Achievable Rate of the Half-Duplex Multi-Hop Buffer-Aided Relay Channel With Block Fading
Bibliographic record
Abstract
The half-duplex (HD) multi-hop relay channel consists of a source, multiple HD relays connected in series, and a destination where links are present only between adjacent nodes. In this paper, we focus on decode-and-forward relays and assume that the links are impaired by block fading and additive white Gaussian noise. We design a new protocol which, unlike the conventional protocols for the multi-hop relay channel, does not adhere to a fixed and predefined pattern of using the transmit, receive, and silent states of the nodes. In particular, the proposed protocol selects the optimal states of the nodes and the corresponding optimal transmission rates based on the instantaneous channel state information (CSI) of the involved links in each fading block such that the achievable average rate from source to destination is maximized. To enable adaptive scheduling of the states of the nodes, the relay nodes have to be equipped with buffers for temporary storage of the information received from the preceding node. Additionally, we discuss and address two practical challenges arising in the implementation of the optimal protocol, namely the unconstrained end-to-end delay due to data buffering at the relays and the required CSI overhead. Numerical results confirm the superiority of the proposed buffer-aided protocols compared to existing multi-hop relaying protocols.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".