# A Symbolic Encapsulation Point as Tool for 5G Wideband Channel Cross-Layer Modeling
## 6 Conclusions  
In this work, usage of second-order statistics, namely LCR and AFD, as metadata to QoS modeling were emphasized. By using complex envelope signal samples collected from a small travel route as input, we achieved the generating of enough statistics to describe the non-stationary nature for different types of mobile channel, showing generality of approach. Present general channel 5G models can benefit from this approach by extending their missing features for dual-mobility or high-mobility cases. With the extension to wideband case, it is more clear how observed mobility affects different bandwidth parts, which optimization engineers can utilize for adaptive modulation  
Figure 8: Evaluation results for second-order statistics in V2V crossroad scenario: (**a**) Case A passband LCR; (**b**) Case B passband AFD; (**c**) Case B passband LCR; (**d**) Case B passband LCR; (**e**) Case C passband LCR; (**f**) Case C passband LCR.  
and coding schemes, resource blocks interpolating, optimal buffering, transport block size calculating, and other mechanisms for gaining targeted throughput, packet error rate, latency, etc. Using the presented 5G tool roadmap, we simplified complexity of calculations, reducing simulation time with an acceptable amount of accuracy. By means of microsimulations and their encapsulation through symbolic representation, symbolic encapsulation point manifested as a promising tool for a kind of cross-layer network design and optimization.  
Directions of further investigations are seen in integration of the symbolic encapsulation point tool into existing general channel 5G models with sophisticated propagation mechanisms (map-based) and applied 5G technologies to provide more accurate data and confirmation by measurements. Another option for work is extension to symbolic encapsulation of Markov chains and that way, through cross-layer modeling, an approach to wanted quality of service parameters evaluation in a dynamically changing wireless environment. This implicates reduced time for network planning and optimizing, a smaller number of (real-time) fading measurements, and field mobility testing, often connected with high costs and ability to evaluate user-experience data in a dynamic channel environment.  
Methodology and developed algorithms; N.S. and V.M.; data analysis, M.B. and Y.C.; testing and validation, I.P. and M.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia grant number 451-03-68/2020-14/200132.  
This paper is supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia, and these results are part of the Grant No. 451-03-68/2020-14/200132 with University of Kragujevac-Faculty of Technical Sciences Cacak.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.