# Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review
## 3 Research Method and Overview of Selected Articles
### Overview of the Data Collected from Selected Papers  
Before analyzing the papers classified under their objectives in Sections 4-7, we present an overview of the data collected on the current trends in the algorithms for optimizing the performance of mmWave optical and radio networks, and understand the key network state parameters used to evaluate the performance of the presented algorithms. For this reason, in this section we provide answers to the following three questions with the data collected from the eligible studies:  
* Question 1: Which algorithms are used more often in performance optimization in converged mmWave networks?
* Question 2: Which performance metrics are determined to show that the optimization method achieves the objective?
* Question 3: Which criteria are used to evaluate the solution method?  
Regarding the first question, Figure 3 shows the distribution of the optimization algorithms used by the selected papers. Heuristic and iterative algorithms are frequently used in the literature, due to the fact that many optimization problems in this field are non-deterministic polynomial time hard (NP-hard) and thus require decomposition and simplification to create sub-optimal solutions which can then be solved with heuristic algorithms. Game theory and matching theory-based solutions are also attractive for distributed decision-making among entities, as centralized optimization is challenging in multi-stakeholder environments [42; 43; 44; 45; 46; 47]. Finally, AI-based models (artificial neural networks (ANN) [48; 49], Q-learning [50]) are also being utilized to resource management optimization problems. The distribution of the main performance metrics according to the resource optimization objectives is given in Table 1 and the objectives are also mapped to their sections in this review. Finally, the evaluation criteria to test the performances of the selected papers are grouped in Table 2, which shows how many times each criterion is used together with how many of the resource management objectives use these criterion.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Objective** & **Performance Metric** & **Total Papers** \\ \hline \multirow{4}{*}{\begin{tabular}{c} **Throughput** \\ **Maximization** \\ **(Section 4)** \\ \end{tabular} } & Throughput/Sum rate (Bits/s) & 7 \\ \cline{2-3}  & Spectrum efficiency & 2 \\ \cline{2-3}  & (bits/s/Hz) & 2 \\ \cline{2-3}  & Quality of Service (QoS) & 2 \\ \cline{2-3}  & Fairness & 2 \\ \cline{2-3}  & Bandwidth Utilization & 1 \\ \hline \multirow{4}{*}{\begin{tabular}{c} **Delay** \\ **Minimization** \\ **(Section 5)** \\ \end{tabular} } & Delay & 3 \\ \cline{2-3}  & Average response time & 2 \\ \cline{2-3}  & End-to-end delay & 1 \\ \cline{2-3}  & Maximum delay & 1 \\ \cline{2-3}  & EE gain & 5 \\ \cline{2-3}  & \begin{tabular}{c} **Energy** \\ **Efficiency** \\ **(EE)** \\ **(Section 6)** \\ \end{tabular} & Power consumption & 3 \\ \cline{2-3}  & Achievable EE & 1 \\ \cline{2-3}  & Revenue & 1 \\ \hline \multirow{4}{*}{\begin{tabular}{c} **Virtual** \\ **Wirtualized** \\ **Resource** \\ **Allocation** \\ **(Section 7)** \\ \end{tabular} } & Virtual Network (VN) & 4 \\ \cline{2-3}  & \
\begin{tabular}{c} **Accordance ratio/embedding** \\ **success** \\ **Resource** \\ **Allocation** \\ **(Section 7)** \\ \end{tabular} & Resource utility/pooling gain & 3 \\ \cline{2-3}  & Revenue/Profit & 3 \\ \cline{2-3}  &
\begin{tabular}{c} Average sum Quality of \\ Experience (QoE) of operators \\ \end{tabular} & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of the main performance metrics depending on optimization objectives.  
Figure 3: Distribution of optimization algorithms for converged optical and mmWave radio resource management.