How much can we gain by exploiting buffers in wireless relay networks?
Bibliographic record
Abstract
Wireless relays will play an important role in future wireless communication networks. This talk will focus on the new concept of buffer-aided relaying. In conventional relay protocols, the schedule of when the different nodes in the network transmit is pre-fixed and non-adaptive. In contrast, buffer-aided relaying protocols exploit the additional degrees of freedom introduced by relays with buffers and employ an adaptive transmission schedule which takes into account the quality of the different links in the network. We will show that this new approach leads to substantial performance improvements in relay networks with fading links. In particular, buffer-aided relays enable significant gains in throughput as well as outage and error probability at the expense of an increased delay. These gains are introduced by adaptive link selection and/or adaptive transmission mode selection. We will first introduce the basic concept of buffer-aided relaying using the example of a simple three node one-way relay network before considering more complex networks such as relay-selection networks, multi-antenna relay networks, and two-way relay networks.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.021 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".