Channel Estimation, Equalization and Phase Correction for Single Carrier Underwater Acoustic Communications
Bibliographic record
Abstract
In this paper, we employ a time-domain channel estimation, equalization and phase correction scheme for single carrier single input multiple output (SIMO) underwater acoustic communications. In this scheme, Doppler shift, which is caused by relative motion between transducer (source) and hydrophones (receiver), is estimated and compensated in the received baseband signals. Then the channel is estimated using a small training block at the front of a transmitted data package, in which the data is artificially partitioned into consecutive data blocks. The estimated channel is utilized to equalize a block of received data, then the equalized data is processed by a group-wise phase correction before data detection. At the end of the detected data block, a small portion of the detected data is utilized to update channel estimation, and the re-estimated channel is employed for channel equalization for next data block. This block-wise channel estimation, equalization and phase correction process is repeated until the entire data package is processed. The receiver scheme is tested with experimental data measured at Saint Margaret's Bay, Nova Scotia, Canada, in May 2006. The results show that it can be applied not only to the scenario of fixed source to fixed receiver, but also to the moving source to fixed receiver case. The achievable uncoded bit error rate (BER) is on the order of 10-4for moving-to-fixed transmissions, and on the order of 10-5for fixed-to-fixed transmissions.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".