# The Analysis of Service Convergence in an Optical Access Network +
## 5 Conclusions  
In this work, the physical implementation of an RoF system in the context of 5G mobile networks was achieved to analyze the UFMC and GFDM waveforms using BER and EVM metrics with M-QAM modulation. Signal generation and processing were performed using Matlab, while the optical channel was set up with equipment and components available in the laboratory. The UFMC and GFDM signals were transmitted individually under 4/16/64/256-QAM modulation schemes, with the power level varied using a variable attenuator within the range of power levels acceptable for the photodetector. The results demonstrate that as optical reception power increases, the BER decreases for both waveforms. Moreover, higher modulation orders lead to greater signal degradation, reducing the rate of BER improvement. The EVM exhibits a similar behavior to the BER but tends to stabilize as power increases. Notably, in all cases, the UFMC provides better performance than GFDM in both the BER and EVM.  
In the evaluation of service convergence within the optical network, significant results were observed regarding optical reception power for both LTE and UFMC signals. The simulation demonstrated that the system effectively enables the transmission of both signals through the fiber optic channel, achieving a reception power of \(-30.987\) dBm for the UFMC and \(-26.987\) dBm for LTE, while maintaining a bit error rate (BER) within acceptable limits  
Figure 6: Rx optical power vs. bit error rate from UFMC.  
for service quality. These results highlight the system's capability to handle multiple high-demand services without compromising signal integrity, thereby demonstrating improved efficiency and performance in converged access networks. This optimized performance underscores the potential of optical networks to support the growing demands for data and connectivity in modern applications, paving the way for broader implementation of 5G technologies in existing networks.  
The following supporting information can be downloaded at: [[https://github.com/Davosin1404/Convergencia_OptSim-Matlab](https://github.com/Davosin1404/Convergencia_OptSim-Matlab)]([https://github.com/Davosin1404/Convergencia_OptSim-Matlab](https://github.com/Davosin1404/Convergencia_OptSim-Matlab)), [[https://github.com/ErickCifuentes01/TIC-2024.git](https://github.com/ErickCifuentes01/TIC-2024.git)]([https://github.com/ErickCifuentes01/TIC-2024.git](https://github.com/ErickCifuentes01/TIC-2024.git)).  
Conceptualization, C.T., E.C. and D.M.; methodology, E.C. and D.M.; software, D.M and B.A.; validation, C.T. and B.A.; formal analysis, E.C. and D.M.; investigation, E.C. and D.M.; resources, B.A and G.V.A.; data curation, E.C. and D.M.; writing--original draft preparation, E.C. and D.M.; writing--review and editing, E.C. and D.M.; visualization, E.C. and D.M.; supervision, C.T. and B.A.; project administration, C.T., B.A., G.V.A., E.C. and D.M.; funding acquisition, E.C. and D.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data are contained within the article and Supplementary Materials.  
The authors declare no conflicts of interest.