VLC in Future Heterogeneous Networks: Energy– and Spectral–Efficiency Optimization
Bibliographic record
Abstract
Energy efficiency (EE) and spectral efficiency (SE) have been identified as key performance indicators in the design of future cellular networks. However, the available radio frequency (RF) spectrum is becoming highly saturated, thus making it difficult for network operators to achieve significant throughput and SE enhancement without increasing their power consumption. To that end, exploiting the abundant unlicensed spectrum in the visible light band to complement RF communication has become an important research direction in the design of wireless systems. Visible light communication (VLC) combines illumination and communication while significantly reducing the power consumption and related carbon footprint of wireless systems. This paper investigates the introduction of a VLC system in a two-tier RF heterogeneous network (HetNet). The EE and SE performance of the resulting three-tier HetNet is investigated, and a novel energy efficient resource allocation scheme is proposed. More specifically, the joint problem of user association and power control to maximize the EE is formulated as a fractional programming problem under the transmit power and quality-of-service requirements constraints. To tackle the nonconvexity of the problem, the original EE problem is first transformed into a parametric subtractive form. Then, the joint problem is separated into a user association and power control sub-problems. An efficient iterative algorithm is proposed to solve these two sub-problems, alternately. The performance of the proposed algorithm in terms of total network throughput, EE, and SE for different user densities is verified using simulation results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".