Novel retransmission scheme for energy harvesting transmitter and receiver
Bibliographic record
Abstract
We consider a point-to-point wireless link with automatic repeat request (ARQ) based packet transmission where both the transmitter and receiver nodes are energy harvesting (EHNs). Transmitter EHN has access to low-grade channel state information (CSI) as it is implicitly obtained from ARQ feedback. Furthermore, signal processing tasks such as sampling and decoding at the receiver EHN can be interrupted if there is insufficient energy in the battery that cause loss of packet and wastage of harvested energy both at the transmitter and receiver EHNs. We propose selective sampling (SS) scheme where only part of the transmitted packet is sampled and stored depending on the receiver nodes stored energy. SS information (SSI) is then fed back to the transmitter. Packet decoding is not performed until full packet is constructed. Hence, we modify the conventional ARQ messages, i.e., ACK/NAK by adding few more bits to carry additional SSI as well. Another objective is to find the optimal power allocation policy to adapt to the low-grade CSI and SSI available at the transmitter such that harvested energy can be utilized efficiently especially at the receiver. Furthermore, using a decision-theoretic framework, we propose greedy power allocation scheme to evaluate the performance of the proposed retransmission scheme. In numerical examples, we illustrate that our proposed scheme has lower average packet transmission time and packet drop probability (PDP) compared to the equal power allocation and greedy power allocation with conventional retransmission scheme.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".