Optimum decode-and-forward relay-assisted combining scheme with relay decision information
Bibliographic record
Abstract
Diversity combining is a form of spatial diversity which is of primary importance in wireless communications. Maximum Ratio Combining (MRC) is known as the best combining scheme because it effectively uses the Channel State Information (CSI) at the receiver in the combining process. However, in cooperative relay-based systems employing relay detection, MRC performance is limited by the fact that the CSI is insufficient to optimize the combining process due to hard decisions performed by some signaling methods at the relay such as Decode-and-Forward (DF). In this paper, we propose a new optimum combining scheme for DF cooperative systems in which the performance gain is achieved through the use of relay decision information along with the CSI in the combining process. The proposed optimum combining is a general scheme, where MRC and other combining schemes are considered to be special cases of optimum combining. Results show that optimum combining significantly improves the system throughput and reduces the error probability. Moreover, it enhances the system robustness due to the adaptivity of the combiner with the change in cooperative channel conditions and packet size.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".