**5. Use cases: network planning, optimisation and management**

## **5.1 AI in network life cycle: from planning to control and management**

The life cycle of a communication network starts with its planning, dimensioning and deployment in a first life phase. Here, the network operator aims an investment optimization, minimal capital expenditure (CAPEX), while respecting the design

#### **Figure 9.**

*Three pillars for integrating AI in network life cycle, by ITU-T [20].*

requirements, especially the quality of service delivered or experimented by the endusers. In the second phase of the network life, a continuous control and management should guarantee a continuity of the service in a certain quality and a reliability of the services. In addition, network optimization must assist the management in order to keep the quality of service when necessary through upgrade the network hardware/ software components to cope with the changes in the operating environment. Such changes can be in form of increase of the subscribers' number along the years or the apparition of new services with high demand of capacity, etc. In order to integrate a certain level of artificial intelligence in the above cited workflow processes (planning, dimensioning, etc.), **Figure 9** elaborated by the ITU-T illustrates the different intelligence pillars needed in the network intelligence landscape 19]. Therefore, the intelligence in the workflow requires different big data sets, like the demand mapping (and/ or its forecasting for the coming years), a continuous collection of large data volume for the optimization tasks during the entire life cycle of the network. Moreover, the execution of such AI decisions and outputs requires intelligent sub-systems, which are able to interpret and to learn from the collected and analyzed big data streams.

The ideal case is to reach ZSM (Zero-touch network & service management), which is based on self-optimisation and self-healing network at different level of complexity either in the optimisation or in control and management. ITU-T gives three most desired cases, namely:


**61**

data center, etc.

*Artificial Intelligence and Machine Learning in 5G and beyond: A Survey and Perspectives*

System Human &

**Data Collection**

Human & System

System

System System Human

L5 Full Intelligence System System System System System

Human Human Human Human Human

Human & System

& System

System System System System Human &

**Analysis Decision Demand** 

Human Human Human

Human & System

Human Human

**Mapping**

Human

System

**Dimension of Intelligence**

**Action Implementation**

Human & System

*Five possible degrees of intelligence in next generation networks [20].*

However, it is clear that the migration toward a full intelligent network will not be an easy and one-dimensional task. Therefore, ITU-U has defined different degrees or levels of network intelligence according to five dimension, as listed in **Table 3** [20]. It is up to the network operator to define its own roadmap by prioritizing its objective concerning the investment, introduction of new services, etc. Nevertheless, in order to take a full benefit of the AI the operator should reach the fifth level "L5: Full Intelligence" in all the five dimensions. In addition, from telco's perspective the migration has to be in coherence with the business model as well as

One of the most critical processes to determine the final performance of a mobile network, and about its success in technical as well as financial aspects, is the initial planning process. Because the initial planning will determine also the way of functioning, operating, control and management processes, a baddimensioned network will always require more interventions from the control and management teams to try to bring the network performance to an acceptable level. In this process, decisions must be made about infrastructure (node deployment), spectrum, parameters and configuration setting procedures, energy consumption, network capacities to serve the worst-cases (peak or busy-hours traffic), evolution of the bandwidth demand over the years, etc. Furthermore, the planning and deployment of the next generation mobile network is not a greenfield task, i.e. starting from scratch. In fact, this planning task should take into consideration the already existing legacy systems and assets, such as point-of-presence, already existing base station, optical fiber for connecting the core network elements, the

In this very complex optimization problem, i.e. network planning, where several input parameters are uncertain random variables or distribution the AI can play a very important role in mastering the high complexity as well as delivering high efficient solutions. **Figure 10** shows how the AI can be integrated in the planning

process [1]. The AI integrating module contains three parts:

**5.2 AI/ML revolutionizing the planning and optimization process**

*DOI: http://dx.doi.org/10.5772/intechopen.98517*

**Network Intelligence** 

L0 Manual

L1 Assisted Operation

L2 Preliminary Intelligence

L3 Intermediate Intelligence

L4 Advanced Intelligence

**Table 3.**

Operation

**Level**

the return on investments.

*Artificial Intelligence and Machine Learning in 5G and beyond: A Survey and Perspectives DOI: http://dx.doi.org/10.5772/intechopen.98517*


#### **Table 3.**

*Five possible degrees of intelligence in next generation networks [20].*

However, it is clear that the migration toward a full intelligent network will not be an easy and one-dimensional task. Therefore, ITU-U has defined different degrees or levels of network intelligence according to five dimension, as listed in **Table 3** [20]. It is up to the network operator to define its own roadmap by prioritizing its objective concerning the investment, introduction of new services, etc. Nevertheless, in order to take a full benefit of the AI the operator should reach the fifth level "L5: Full Intelligence" in all the five dimensions. In addition, from telco's perspective the migration has to be in coherence with the business model as well as the return on investments.

