On the performance of adaptive hybrid MQAM–MPPM scheme over Nakagami and log-normal dynamic visible light communication channels
Bibliographic record
Abstract
In this paper, we introduce the idea of using adaptive hybrid modulation techniques to overcome channel fading effects on visible light communication (VLC) systems. A hybrid <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>M</mml:mi> </mml:math> -ary quadrature-amplitude modulation ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>M</mml:mi> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">Q</mml:mi> <mml:mi mathvariant="normal">A</mml:mi> <mml:mi mathvariant="normal">M</mml:mi> </mml:mrow> </mml:math> ) and multipulse pulse-position modulation (MPPM) technique is considered due to its ability to make gradual changes in spectral efficiency to cope with channel effects. First, the Zemax optics studio simulator is used to simulate dynamic VLC channels. The results of Zemax show that Nakagami and log-normal distributions give the best fitting for simulation results. The performance of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>M</mml:mi> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">Q</mml:mi> <mml:mi mathvariant="normal">A</mml:mi> <mml:mi mathvariant="normal">M</mml:mi> </mml:mrow> </mml:math> –MPPM is analytically investigated for both Nakagami and log-normal channels, where we obtain closed-form expressions for the average bit-error rate (BER). The optimization problem of evaluating the hybrid modulation technique settings that lead to the highest spectral efficiency under a specific channel status and constraint of outage probability is formulated and solved using an exhaustive search. Our results reveal that the adaptive hybrid scheme improves system spectral efficiency compared to ordinary QAM and MPPM schemes. Our results reveal that the adaptive hybrid scheme improves system spectral efficiency compared to ordinary QAM and MPPM schemes. Specifically, at low average transmitted power, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mo>−</mml:mo> <mml:mn>32</mml:mn> <mml:mspace width="thickmathspace"/> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">d</mml:mi> <mml:mi mathvariant="normal">B</mml:mi> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> </mml:math> , the adaptive hybrid scheme shows 280% improvement in spectral efficiency compared to adaptive versions of ordinary schemes. At higher power, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mo>−</mml:mo> <mml:mn>20</mml:mn> <mml:mspace width="thickmathspace"/> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">d</mml:mi> <mml:mi mathvariant="normal">B</mml:mi> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> </mml:math> , 6.5% and 725% improvement are obtained compared to ordinary QAM and ordinary MPPM, respectively. Also, the adaptive hybrid scheme shows great improvement in average BER and outage probability compared to ordinary schemes. The hybrid scheme shows 28%, 34%, and 38% improvement, respectively, for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>m</mml:mi> <mml:mo>=</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn> <mml:mo>,</mml:mo> <mml:mn>3</mml:mn> </mml:math> for Nakagami channels at <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">B</mml:mi> <mml:mi mathvariant="normal">E</mml:mi> <mml:mi mathvariant="normal">R</mml:mi> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>=</mml:mo> <mml:msup> <mml:mn>10</mml:mn> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>−</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> . Also, the outage probability of hybrid schemes of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">B</mml:mi> <mml:mi mathvariant="normal">E</mml:mi> <mml:mi mathvariant="normal">R</mml:mi> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>=</mml:mo> <mml:msup> <mml:mn>10</mml:mn> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>−</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> shows 30% and 14% better performance than ordinary <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>M</mml:mi> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">Q</mml:mi> <mml:mi mathvariant="normal">A</mml:mi> <mml:mi mathvariant="normal">M</mml:mi> </mml:mrow> </mml:math> and MPPM schemes, respectively.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".