Hybrid NOMA and ZF Pre-Coding Transmission for Multi-Cell VLC Networks
Bibliographic record
Abstract
Though visible-light communication (VLC) channels are contained by opaque boundaries, they present unique challenges in the development of multi-user/multi-cell scenarios. In this paper, two hybrid transmission schemes are proposed for managing multiple users in multi-cell VLC networks. The proposed schemes are based on using non-orthogonal multiple access (NOMA) in the network access points (APs), while applying zero-forcing (ZF) pre-coding to the cell edge users' signals, which are cooperatively broadcast from the APs. The proposed approach allows a reduction of the inter-cell interference affecting the cell-edge users thanks to ZF pre-coding, while dealing with inter-user interference for cell-center users via NOMA signaling. Considering different transmission scenarios, we show the improvement in the network total achievable data rate as well as fairness, as compared to conventional NOMA. For example, for a typical scenario considered, an improvement of up to 39% in total achievable rate and up to 112% in the network fairness is achieved. The proposed approach also presents a clear advantage over the conventional ZF pre-coding, for which the maximum number of users is constrained to the number of APs.
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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 | 0.002 |
| 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 | 0.001 | 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".