#### **5.2 AI/ML revolutionizing the planning and optimization process**

One of the most critical processes to determine the final performance of a mobile network, and about its success in technical as well as financial aspects, is the initial planning process. Because the initial planning will determine also the way of functioning, operating, control and management processes, a baddimensioned network will always require more interventions from the control and management teams to try to bring the network performance to an acceptable level. In this process, decisions must be made about infrastructure (node deployment), spectrum, parameters and configuration setting procedures, energy consumption, network capacities to serve the worst-cases (peak or busy-hours traffic), evolution of the bandwidth demand over the years, etc. Furthermore, the planning and deployment of the next generation mobile network is not a greenfield task, i.e. starting from scratch. In fact, this planning task should take into consideration the already existing legacy systems and assets, such as point-of-presence, already existing base station, optical fiber for connecting the core network elements, the data center, etc.

In this very complex optimization problem, i.e. network planning, where several input parameters are uncertain random variables or distribution the AI can play a very important role in mastering the high complexity as well as delivering high efficient solutions. **Figure 10** shows how the AI can be integrated in the planning process [1]. The AI integrating module contains three parts:

**Figure 10.**

*Integrating AI/ML in the planning process of mobile networks (adapted from [1]).*


**63**

**Figure 11.**

plane, agent plane and business lane.

tasks of each layers are summarized in **Table 4**.

*Artificial Intelligence and Machine Learning in 5G and beyond: A Survey and Perspectives*

situations and scenarios in the operating environment. In other words, this part gives out options and/or planning for slicing, virtualization, edge computing and impact of each decision option and/or planning, decisions about network expan-

sion plan or resource utilization plan, suggestions on corrective actions.

*Intelligence plane for the integration of AI in SDN, NFV and network control in the platform "FINE" [9].*

**5.3 AI building efficient collaboration NFV/SDN and network management**

Already with 4G, the mobile network operators were facing an increasing network densification as response to the increasing demand for capacity and coverage, while with 4.5G operators were facing an exponential increasing number of end-devices, essentially in case of M2M and NB-IoT LTE. Therefore, research works have been dealing with the integration of AI in different levels of mobile architecture; independently of the access technology, either 4G or 5G. For example, authors in [9] proposed a functional architecture of the integration of AI to exploit and serve SDN, NFV and network control/monitoring. The authors proposed a framework of an intelligent communication network, called future intelligent network (FINE). The framework architecture is constituted of three planes: intelligence

In this section, we focus on the integration of the AI in SDN/NFV and network management, which is achieved through the intelligence plane that acts as the brain of the entire framework, **Figure 11**. Therefore, FINE is an intelligent network with an AI core. The intelligence plane can be composed of the basic layer, the core layer, the platform layer, the application/terminal layer and the solution layer. The basic

*DOI: http://dx.doi.org/10.5772/intechopen.98517*

*Artificial Intelligence and Machine Learning in 5G and beyond: A Survey and Perspectives DOI: http://dx.doi.org/10.5772/intechopen.98517*

**Figure 11.**

*Intelligence plane for the integration of AI in SDN, NFV and network control in the platform "FINE" [9].*

situations and scenarios in the operating environment. In other words, this part gives out options and/or planning for slicing, virtualization, edge computing and impact of each decision option and/or planning, decisions about network expansion plan or resource utilization plan, suggestions on corrective actions.

#### **5.3 AI building efficient collaboration NFV/SDN and network management**

Already with 4G, the mobile network operators were facing an increasing network densification as response to the increasing demand for capacity and coverage, while with 4.5G operators were facing an exponential increasing number of end-devices, essentially in case of M2M and NB-IoT LTE. Therefore, research works have been dealing with the integration of AI in different levels of mobile architecture; independently of the access technology, either 4G or 5G. For example, authors in [9] proposed a functional architecture of the integration of AI to exploit and serve SDN, NFV and network control/monitoring. The authors proposed a framework of an intelligent communication network, called future intelligent network (FINE). The framework architecture is constituted of three planes: intelligence plane, agent plane and business lane.

In this section, we focus on the integration of the AI in SDN/NFV and network management, which is achieved through the intelligence plane that acts as the brain of the entire framework, **Figure 11**. Therefore, FINE is an intelligent network with an AI core. The intelligence plane can be composed of the basic layer, the core layer, the platform layer, the application/terminal layer and the solution layer. The basic tasks of each layers are summarized in **Table 4**.


#### **Table 4.**

*Main tasks and layers in the intelligence plane of the platform "FINE" [9].*
