Rate-Splitting Multiple Access: Unifying NOMA and SDMA in MISO VLC Channels
Bibliographic record
Abstract
The increased proliferation of connected devices requires a development of innovative technologies for the next generation of wireless systems. One of the key challenges, however, is the spectrum scarcity, owing to the unprecedented broadband penetration rate in recent years. Based on this, visible light communication (VLC) has recently emerged as an effective potential solution for enabling high-speed short-range communications. Yet, despite their undoubted advantageous features, VLC systems suffer from several limitations which constraint their capabilities. As a result, several multiple access (MA) techniques, such as space-division multiple access (SDMA) and non-orthogonal multiple access (NOMA), have been considered in VLC networks as an effective approach, among others, to circumvent these limitations. However, despite their achievable multiplexing gain, their overall performance is still limited compared to the overall potential of this technology. Motivated by this, the presented article offers two contributions: firstly, we provide an overview of the key MA technologies used in VLC systems and then we introduce rate-splitting multiple access (RSMA), and discuss its capabilities and potentials in VLC systems. Secondly, through realistic system modeling and simulations of an RSMA-based two-user scenario, we illustrate the flexibility of RSMA as well as its superiority in terms of the achievable weighted sum rate over NOMA and SDMA in the context of VLC. Finally, we discuss technical challenges, open issues, and research directions, along with the offered results and insights that are expected to be useful towards the effective practical realization of RSMA in VLC configurations.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".