AI-based Inter-Tower Communication Networks: First approach
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
Motivated by the need to offer large amounts of data, user interactivity, and other requirements to enhance user experience, digital TV standards like ATSC 3.0 have evolved significantly. Particularly, in the case of ATSC 3.0, In-band Distribution Link (IDL) and Inter Tower Communication Networks (ITCN) have been proposed, among other novelties. These technologies imply the implementation of In-Band Full-Duplex (IBFD) communications, which increase the overall network capacity but have to manage strong self-interference signals. In this paper, an artificial intelligence technique based on Convolutional Neural Networks (CNN) is proposed to perform the cleaning of the loopback channel estimation. Moreover, computer-based simulations have been carried out, and methods proposed in previous papers are compared to Neural Networks (NN). Results indicate that NNs show a greater cleaning capacity than the previously mentioned techniques.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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