# Adaptive Software Defined Equalization Techniques for Indoor Visible Light Communication
## 5 Simulations  
The simulation platform based on virtual instrumentation was developed to estimate the results of future measurements roughly. It consisted of multiple blocks from the NI Modulation Toolkit and Adaptive Filter Toolkit. The channel itself was modeled by inserting additive white Gaussian noise block (AWGN), which simulated impairments during laboratory measurements. The results were represented not only by constellation diagrams of the transmitted/received signal, but also by multiple parameters such as EVM, BER, phase error, magnitude error, etc. Three equalization techniques were tested: QR-RLS, LMS, and NLMS.  
Other than white Gaussian noise, the software was also capable of simulating the Rician fading channel or Rayleigh fading channel. These two were not tested during the first phase, but they will be useful for further measuring scenarios, such as propagation in a highly reflective environment. This software was developed as an extension of previously explored channel modeling applications [62]. Various aspects, which can be further analyzed, were thoroughly explained in the cited article.  
Simulations were carried out repeatedly for each modulation format/equalization technique combination, and the E\({}_{\rm b}\)/N\({}_{0}\) parameter of AWGN was gradually changed from 20 to 40 dB. BER, MER, and EVM parameters were used for evaluation. Modulation order of QAM was changed from 4-QAM to 64-QAM. Figures 7 and 8 represent the relationship between E\({}_{\rm b}\)/N\({}_{0}\) and MER. Figure 9 displays the relationship between EVM and E\({}_{\rm b}\)/N\({}_{0}\). Individual graphs more or less corresponded to the assumptions. Measured values corresponded to the decreasing E\({}_{\rm b}\)/N\({}_{0}\) since the signal itself was gradually influenced by noise. It is noticeable that equalization techniques significantly improved the MER values. They were advantageous mainly at 20 to 32 dB, where their deployment improved the reliability of the whole platform. However, their contribution was heavily influenced by the amount of noise. When the signal had higher E\({}_{\rm b}\)/N\({}_{0}\) values, the benefit of equalization rapidly decreased. In worst-case scenario (at 20 dB), the difference between the signal with and without the equalizer was more than 15 dB.  
The whole simulation used a bandwidth of 1 MHz. The used number of transmitted symbols mainly corresponded to the performance of the measuring computer. A tradeoff between simulation performance and measurement accuracy was chosen, as the number of transmitted symbols influenced the maximal measurable order of BER. By choosing 5000 symbols, the whole simulation was able to measure at least \(10^{-5}\) BER reliably. Anything higher than \(10^{-5}\) was therefore rounded to zero. Since  
Figure 6: EVMin part of the IQ diagram.  
the earlier publication covered the BER parameter simulations, therefore this was skipped in this article [62].  
In Figure 9, which compares measured EVM values, it is noticeable that starting from approximately 30-34 dB, the adaptive algorithms positively influenced the whole simulation. In the worst-case scenario, the difference between the equalized signal and the signal without the equalizer was approximately 14% for 4-QAM or 12% for 8-QAM. The difference between individual equalization techniques increased with the higher order of QAM modulation (4-QAM \(=1.4\%\), 16-QAM \(=3\%\)).  
Figure 8: Dependence of MER on \(\rm E_{b}/N_{0}\) for higher state M-QAM modulation formats.  
Figure 7: Dependence of MERon \(\rm E_{b}/N_{0}\) for lower state M-QAM modulation formats. NLMS, normalized least mean squares.  
The settings of the equalizer parameters can be seen in Table 1. These parameters were chosen based on extensive simulations similar to the experiments presented by [PERSON] et al. in 2017 and exhibited the best results across the board [63].