A novel multi-objective approach for link selection in aeronautical telecommunication networks

Abstract

The International Civil Aviation Organization has recently established new standards for the aeronautical telecommunication network using Internet protocol suite (ATN/IPS). As ATN services may operate on shared data communication links, optimizing the use of the shared resources has become fundamental to ensure that applications operate adequately. In this context, load balancing for ATN/IPS can be considered in an attempt to optimize the use of links based on their capacities. The literature is scarce of network management studies in which load balancing is taken into account. In this paper, we propose an integrated model for managing ATN/IPS links. In addition to objective parameters, we define subjective parameters of the model through the voluntary participation of air traffic controllers. The well-known multi-criteria decision making method Analytic Hierarchy Process was employed to define the subjective parameters of the integrated model, which met the requirements of communication services and applications of the aeronautical network. The network selection problem was then modeled in a multi-objective optimization problem after combining the criteria by using Simple Additive Weighting method. Computational experiments with real data from Brazilian ATN (ATN-Br) were performed. The $\varepsilon$-constraint method was applied to solve the multi-objective problem by varying the balance levels of the network. The results of the experiments show that the proposed integrated model maintains links with a usage rate closer to the trade-off point. In addition, the use of objective and subjective criteria allows the model to effectively prioritize critical communications in link degraded scenarios, which does not happen in the baseline model, in which priorities are pre-defined.

A Data details

This appendix presents more detailed information about the case study. First, a brief description regarding the 9 (nine) types of services is provided. Then, details on the objective and subjective criteria experiment are provided.

1.1 A.1 Types of services

The case study has the following types of services:

Freq. A—Primary radio frequency communication channel with amplitude modulation for voice air traffic coordination between pilots and air traffic controllers.

Freq. B—Secondary radio frequency communication channel with amplitude modulation, aimed at coordinating air traffic by voice between pilots and air traffic controllers.

TF-1—Direct telephone link, of high priority, which is intended exclusively for oral operational communications related to the coordination and control of air traffic, as well as military operational circulation at SISCEAB.

TF-2—Telephone network with switched link intended exclusively for oral operational communications, allowing calls related to the coordination and control of air traffic, as well as military operational circulation at SISCEAB.

System T—Total Air Traffic Information Control System that comprises status messages and modification of flight plans and authorization messages in air traffic co-ordinations.

System A—Airport Control and Monitoring System.

System TS—Integration messages between Control systems Total Air Traffic Information and the Advanced Air Traffic Information Management System and Operational Interest Reports.

Radar—Radio-determination system.

Meteorology—Weather information system.

1.2 A.2 Objective criteria

This section presents the threshold values defined to calculate the utility functions of the case study. Table 8 presents the threshold values according to the services.

Table 8 Threshold values employed to define the utility functions

The document that provides us the technical information about the services (Brasil 2017) does not define all the threshold values required by the introduced model. Therefore, we considered every threshold not reported in the table as the value 1.

In voice services, in relation to the bandwidth, there is a variation in the utility value due to the possibility of using two transmission modes (with codec G.711 or G.729), defined in International Telecommunication Union (ITU) technical standards, by the telecommunications standardization sector (ITU-T). The two modes have different bandwidth needs: G.711 (64kbps) and G.729 (8kbps); and distinct perceptions of quality by the user (utility), measured by the parameter MOS (mean opinion score) (CISCO 2019).

The mean opinion score (MOS) value was used to define the two utility values. The MOS scale ranges from “1” (worst sound perception) to “5” (best sound perception). The best (highest) value (4.1 with the codec G.711) was considered as a reference, corresponding to the utility “ 1 ” (100%). And the value of codec G.729 (3.92) in relation to this reference value represents a sensation of voice quality of approximately 95%. Thus, the utility was set to “ 1 ” for the codec G.711 and “ 0.95 ” for the codec G.729.

Also, in Brasil (2017), there is no information regarding the threshold values for the other services, in relation to the bandwidth. Therefore, for the experiments of the case study, the utility values of these cases are all “1”.

1.3 A.3 Evaluation of the subjective criteria

The first step of formulating the integrated model consists in assessing the subjective criteria on the classes and types of services. The result is a priority vector (w) that is defined through the hierarchical structure of the AHP with ratings.

1.3.1 A.3.1 Assessing the classes of services

Let ${{\varvec{H}}}^{(cs)}$, defined in Equation (19), be the matrix of comparison between classes of services, according to the fundamental scale of the AHP (Table 1). Rows 1 to 4 of the matrix correspond to the classes of services RT, MC, PD and BE, respectively.

$$\begin{aligned} \varvec{H}^{(\varvec{cs})}=\begin{bmatrix} 1&{}3&{}5&{}7\\ \dfrac{1}{3}&{}1&{}3&{}5\\ \dfrac{1}{5}&{}\dfrac{1}{3}&{}1&{}3\\ \dfrac{1}{7}&{}\dfrac{1}{5}&{}\dfrac{1}{3}&{}1\\ \end{bmatrix} \end{aligned}$$

(19)

Then, after normalizing this matrix (by dividing each element by the sum of the elements of the column), we obtain the weight vector of the classes of services $\mathbf{w}^{(cs)}$ by solving the system $H^{(cs)}{'}{} \mathbf{w}^{(cs)}=\lambda _{max} \mathbf{w}^{(cs)}$, where $H^{(cs)}{'}$ is $H^{(cs)}$ normalized. The normalized rating $\mathbf{r}$ of each service is calculated by dividing the weights by the highest priority value, 0.558. Table 9 shows the values obtained in this case study.

