Performance of rank metric codes for interference constrained wireless sensor networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Future wireless communication systems will consist of multiple networks with various capabilities. Wireless networks may encounter severe distortions due to the presence of interfering signals generated at some power stations dedicated to smart grid applications. In fact, severe environmental effects of high voltage substations must be considered, particularly impulsive noise needs to be taken into account. Firstly, to cope with this kind of hostile environment, an efficient channel coding scheme in a mono‐user system is proposed, when one source user transmits data directly to one terminal user. Performance analysis shows that the proposed coding schemes based on rank metric codes are very efficient to eliminate impulsive noise in mono‐user mode. Furthermore, this approach are expanded in order to show the efficiency of rank codes in Wireless Sensors Networks (WSNs), when considering the problem of collecting data in WSNs in the presence of impulsive noise errors together with AWGN channel. To increase the reliability of the system, an advanced Network Coding technique (NC) is applied based on LRPC (Low Rank Parity Check) codes which exhibits noteworthy performances.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it