# A Model-Based Engineering Approach for Evaluating Software-Defined Radio Architecture
## 5 Multi-Criteria Decision-Making Analysis  
This method involves making a multiple-attribute decision where we must weigh different performance metrics to determine the best architectural alternatives (derived in Section 4) that satisfies the requirements, CONOP, technical performance, and design constraints. There are many MCDM methods available for use. The Analytic Hierarchy Process (AHP) is an MCDM method to evaluate and prioritize options based on a set of criteria. It is particularly useful when there are multiple conflicting objectives or criteria that need to be considered in a decision. AHP is widely employed for making decisions \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Assessment Criteria** & **Arch.1** & **Arch.2** & **Arch.3** \\ \hline Cr.1 (USD) & 10,080 & 10,205 & 15,265 \\ Cr.2 (kg) & 1.88 & 2.12 & 2.79 \\ Cr.3 & Max of 1 NB output & Max of 3 NB outputs & Max of 2 WB outputs \\ Cr.4 (\({}^{\circ}\)C) & 129 \({}^{\circ}\)C & 127 \({}^{\circ}\)C & 113 \({}^{\circ}\)C \\ \hline \hline \end{tabular}  
* C.r.1: SDR-MM Cost (USD). Cr.2: SDR-MM Mass (kg). Cr.3: Signal Processing Performance—Number of Equivalent NB Outputs. Cr.4: SDR-MM Reliability—Average Electronic Surface Temperature (AES).  
\end{table} 
Table 2: Summary of the four-assessment criteria of the three SDR-MM architectures. Figure 16: Level 2 functional allocation of Architecture 3.  
and determining criteria weights from SME entered pair-wise comparisons of all criteria AHP also provides a quality metric from SME inputs, which can flag inconsistencies in the pair-wise comparisons [32; 33; 34]. Fuzzy AHP is another variant that uses a range of High/Typical/Low inputs as part of the pair-wise comparison process, allowing for inherent sensitivity checking in the process. Relative AHP is an effective method for ranking a limited number of alternatives in new and exploratory decision-making processes. It involves comparing each alternative pairwise and determining their relative importance. The relative comparison, even if consistent, must always be assessed for any inherent SME biases. For this reason, many MCDM applications use AHP for the weighting and other methods such as TOPSIS [35] and VIKOR [36] for scoring. These methods are based on various ways of determining the distance from an \"ideal solution\". For our application, we selected AHP-based weighting due to the multiple diverse criteria used (e.g., robustness). To address any \"SME bias\", three SMEs were used to independently perform the AHP criteria weight calculation. It was decided that instead of using a single set of derived mean SME weights, all three sets were carried forward to assess uncertainty. This could have also been achieved by using the Fuzzy AHP method. For scoring, the criteria utility functions are very non-linear, and it is less clear what the ideal solution is (e.g., the robustness of SDR outputs). For that reason, a relative AHP approach was used so that the SMEs could best assess the relative score of the alternatives for each category. As was performed for the weighting, three SMEs were used to prevent any single SME bias. Sensitivity analysis in AHP helps to evaluate the stability, reliability, and robustness of the decision-making process. It provides insights into the sensitivity of the decision to changes in the criteria or alternatives and helps decision makers make informed choices based on the impact of different scenarios on the overall outcome [37; 38]. The AHP process consists of several steps that guide the decision maker in evaluating options and making a final choice. The first step is to define the problem or decision that must be made and identify the objectives or criteria used to evaluate the options. These objectives and criteria should be organized into a hierarchical structure, starting with the most general objective at the top and breaking it down into more specific criteria at lower levels.