation. The carrier frequency offsets (CFOs) present among multiple nodes (source, relays and destination) are more difficult to tackle than the single CFO problem in point-to-point systems. Multiple CFOs cause phase drift, inter-carrier interference (ICI) and inter-block interference (IBI) in the received signal. This paper deals with the CFO induced interference mitigation problem in distributed space time block coded (STBC) amplify-and-forward (AF) cooperative OFDM systems. We propose a two step approach to recover the phase distortion and suppress the ICI and IBI using low complexity methods to achieve high performance. The first step is time domain (TD) compensation and the second step is frequency domain (FD) decoding. Two TD compensation schemes are proposed, i.e., IBI-removal and ICI-removal. The IBI-removal scheme decouples the two blocks of one STBC codeword completely and then decodes the ICI degraded blocks individually. The ICI-removal scheme removes ICI first and the subsequent decoding requires joint decoding of the two blocks. Simulation results show that the IBI-removal scheme which is of lower complexity performs well with small CFO. For large CFO, the ICI-removal with modified iterative joint maximum likelihood decoding (MIJMLD) outperforms other schemes.
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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 | 0.002 |
| 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 | 0.001 | 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".