# Implanting Intelligence in 5G Mobile Networks--A Practical Approach
## 1 Introduction  
As the world is transiting to digital cities, the issues with today's networks become a never-ending effort and are even getting more intense as networks evolve to cater for the applications of low latency, high bandwidth, massive connectivity, support for high mobility, etc. The complex and extremely high dynamic nature of the envisioned network configuration asks for reduced human intervention. Programmable networks were one of the initial attempts to remove humans from the equation. However, implanting intelligence in the networks for complex use cases of different smart city verticals need more than just achieving programmable networks. The features that 5G and beyond pledges provide are the key ingredients. Machine Learning and artificial intelligence (AI) based approaches further add to introducing human context and decision-making in the networks. The telecom industry has been completely revolutionized by several developing technologies that enable new business models and provide customers with new experiences. The emergence of programmable systems such as Software Defined Networks (SDN) and Network Function Virtualization (NFV) have evolved and benefited the networks. Among the crucial services that 5G networks would encompass are autonomous driving, enterprise business models, virtual reality solutions, industrial automation, remote monitoring, smart health, smart cities, and many more. Network slicing (NS) is viewed as a major enabling technology for 5G by the Third Generation Partnership Project (3 GPP). It would enable operators to effectively run several network instances over a single infrastructure to serve various applications, use cases, and business services while providing the highest possible quality of service (QoS). NS, defined by Next Generation Mobile Network Alliance (NGMN) [1] is an important component of 5G networks enabling the coexistence of multiple isolated and independent virtual networks (slices) running on the same physical infrastructure. Each network slice is an independent self-contained virtualized end-to-end network with its virtual resources,topology, traffic flow, and provisioning rules which allows the network operators to run different deployments based on different architectures in parallel. Network slicing provides multifold advantages such as (i) provides multi-tenancy therefore, multiple virtual network operators can share the same physical infrastructure which reduces the capital expenses of deploying and operating new networks; (ii) can create customized slices with varying QoS (e.g., data speed, reliability, latency, delay, throughput) requirements for each service meeting the service level agreements of the services; (iii) supports on-demand cost-effective creation, modification and annuli of slices increasing the flexibility and adaptability of the network management [2]. However, some of the major challenges of NS are (i) dynamically adapting application service quality requirements and (ii) unpredictable resource and service quality demands. Furthermore, it is also worth highlighting that NS implementation deals with routing issues in different settings. There are known challenges to routing [3], and the research community have proposed various solution approaches to readers such as interfere-aware routing [4], measurement-based routing, channel-diversity-based routing, priority-based multi-path routing, and experience-driven model-free deep reinforcement learning-based approaches [5], etc. On similar lines, the shortest path and route problem in the network is one of the major challenges in the transportation and communication sectors. In this connection, [PERSON] et al. [6] proposed a genetic, particle swarm optimization algorithm to find the shortest path in a network with mixed fuzzy arc weights due to the complexity of the addition of various fuzzy numbers for larger problems. [PERSON] et al. [7] also proposed a fuzzy-based Ant Colony Optimization algorithm to solve the shortest path problems with different types of fuzzy weights.