On the Detection of Distributed STBC AF Cooperative OFDM Signal in the Presence of Multiple CFOs
Bibliographic record
Abstract
This paper deals with the interference mitigation problem of the distributed space time block coded (STBC) amplify-and-forward (AF) cooperative orthogonal frequency division multiplexing (OFDM) signal in the presence of carrier frequency offsets (CFO). Multiple CFOs introduce phase drift, inter-carrier interference (ICI) and inter-block interference (IBI) to the received signal. A joint time domain (TD) and frequency domain (FD) method is presented to recover the phase distortion and mitigate the ICI and IBI with low complexity and high performance. The TD compensation proposed in this paper removes the IBI first and then the ICI is mitigated through FD equalization (FDE) methods. For the FDE, the minimum mean square error (MMSE) equalizer has been derived which has a different noise covariance matrix from the conventional MMSE. Sub-block processing is employed to reduce the computational complexity in FD equalization. Furthermore, a two-pilot-block assisted channel estimation method is proposed for AF cooperative OFDM in the presence of CFOs. The bit-error-rate (BER) performance evaluated via computer simulation demonstrates the effectiveness of the proposed method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".