Distributed space-time coding for multihop transmission in power line communication networks
Bibliographic record
Abstract
In this paper, we consider transmission in relatively wide-stretched power line communication (PLC) networks, where repeaters are required to bridge the source-to-destination distance. In particular, it is assumed that each network node is a potential repeater and that multihop transmission is accomplished in an ad hoc fashion without the need for complex routing protocols. In such a scenario, due to the broadcasting nature of the power line channel, multiple repeater nodes may receive and retransmit the source message simultaneously. It is shown that, if no further signal processing is applied at the transmitter, simultaneous retransmission often deteriorates performance compared with single-node retransmission. We therefore advocate the application of distributed space-time block codes (DSTBCs) to the problem at hand. More specifically, we propose that each network node is assigned a unique signature sequence, which allows efficient combining at the receiver. Most notably, DSTBC-based retransmission does not require explicit collaboration among network nodes for multihop transmission and detection complexity is not increased compared with single-node retransmission. Numerical results for multihop transmission over PLC networks show that DSTBC-based retransmission achieves a considerably improved performance in terms of required transmit power and multihop delay compared with alternative retransmission strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".