# Experimental Measurements of a Joint 5G-VLC Communication for Future Vehicular Networks
## V Advanced Viability Experimental Activity
### Experimental Results  
The performance of the overall joint network is measured by the latency metric. The results are taken by an oscilloscope connected at the first raspberry (yellow line in Figures 5 and 6), at the second Raspberry (purple line) and at the VLC receiver (green line). The yellow line, when high, represents an alarm trigger, the rising edge in the pink line represents the time needed to forward the packet on the USB cable, and the green line represents the packet arrived at the VLC receiver. Figure 4: Test-bed architecture of the 5G-VLC joint network. Figures 5 and 6 show the maximum and the minimum measured end-to-end latency time. With end-to-end we intend here the time interval from the generation of one packet by the sensors to the correct reception of the packet by the VLC receiver in the car. The rectangle named a in Figures 5 and 6 shows the 5G latency time, while the rectangle named b reports the VLC latency time. The portion between the two rectangles represents the processing time introduced by the third raspberry Pi3. Figures 7 and 8 show the distribution of the latency for the 5G segment and for the overall system (5G link and VLC link), respectively. The latency was calculated over a transmission of 2250 packets. The most frequent latency for the 5G link is about 9.5 ms, while for the VLC link is about 2.5 ms. The 5G network latency time ranges from 2.4 to 29 ms, while the VLC network latency time varies from 2.4 to 3.1 ms. This time describes the data transmission time through the optical channel at the rate of 100 kbps, as defined by the IEEE 802.15.7 standard for outdoor applications. The processing time of the second raspberry Pi 3 is highly stable, and it is equal to a few hundreds of \(\mu\)s.  
Figure 5: Maximum measured end-to-end latency. Figure 6: Minimum measured end-to-end latency. The best fitting distribution of the latency for both the 5G link and overall system has been derived by using the Bayesian information criterion (BIC) [24]. Seventies different distributions have been evaluated as fitting models for the empirical data, and the distribution that minimizes the BIC was selected. For both the 5G link and the overall system, the distribution that best fits the empirical data is the t-location scale whose probability density function (PDF) \(f(x|\mu,\,\sigma,\,\
u)\) is:  
\[f(x|\mu,\,\sigma,\,\
u)=\frac{\Gamma\left(\frac{\
u+1}{2}\right)}{\sigma\sqrt{ \
u\pi\Gamma\left(\frac{\
u}{2}\right)}}\Bigg{[}\frac{\
u+\left(\frac{\
u-\mu} {\sigma}\right)}{\
u}\Bigg{]}^{-(\frac{\
u+1}{2})} \tag{2}\]  
where \(\Gamma(\,)\) is the gamma function, \(\mu\) is the location parameter, \(\sigma\) is the scale parameter, and \(\
u\) is the shape parameter. The best fit t-location scale parameters [\(\mu,\,\sigma,\,\
u\)] for the 5G link are [0.0088, 7.43 \(\times\) 10\({}^{-4}\), 1.09], while for the overall system are [0.0119, 0.001, 1.253]. Table 2 summarizes the results. Figure 8: Empirical distribution of the latency of the overall system (5G link and VLC link) and PDFs of the fitting distributions. Figure 7: Empirical distribution of the latency of the 5G link only and PDFs of the fitting distributions. Figures 9 and 10 show the cumulative distribution function (CDF) of the four best fitting distribution models for the 5G link and for the overall system, respectively. As it can be seen, the CDF error is minimized by the t-location scale distribution in both cases. Although the NSA-5G network shows a very low latency compared to previous generations, we can assert that the longest part of the end-to-end latency time of the joint 5G-VLC network is introduced by the 5G part due to the packet error rate (PER) optimization. The 5G network provider has implemented techniques to reduce the PER at the 5G receiver. This means that a redundancy of the transmitted packet is introduced, during the network set up phase, to obtain PER = \(10^{-6}\) at the receiver