# A Model-Based Engineering Approach for Evaluating Software-Defined Radio Architecture
## 6 Conclusions and Future Work  
This work demonstrates the integration of multi-disciplinary engineering models into a comprehensive MBE approach which was used to enable engineers to make informed decisions and assess various architecture alternatives early in the development process. In addition to integrating engineering models, an MCDM analysis was performed to determine the best system architecture that meets functional requirements, technical performance, and design constraints. The AHP was used to evaluate and prioritize options based on assessment criteria. Three SMEs were asked to participate in the pairwise comparison of the criteria and alternatives, which helped to reduce bias and improve the accuracy of the results. Architecture 1 had limitations in robustness due to its single-channel design. Architecture 2 was highlighted as the most balanced option, excelling in performance, robustness, reliability, and cost. Architecture 3, despite its high performance and robustness, was less favorable due to significant increases in cost and weight. Sensitivity analysis was also carried out to assess the robustness and stability of alternative rankings with respect to criteria weights. It was found that if the weight of Cr.3 increases by 20%, Architecture 3 would statistically tie Architecture 2 as the best solution. However, this was posited to be a point beyond. In this research journey of exploring the integration of multi-disciplinary engineering models into system development, several pivotal insights were unearthed. This research successfully incorporated these models into a holistic MBE framework, enhancing the early stages of system development. This work identified key parameters promoting interoperability between Systems Engineering and Mechanical Engineering models, underscoring the potential for deepened interdisciplinary collaboration. Importantly, our findings underscore the invaluable role of Mechanical Engineering models in system-level decision making and the proven efficacy of multi-criteria decision-making MCDM methods in guiding architectural choices. Collectively, these results promise to revolutionize system development paradigms and pave the way for innovative strides in the domain. This study demonstrates a scalable approach for incorporating multiple engineering models, assessment criteria, and the knowledge and expertise from SMEs to form an MBE model. The methodology and results can be easily adapted to similar projects and extended to include more models, criteria, experts, and alternative CONOPs. In the future, machine learning approaches can transform the way multi-objective optimization is performed. Its ability to learn and make predictions based on data can enable it to perform more advanced decision making, considering multiple conflicting objectives, and providing optimal and accurate results. In future work, details of manufacturability during the early phases of product development can be considered to ensure that the final product is not only functional but also cost-effective and producible. This will allow for a more thorough evaluation of system architectures and ensure that the final decision is based on a comprehensive understanding of all relevant factors. **Author Contributions:** M.G.A., conceptualization, methodology, validation, writing--original draft preparation, investigation, and data curation. E.B.D., conceptualization, methodology, and writing--reviewing and editing. R.R., methodology and writing--reviewing and editing. A.E.T., conceptualization, methodology, supervision, funding acquisition, and writing--reviewing and editing. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Air Force Research Laboratory, Materials and Manufacturing Directorate (AFRL/RXMS) for support via Contract No. FA8650-20-C-5206. Additional support was provided by the University of Connecticut Pratt & Whitney Institute for Advanced Systems Engineering (UConn-PW-IASE).