A two-hop equalize-and-forward relay scheme in OFDM-based wireless networks over multipath channels
Bibliographic record
Abstract
Relay communications have attracted increasing research attentions as a cost-effective technique to improve spatial diversity, service coverage, and energy efficiency in wireless networks. However, existing relay schemes e.g., amplify-and-forward and decode-and-forward DF schemes still face several major challenges, particularly the accumulation of multipath channels effect in AF and long processing latency in DF. To address these issues, we propose a novel equalize-and-forward EF relay scheme to enhance the retransmission reliability while maintaining low processing delay at the relay node. In particular, the proposed EF relay estimates and equalizes the channel between source and relay to eliminate the channel accumulation effect without signal regeneration. To further reduce the relay processing time, the channel estimation and equalization in the proposed EF design are performed in parallel. The proposed equalization is realized by presetting the equalizer coefficients with the current channel response that is predicted in parallel using multiple past channel responses. Numerical results show that the proposed EF relay scheme can achieve comparable symbol error rate performance as the DF relay with much less relay latency. In addition, the EF relay exhibits low outage probability at the same data rate as compared with traditional amplify-and-forward and DF schemes. schemes. Copyright © 2015 John Wiley & Sons, Ltd
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".