# A Model-Based Engineering Approach for Evaluating Software-Defined Radio Architecture
## 1 Introduction
Trade study analysis involves transforming qualitative data into quantitative data based on a pre-identified set of criteria to select the preferred solution(s) out of many alternatives [17]. With the right trade study setup, the system design can be performed at hierarchical levels of abstraction. Evaluating alternatives and conducting trade-off analyses are crucial activities in systems engineering to lower costs during early design phases [18]. In a subsequent study, [PERSON] et al. [19] surveyed the literature to evaluate the use of MBSE approaches and tools for trade study analysis. They found very few MBSE papers dealing with trade space exploration and the evaluation of design alternatives. The approach to conducting an architecture trade study, as described by [PERSON] [20], involves the following steps: (1) identifying key system functions, (2) identifying candidate solutions, (3) defining assessment criteria, (4) assigning weights to the criteria, (5) defining a utility curve for each criterion, (6) assigning measures of effectiveness (MOEs) to the candidate solutions, (7) conducting a sensitivity analysis, and (8) determining the final solution. This method builds upon the importance of alternative evaluation and trade-off analysis, which are critical systems engineering activities during the architecture phases, where the cost of redesigning the system is much lower compared to later stages in the system lifecycle. The design of complex systems can be challenging and requires the use of effective engineering tools to evaluate and optimize different alternatives. MBSE has emerged as a powerful approach to system design, providing a comprehensive and systematic way to analyze and optimize complex systems. In one example, [PERSON] et al. [21] demonstrated the effectiveness of MBSE in designing satellite communication systems, showing how tradespace analysis can lead to better communication and collaboration among stakeholders. In another example, [PERSON] and [PERSON] [22] implemented MBSE in developing a tool to assess implantable wireless biotelemetry communication systems, highlighting the potential of this approach to identify potential risks and trade-offs and enhance the design process. Similarly, [PERSON] et al. [23] proposed using MBSE and trade-off analysis to optimize the design of implantable biomedical microdevices, demonstrating the potential of system engineering approaches in improving the design process and resulting system performance. These studies highlight the importance of MBSE in complex system design and the potential of this approach to enhance communication, collaboration, and cost savings in the design process. While other studies have demonstrated the effectiveness of integrating systems and domain models or systems models with multi-criteria decision-making (MCDM) for complex system design, this paper demonstrates a true MBE approach by integrating systems models, domain models, and MCDM. The goal of this paper is to show how the use of transdisciplinary MBE models and MCDM decision analysis techniques can be used to optimally select between architectural alternatives. The research questions (RQs) to realize this goal are: RQ#1: What model parameters are required to make systems and mechanical models interoperable, RQ#2: Can system models be informed by linked mechanical engineering models, and RQ#3: Can MCDM be used to select between systems engineering architectural alternatives. Our proposed methodology to address the research questions is depicted in Figure 1. Each method in Figure 1 will be elaborated upon in the following sections, including the rationale for its selection in comparison to other options, its application to the SDR application, and the findings. The proposed integrated methodology, shown in Figure 1, consists of four primary methods: (1) descriptive models (described in Section 2). The descriptive model, created using SysML, includes requirements, structure, behavior, and parametric.