Table 9 Sorted $\mathbf{w}^{(cs)}$ to define the priority of the classes of services

1.3.2 A.3.2 Assessing the types of service

In the flight controllers’ assessment of types of service, the services were ranked in order of priority. The number assigned as a priority (1 to 12) for each service was chosen, obtaining a respective total grade. The difference between the highest and lowest total grade (the largest difference) was used to construct the comparison matrix, defining value ranges (eight ranges) to transform the degree differences into levels of relative importance on the fundamental Saaty scale (from 2 to 9). The value 1 was assigned when the difference was null (equal to zero). This value on the fundamental Saaty scale was assigned according to the analysis of relative importance - if two services have a difference of 32, for example, the relationship between them is 1 to 4 (32 is in the $4^{th}$ interval) ; if the difference is 4, the ratio will be 1 to 2 (4 is in the $2^{nd}$ interval); and so on, comparing services two by two.

It is possible to obtain a consistent comparison matrix ${{\varvec{H}}}^{(s)}$ that reflects the priorities among services, in accordance with the air traffic rules contained in ICA 100- 12 (Brasil 2016) and the practical experience of these consulted controllers. The matrix in (20) shows the values of the comparisons among the nine priority services, where each line corresponds to the services Freq. B, TF-1, Meteorology, Freq. A, Systems T and TS, TF-2, Radar, and System A.

$$\begin{aligned} \varvec{H}^{(s)}=\begin{bmatrix} 1&{}\dfrac{1}{2}&{}6&{}\dfrac{1}{5}&{}3&{}6&{}5&{}4&{}4\\ 2&{}1&{}6&{}\dfrac{1}{4}&{}3&{}7&{}6&{}4&{}4\\ \dfrac{1}{6}&{}\dfrac{1}{6}&{}1&{}\dfrac{1}{9}&{}\dfrac{1}{4}&{}2&{}\dfrac{1}{2}&{}\dfrac{1}{3}&{}\dfrac{1}{3}\\ 5&{}4&{}9&{}1&{}6&{}9&{}9&{}7&{}7\\ \dfrac{1}{3}&{}\dfrac{1}{3}&{}4&{}\dfrac{1}{6}&{}1&{}5&{}4&{}3&{}3\\ \dfrac{1}{6}&{}\dfrac{1}{7}&{}\dfrac{1}{2}&{}\dfrac{1}{9}&{}\dfrac{1}{5}&{}1&{}\dfrac{1}{2}&{}\dfrac{1}{4}&{}\dfrac{1}{4}\\ \dfrac{1}{5}&{}\dfrac{1}{6}&{}2&{}\dfrac{1}{9}&{}\dfrac{1}{4}&{}2&{}1&{}\dfrac{1}{3}&{}\dfrac{1}{3}\\ \dfrac{1}{4}&{}\dfrac{1}{4}&{}3&{}\dfrac{1}{7}&{}\dfrac{1}{3}&{}4&{}3&{}1&{}2\\ \dfrac{1}{4}&{}\dfrac{1}{4}&{}3&{}\dfrac{1}{7}&{}\dfrac{1}{3}&{}4&{}3&{}\dfrac{1}{2}&{}1\\ \end{bmatrix} \end{aligned}$$

(20)

Following the steps of the AHP method, matrix ${{\varvec{H}}}^{(s)}$ is normalized (again, by dividing the elements by the sum of the elements of each column). Then, the weight vector regarding the services $\mathbf{w}^{(s)}$ is obtained—the eigenvector associated to the largest eigenvalue of normalized ${{\varvec{H}}}^{(s)}$.

$$\begin{aligned} \mathbf{w}^{(s)}=\begin{bmatrix} 0.1486 \\ 0.1773 \\ 0.0294 \\ 0.3795 \\ 0.0976 \\ 0.0201 \\ 0.0315 \\ 0.0635 \\ 0.0638 \\ \end{bmatrix} \end{aligned}$$

(21)

Table 10 presents the correspondence between the classes and their respective ordered ratings, obtained by ordering the elements of $t ^ {(s)}$. The normalized rating $\mathbf{r}$ of each service is also shown in the last column of this table. The normalized rating is obtained by dividing the rating of the type of service by the value of the type of service with the highest priority, 0.3795.

Table 10 Ratings for the types of service

1.3.3 A.3.3 Definition of vector $\mathbf{w}$

After comparisons are made to obtain the weights regarding the types and classes of services, a comparison is made between the criteria in relation to the objective (obtaining the subjective “ weights ”) defined in the hierarchical structure of the AHP, to obtain global priorities (p). As the documentation of the Air Force Command does not clearly specify what the priority should be between the criteria (classes and types of services), the same importance is adopted for both. Following the steps of the AHP method, the comparison matrix (Eq. 22) on the fundamental scale is very simple, since the two criteria have the same importance. In the matrix, each line represents one of the criteria (classes and types of service, respectively).

$$\begin{aligned} \varvec{H}^{(gp)}=\begin{bmatrix} 1&{}1\\ 1&{}1\\ \end{bmatrix} \end{aligned}$$

(22)

The weight vector $\mathbf{p}$, which is the leading eigenvector of the matrix $\mathbf{H}^{(gp)}$ after normalization (again, the normalization is calculated by dividing each element by the sum of the values of its columns), defines the global priorities of the classes and types of services. Table 11 shows these values.

Table 11 Global priorities of type and class of services

Therefore, the hierarchical structure of the AHP with ratings is complete, rendering it possible to calculate the weight of each service $\tau$ by Eq. (3) as presented in Table 4.