Noncoherent Distributed Beamforming in Decentralized Two-Way Relay Networks
Bibliographic record
Abstract
Many noncoherent distributed strategies for cooperative sensor networks that do not require channel knowledge at any antenna to overcome the overhead involved in channel estimation are lately suggested; however, these strategies suffer from low system performance in terms of bit error rate (BER) and a comparably high decoding complexity. Differential beamforming strategies have recently been proposed to overcome these problems; however, they are implemented using the four-phase protocol. Thus, we propose a new strategy based on the three-phase protocol to increase the symbol rate. By doing this, a significant improvement can be achieved in the overall system performance. Hence, in this article, a new bidirectional differential beamforming strategy is suggested: 1) to be applied on the three-phase protocol instead of the four-phase protocol; 2) to be applicable for a decentralized wireless sensor network using single-antenna sensors distributed randomly between the communicating base stations; 3) to enjoy low decoding complexity; and 4) to improve the network performance in terms of BER by maximizing the received signal-to-noise ratio at the receiving base station without requiring channel knowledge at any antenna in the whole network. From our simulation results, the proposed strategy shows a substantially improved BER performance compared with the current state-of-the-art ones.
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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 | 0.001 |
| 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".